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A Journal of Policy Development and Research Crime aNd urbaN Form Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development | Office of Policy Development and Research

Managing Editor: Mark D. Shroder Associate Editor: Michelle P. Matuga Advisory Board Peter Dreier Occidental College Richard K. Green University of Southern California Keith R. Ihlanfeldt The Florida State University Annette null Kinull nullssachusetts Institute of Technology Carlos null nullrtnull Anull Associates Inc. Douglas S. nullssey Princeton University Sandra null nullnullan nullhns nullnullins University nullrynullth Shinn nullandernulllt University Raynullnd null Struynull nulltional Onullnion Research Center Paul nulladdell University of Californianullnullrnullley nullhn C. nulleicher nulldson InstitutenullInc.

A Journal of Policy Development and Research U.S. Department of Housing and Urban Development Office of Policy Development and Research Cityscape Crime and Urban Form VolUme 13, nUmber 3 • 2011

The goal of Cityscape is to bring high-quality original research on housing and community development issues to scholars, government officials, and practitioners. Cityscape is open to all relevant disciplines, including architecture, consumer research, demography, economics, engineering, ethnography, finance, geography, law, planning, political science, public policy, regional science, sociology, statistics, and urban studies. Cityscape is published three times a year by the Office of Policy Development and nullsearch nullDnullnullof the nullnull Department of nullusing and nullban Development. nullbscriptions are available at no charge and single copies at a nominal fee. The nullurnal is also available on line at httpnullnullww. huduser.orgnulleriodicalsnullityscape.html . PDnull welcomes submissions to the nullfereed Papers section of the nullurnal. Our referee process is double blind and timely, and our referees are highly qualified. The managing editor will also respond to authors who submit outlines of proposed papers regarding the suitability of those proposals for inclusion in Cityscape. nullnd manuscripts or outlines to Cityscapenullud.gov . Opinions expressed in the articles are those of the authors and do not necessarily reflect the views and policies of HUD or the U.S. government. nullisit PDnullnulls websites, www.hud.govnullolicy or www.huduser.org, to find this report and others sponsored by nullDnulls Office of Policy Development and nullsearch nullDnullnull Other services of nullD nullnull, PDnullnulls nullsearch and nullformation nullrvice, include listservs, special interest and bimonthly publications nullest practices, significant studies from other sourcesnull access to public use databases, and a hotline nullnullnullnullnullnullnullnull for help with accessing the information you need.

iiiCityscape Contents Symposium Crime and Urban Form Guest Editor: Ronald E. Wilson Guest Editor’s Introduction ........................................................................................................ null Sex Offenders, Residence Restrictions, Housing, and Urban Morpnulllognull nullRenullenulland Snulltnullsis .............................................................................................................. null by Tony null nullubesic, nullan T. nullrray, and nullinullbeth null nullcnull nulle nullaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull .................... null by Philip null. nullrris, nullremy nullnnis, nullran Obradovic, nullan null nullenman, and nullidi null nullunwald Mapping tnull Spatial Innullence of nullinull nullrrelates: nullnullnullarison of Operationalinulltion Scnullnulls and Inulllications for nullinull nullalnullis and nullinullnal nullstice nullactice ....................... null by nullel null Caplan HOnull null Resident nullsplacenullnt: Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls .......................................................................................................... null by nulldericnullnull. nullnes and Derenullnull Paulsen Using tnull Weignulled nullsplacenullnt nullotient nullo Explore nullinull nullsplacenullnt nullonull nullblic Housing Redenulllopnullnt Sites ..................................................................................... nullnull by nullagan Cahill null nulloucnullrs Help nullnullInconull Housenulllds nullnull in Safer nullignullornullodsnullEnulldence on tnull Housing nulloice nulloucnullr nullogranull .................................................................................... nullnull by nullchael C. nullns, nullgrid nulluld nulllen, and nulltherine Onullegan Modeling nullinullnal nullstance nullcanull ........................................................................................ nullnull by nullnull Onulleary nullinull and nullace: RapidlnullEnulllnullng ResearcnullMetnullds in tnull nullst nullnturnull ........................ nullnull by Patricia nullantingham nullnullnces in tnull Identinullation of Space null a Structuring nullctor of Social Realitnull .............. nullnull by nullandro nullmos nullSoutnullnullrican nullnullentarnullon tnull nullticles in tnull Cityscape Snullposiunullon nullinull and Urban nullrnull ...................................................................................................................... nullnull by Peter nullnull nullhmitnull Tinus nulluger, and nulltony null Cooper Departments nullaphic Detail nullisualinullng Racial Segregation nullfferentlnull Exploring Geograpnullc nulltterns in nullntext ..... nullnull by nullnald null nullilson

iv Crime and Urban Form Contents Data Shop nullnullginner’s Guide nullo nulleating Snullllnullrea nullossnullabulations ............................................ nullnull by nullydar nullrban, nullyan nullllagher, nulllrinullnullytenulln nullrban, and nullseph Persnull nullpact nulle Inullact of Honull EnergnullRetronull nullan Insurance: nullnulllot nullogranull ............................... nullnull by nullastair nullnullrlane Correction ................................................................................................................................. nullnull

1Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Guest Editor’s Introduction Ronald E. Wilson nullnull Department of nullusing and nullban Development Introduction The increasing role of geographical analysis in solving social problems is not nullust a practical response by government agencies. nulllustrated in this issue of Cityscape is the incorporation of geographic methods and techniques toward understanding issues of crime and disorder, two manullr negative innullences on the quality of life in urban nullerica. The primary emphasis in the use of this new ap - proach is on the delivery of programs and services to people through place as a means to increase the impact of federal, state, and local investments on the quality of life in urban neighborhoods. Urban Geography, Place, and Crime Crime changes with urban development patterns. Opportunities for criminal activity emerge, dis- appear, or move as geography changes across the urban landscape. Patterns emerge, dissipate, or persist based on changes in economic, ecological, and demographic conditions. nullture crimes are far more predictable by place of occurrence than by a particular offender nullherman and nullisburd, nullnullnullnullisburd et al., nullnullnull The greater predictability arises because places are relatively rigidnull land use, infrastructure, and even demography change slowly. Places are resistant to change in the absence of manullr investments of time and money to manull change occur. Public policy, especially local public policy, usually drives such investments. The environmental criminology subdiscipline began with the nullnull paper, nullesidential nullrglary and nullban nullrm,nullnullrantingham and nullantingham, nullnull, nullnullnull null one of the first empirical studies to establish the interaction between criminals and geography, the nullantinghams enullmined the geographic patterns of criminal offending within and between places. nullvironmental criminol - ogy fused geographical principles with criminological theory, providing opportunities to test empirically interactions between crime and place. The resulting research applied and tested new theories of crime rooted in urban development, including crime pattern theory nullrantingham and nullantingham, nullnullnull routine activities nullohen and nulllson, nullnullnull nullurney to crime nullhillips, nullnullnullnullngert, nullnullnull and geographic profiling nullossmo, nullnullnull Other longstanding theories, such as social disorganinulltion nullhaw and nullnully, nullnullnull rational choice nullornish and Clarnull, nullnullnull territoriality nullaylor, nullnullnull and deviant places nulltarnull nullnullnullwere modified from this research to include a geographic aspect. nullographic theories strengthen criminological theories by providing a place-based foundation for where offenders live, why incidences occur where they do, and how

2 Wilson Crime and Urban Form offenders move within a particular geography. nullographic theories also offer insight into the devel - opment and change of urban form nullpatial organinulltionnulland into the interactions between places that form from those changes nullpatial interactionnull The development of nullographic nullformation nullstem nullnullnullsoftware and spatial statistics has significantly affected the ability of law enforcement to combat crime and criminal nullstice to deliver services nullilson, nullnullnull nullcause crimes have a spatial structure and form cohesive patterns, place-based approaches can manull public policy more efficient. nulltions from public policy simul - taneously affect multiple people within the same target area. Crime types have a spatial structure based on urban geography. nullsidential burglaries happen only in residential areas. Commercial robberies, linullwise, transpire only in commercial districts. nullto thefts more frequently happen in places with large amounts of parnullng that are difficult to monitor. nullmicide tends to occur across larger areas where poverty, inequality, physical deterioration, and economic decline have long been established. nullcause places are interconnected, the benefits from targeted places can diffuse outward to adnullcent places and upward to larger geographic areas to strengthen the whole. This diffusion compounds the positive effects of such programs while adding little additional cost. Places are not isolated or detached from the surrounding geography. nullcial, economic, or political interactions occur within and between places in which places that are closer together are more related and share similar conditions. This phenomenon is nullown as the first law of geography nulliller, nullnullnullTobler, nullnullnull Distance decay, a core idea by which spatial interactions and relation - ships between places are analynulld, measures the first law of geography. nullat occurs in one place diffuses to adnullcent places, and from adnullcent places, upward to larger geographies. The outward spillover of crime into adnullcent neighborhoods leads to several places becoming unattractivenullthis, in turn, can lead to an entire set of neighborhoods becoming afnullcted with a host of new social ills nullarr and Pease, nullnullnullnulllster, nullnullnullnullnullm and nullngert, nullnullnullnullsseling, nullnullnullnullpp, Tita, and nulleenbaum, nullnullnull These places sometimes become failed neighborhoods. nullfailed neighborhood upwardly supplies an area with lower educational attainment, higher unemployment, unhealthy conditions, dilapidated buildings, and a wrecnulld infrastructure. Disproportionate amounts of human services, public safety, maintenance pronullcts, and linull resources are spent on these places, with little positive contribution in return from these places. null Tanullrevenue shrinnull, assets are sold, capital improvement is suspended, businesses move out, nullbs are shed, and residents who can leave do so. The legacy of place nullberts, nullicnullenull and nulleinhennull nullnullnullmay undermine the po - tential to solve social, economic, or political problems because these problems become entrenched. Articles in This Symposium nullban crime policy crosscuts a broad area of governmental actions toward solving problems. Change can be instilled through creating programs, enhancing laws, establishing regulations, null nullsearchers at the nullatial nullformation Design nullb at Columbia nulliversity and the nullstice nullpping Center appropriately described these places as nullllion Dollar nullocnull nullDnullnull The primary aspect of the nullnullpronullct was to research the costs of incarceration whereby offenders were continuously cycling in and out of prison bacnullto and from the same neighborhoods that offered little opportunity for them to succeed. nullre information is available at httpnullnullww.spatialinformationdesignlab. orgnullronullcts.phpnulldnullnull .

3Cityscape Guest Editor’s Introduction improving the environment, or redirecting resources that alter place or behavior in place. The symposium in this issue of Cityscape features a range of articles that demonstrate the interplay of crime patterns and urban geography. The first two articles illustrate the effect of place-based polices on particular segments of the population in which geographical analysis played a nully role in uncovering the effects. null nullenull Offenders, nullsidence nullstrictions, nullusing, and nullban nullrphologynullnullnullview and nullnthesis,null Tony null nullubesic, nullan T. nullrray, and nullinullbeth null nullcnulldemonstrate the human and geographic consequences that arise for offenders, residents, and the government in finding suitable places for offenders to live. The authors summarinull recent research findings on housing availability problems senulloffenders face after residency restriction laws are implemented. nullghlighted are problems that arise when place is not tanulln into account when manullng policy. nullhe Coaction of nullighborhood and nulldividual nullfects on nullvenile nullcidivismnullis an enullellent enullmple of the interaction between individuals, neighborhoods, and rehabilitation programs. Philip null nullrris, nullremy nullnnis, nullran Obradovic, nullan null nullenman, and nullidi null nullunwald uncover effects of historical characteristics of place on the variation of nullvenile reoffending and the effect on the types of crimes committed. The authors manull an argument for crafting place-based rehabilitation programs designed to help nullveniles refrain from reoffending based on the type of neighborhood in which they live. The third article illustrates the spatial interaction between places and their characteristics that enullmplify the first law of geography. null nullapping the nullatial nullnullence of Crime Correlatesnullnull Comparison of Operationalinulltion nullhemes and nullplications for Crime nullalysis and Criminal nulls - tice Practice,nullnullel null Caplan enullmines the geographic correlates of crime that spatially innullence nearby crimes. This article demonstrates two particular aspects of using place to understand crime. The first is the geographic dynamic of spillover and diffusion effects between adnullcent places with shared characteristics. The second is the demonstration of local characteristics of a place that af- fects an offendernull probability of committing crimes. The fourth and fifth articles demonstrate the use of spatial analysis methods to evaluate the ef- fectiveness of place-based programs. nulldericnullnull nullnes and Derenullnull Paulsennull nullOPnullnull nullsident Displacementnullnulling nullPnullnull Program nullals To nullaluate nullighborhood Outcomesnulland nullagan Cahillnull nullsing the nullighted Displacement nullotient To nullplore Crime Displacement nullom Public nullusing nulldevelopment nulltesnullloonullat the effects of nullDnull nullPnullnull Program on residents and the places they live and to which they move. nullth articles enullmine the displacement effect from programs that alter the built environment in three different cities. nullnes and Paulsen enullmined changes in neighborhoods in a small city and found that residents from nullPnullnull sites moved to neighborhoods that were of higher poverty and had more crime. Conversely, Cahill enullmined displacement of crime in two medium-sinulld cities and found a diffusion of benefit from nullPnull null, combined with local redevelopment, in the sites and surrounding areas. Crime was reduced and living conditions for residents improved. The minulld results suggest a better understanding is required of how people affect place when programs temporarily move participants. nullmilarly, the sinullh article also demonstrates the effect on place from people moving but, more spe - cifically, through a program that allows participants to choose their neighborhood. null nullo nulluch - ers nulllp nullw-nullcome nulluseholds nullve in nullfer nullighborhoodsnullnullidence on the nullusing Choice

4 Wilson Crime and Urban Form nullucher Program,nullnullchael C. nullns, nullgrid nulluld nulllen, and nulltherine Onullegan enullmine whether nullusing Choice nullucher nullCnull participants move to safer neighborhoods. null overall goal of the nullnullProgram is to give low-income families the option of moving to neighborhoods with better employment opportunities, improved education for their children, and, most importantly, a safer environment free of crime. This article demonstrates the partial progress of participants finding safe places to live and discusses the characteristics associated with those places. null the final article, nullodeling Criminal Distance Decay,nullnullnull Onulleary shows how urban geogra - phy can be mathematically modeled to understand how urban structure affects criminal decision behavior. The results show how offenders operate and adnullst to the geographic structure as they move through a metropolitan area. nulls wornullis intended to highlight how research can more realistically simulate target selection based on how offenders enullloit their choice of targets within a geographic contenull. The international commentaries from Canada, Colombia, and nulluth nullrica bring this symposium full circle regarding crime and the structure of urban geographies. nullen though various historical circumstances, policies, and cultural aspects have shaped the urban patterns in these countries, place is still a consistent framewornullin which to measure and understand the variation of crime. These commentaries enullmplify the urban development differences between the nullited nullates and other countries, yet they show how places with low qualities of life can systemically lead to many social problems, of which crime is a manullr catalyst. The articles in this symposium are only a small sample of the many wornull highlighting the interac - tion of crime and the urban environment. nulladers interested in pursuing other recent literature should consult the following special issues from other nullurnalsnull null nullrime in the City.nullnullnull. Built Environment null nullnull null nullrime nullpping.nullnullnull. Social Science Computing Review null nullnull null nulleographic nullpects of nullnullOffender nullsidency nullstrictions.nullnullnull. Criminal nullstice nulllicy Review null nullnull null nullayesian nullurney to Crime nulldeling.nullnullnull. nullurnal onullnullvestigative nullycnulllogy annullnullnullnnullr nullonulling nullnullnull null nullmpirical nullidence on the nulllevance of Place in Criminology.nullnullnull. nullurnal onullnullantitative Criminology null nullnull Enhanced Policymaking and Practice nullvernmental actions can alter the fate and tranullctory of places, because public policy solutions to crime are often about wnullrenull This dynamic between people and place can no longer be ignored. null must be appreciated that when policy shapes place, the changes in those places in turn affect peo- ple, and the resulting reactions by people ultimately come bacnullto help reshape policy. Preventing or intervening in crime by targeting place allows neighborhoods to nullurish through building or sustaining a higher quality of life. null nullnulland other spatial analysis tools become more prominent

5Cityscape Guest Editor’s Introduction and more research supports their effectiveness, governments will linullly craft more comprehensive policies that give full consideration to how geography can help meet the goals of policy actions. The utility of more complenullgeographic analysis for policymanullrs confronting crime has yet to be established, and our goal in this symposium is to show what that utility might be. The Department of nullusing and nullban Development, for its part, is manullng significant investments in all aspects of geographical analysis to support the many different community needs at the intersection with the Departmentnull mission and programs. Crime prevention is a persistent need of many communities, and it is hoped these articles will give researchers, practitioners, and policymanullrs new ideas to pursue so they can better meet that need, with geography as a foundation.

6 Crime and Urban Form

7Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Morphology: A Review and Synthesis nullonnullH. Grubesic Drenulll nulliversity nullan null. Murranull Elinullbetnullnull Macnull nullinullna nullate nulliversity nullstract nulltnullugnullresinullnce restrictions nullr convictenullsenullonullnullnnullrs are winullly ennullrcenullin tnull nullitenullStates, tnullse policies remain controversialnullnullst restrictions are nullnullenull geograpnullcally, pronullbiting convictenullonullnullnnullrs nullom establisnullng a permanent resinullnce witnulln a prescribenullnullstance nullom sensitive nullcilities linull scnullols, parnull, annullbus stopsnull nulloponents argue tnullt resinullnce restrictions protect nullmilies annullcnulllnullen nullom senullal violence, but otnullrs argue tnullt tnullse policies can pronullce a variety onullunintennullnullsocial, economic, annullgeograpnullc outcomes, sucnullas renullcing available nullusing, nullrcing onullnullnnull null ers to cluster in socially nullsennullancnullsenullneignullornullonull, limiting access to renullbilitation nullcilities, annullgenerating spillover enullnullcts to nearby communitiesnullnullis article provinulls an overview onullsenullonullnullnnullr laws in tnull nullitenullStates annullsyntnullsinulls tnull literature pertaining to senullonullnullnnullr policies annulltnullir geograpnullc implications nullr nullusing avail null ability annullanullnullrnullbilitynullnullis article also anullresses tnull enullnullcts onullurban morpnulllogy on senullonullnullnnullr policies annulloutcomes, annullit ennull witnullan agennull nullr nullture researcnull

8 Crime and Urban Form Grubesic, Murray, and Mack Introduction The nullnited nulltates is home to a broad spectrum of senull offender policies. nullrom community notification laws to residence restrictions, federal, state, and local legislation conceived for managing convicted offenders is both dynamic and controversial nullung, nullnullnull null One of the more contentious legislative approaches for controlling offender whereabouts at the local and state levels, is the establishment of residence restriction nullones around sensitive facilities, such as schools, daycare centers, parnulls, bus stops, and other locations where children congregate. These spatial restriction nullnes nullnullsnullare defined by a distance criterion, typically ranging from nullnullto nullnullnullfeet from a sensitive facility, within which a convicted offender may not establish a permanent residence. The goal of these restrictions is to miniminull the potential for interaction between offenders and children, thereby reducing the risnull of recidivism and enhancing public safety nullnullrubesic and nullurray, nullnullnullnullnull. nulllthough this type of legislation is popular, little empirical evidence enullists on the effectiveness, or lacnull thereof, of the legislation in achieving its intended goals and obnullctives nullewnullbury and nullvenson, nullnullnull nullvertheless, the general public strongly supports these laws, further motivating continued imple - mentation and enforcement of policies for managing convicted offenders nullancini et al., nullnullnull The effects of residence restrictions on both offenders and communities are the subnullct of intense scrutiny. Tewnullbury nullnullnull suggested social or cultural implications, or nullollateral consequences,null are associated with such restrictions. nullr enullmple, residence restrictions are thought to create dif - ficulties in reintegrating offenders into communities, causing social isolation and limiting offendersnull access to rehabilitation services nullurchfield and nullngus, nullnullnullnullvenson, nullnullnullnullvenson and Cotter, nullnullnullnullvitnulland nullrnulls, nullnullnull nullmilarly, significant effort recently has been dedicated to enullloring the effects of residence restrictions on the availability and affordability of housing in communities. nullch of this literature has suggested that larger nullnull decrease housing availability for senulloffenders nullarnes et al., nullnullnull Chanullwsnull and nullrcado, nullnullnullnullndbergen and nullrt, nullnullnullnulloba, nullvenson, and nullnulle, nullnullnull nullthough these observations are not particularly surprising, they provide the impetus for pursuing more nuanced and relevant matters associated with housing that may be the result of residency restriction laws. nullr enullmple, residence restrictions not only limit housing availability, but recent wornullidentifies potential effects of these policies on local housing values nullinden and nullcnullff, nullnullnullPope, nullnullnull affordability nullrubesic, nulltisnullw, and nullrray, nullnullnullnullubesic, nullrray, and nullcnull nullnullnull and the emergence of senulloffender clusters nullrubesic, nullnullnull The purpose of this article is to provide an overview of how senulloffender residency laws interact with local geography and urban morphology to generate both intended and unintended outcomes. null begin with a general review of senulloffender laws at federal, state, and local levels. This review is followed by a brief historic overview of how local legislation is used to control the spatial distribu- tion of undesirable facilities and populations in the nullited nullates. The nenull three sections enulllore the specific geographic manifestations of senulloffender policies and also highlight how poor cross- nullrisdictional coordination of offender laws can innullence housing options and affordability and create geographic concentrations of offenders. null also review strategic options for communities null nullr a thorough review of community notification laws, see nulldarf nullnullnull, nullghes and nulldlecnullnullnullnull, and nullgan nullnullnull.

9Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis dealing with unique local distributions of sensitive facilities, populations, and convicted offenders. null conclude by discussing the policy implications for senulloffender laws and housing, highlighting the importance of urban morphology. Sex Offender Laws nullumerous senull offender laws enullist in the nullnited nulltates. nulllthough many of these laws do not have an obvious linnullto housing issues for convicted offenders, a variety of enullernalities are associated with these policies that can affect housing availability and affordability. null an effort to better unders tand how such legislation is structured across political nullrisdictions and the housing effects associated with these laws, this section reviews senulloffender legislation at federal, state, and local levels. Federal Laws Perhaps the most widely recogninulled approach to managing senull offenders is community notification laws. These laws are instituted to monitor the whereabouts of convicted offenders once they have served their prison sentence and are released into the community. null the federal level, these laws were initially enacted in nullnullnullnull as the nullacob nulletterling Crimes nullgainst Children and nullenullually nulliolent Offender nullgistration nullt null nulletterling nulltnull The nulltterling nullt mandated that each state imple - ment a senulloffender registry. nullgannull nullw null amended this act in nullnull, requiring states to establish a community notification system to alert residents when a senulloffender moves into their community. Communities are given significant leeway in the approach used for notificationnullmethods include phone calls, going door-to-door, or using some type of publicly accessible nullternet database. The nullnchner nullt null nullnullnull also amends the nulltterling nullt, requiring lifetime registration for recidivists and offenders that commit aggravated senullal offenses. The nullnchner nullt, however, is largely sub - sumed by the more recent nullam nulllsh Child Protection and nullfety nullt of nullnull null nullalsh nulltnull The nulllsh nullt organinulls senulloffenders into three tiers and mandates that the most serious offenders nullier null update their whereabouts every nullmonths for their lifetimes. Tier nulloffenders are required to update their information every nullmonths for null years and Tier nulloffenders must update their information annually for null years. nullthough community notification laws were initially developed to protect children from predatory senull offenders nullnullevenson, nullnullnullnullnull, opponents of these laws argue that most senullual assaults committed against children are by acquaintances nullown to the victim nullatalano, nullnullnull They claim that this method of managing offenders is largely ineffective, and that it is a drain on human and financial resources for local law enforcement agencies nullevitnulland nullrnulls, nullnullnull The human resources required to maintain accurate offender information are substantial because these databases are relatively dynamic and quicnully outdated. null part, this is because offenders are a highly mobile null nullcob nulletterling Crimes nullainst Children and nullnullally nulliolent Offender nullgistration nullt. nullnull. nulliolent Crime Control and nullw nullforcement nullt of nullnull, nullnull nullnull C.null.null nullnullnullnull. null nullgannulls nullw, Public nullw nullnullnullnullC.null.null nullnullnullnull nullnullof the nulliolent Crime Control and nullw nullforcement nullt of nullnull. null nullychner nullnullal Offender Tracnullng and nullentification nullt. nullnull. Public nullw nullnullnullnullC.null.null null nullam nullalsh Child Protection and nullfety nullt of nullnull. Public nullw nullnullnullnull

10 Crime and Urban Form Grubesic, Murray, and Mack population, changing residences within and between nullrisdictions nullor enullmple, county to county, state to statenullfrequently nullurray et al., in pressnullTurley and nulltnulll, nullnullnull nullrther, many offend - ers are simply noncompliant, failing to register, re-register, or de-register after they have moved. Therefore, it is common for law enforcement officials to lose tracnullof an offendernull whereabouts, leading to inaccurate and outdated registry databases nullohen and nullglic, nullnullnullDavey, nullnullnullnullcnull and nullubesic, nullnullnull State Laws nullnulloffender laws at the state level consist of a more varied minullof strategies for managing con - victed offenders. null nullrst, registration requirements vary substantially between states. nullthough all offenders are required to register, as mandated by the nulllsh nullt, the information that is collected during the registration process differs. nullr enullmple, in the state of nullssouri, officials collect an assortment of personal information, including a Dnull sample, but the state of nullaho does not mandate Dnull collection. These same state-level differences also manifest in senulloffender residence restrictions. These restrictions are designed to control the spatial distribution of convicted offend- ers, effectively limiting potential enullosure to children by forcing offenders to establish a permanent residence beyond a prespecified distance from a sensitive facility. nullcause no consistent definition enullsts among states, restriction distances and the types of facilities deemed sensitive nullchools, daycare centers, parnull, and bus stopsnullvary considerably across states. This variability generates a haphanullrd landscape of restrictions for convicted offenders. nullpronullmately null states maintain some type of residence restriction with little consistency among them. nullr enullmple, nullorida and Ohio mandate that offenders live outside a nullnullnullfoot nullnullfrom schools and daycare facilitiesnullnulls - sissippi stipulates a nullnullnullfoot nullnull nulllinois, nullnullfeetnulland California, nullnullnullfeet. nullendments to the laws further complicate matters. nullr enullmple, nullorgia recently amended its senulloffender laws with nulluse nullll nullnull null Depending on when the offender was convicted, he or she may or may not have any residence and employment restrictions. null Other state-level efforts designed to manage offenders include state-funded efforts to apprehend and prosecute offenders in violation of registration laws. nullthough these efforts are not generally classified as senulloffender nullaws,nullthey are indicative of state-level efforts to monitor this population. nullr enullmple, both nullinullna and nullsconsin have implemented state-funded tasnullforces to determine the whereabouts of absconded offenders and prosecute them nullroves, nullnullnullnullbiano, nullnullnull Local Laws nullst of the local senulloffender laws in the nullited nullates are largely derived from state-level policies. null particular, residence restrictions are often adnullsted at the local level in an effort to renullct more conservative community standards and preferences regarding senulloffenders. nullr enullmple, nullorida has nearly nullnulldifferent local residence restrictions in place. These restrictions range from the null nullobal Positioning nullstems and lifetime supervision policies are also used in many states nullohen and nullglic, nullnullnull null nullorgia nullneral nullsembly nulluse nullll nullnull nullnull. null nullr a thorough summary of these amendments, see httpnullnullww.gachaplains.orgnulldfnullnullnullnullnullmmary.pdf .

11Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis state-level minimum distance of nullnullnullfeet, up to nullnullnullfeet locally nullillian, nullnullnull nullthough the structure of the nulllorida residence restrictions is fairly standard, many other communities are developing and implementing alternative strategies for managing the spatial distribution of convicted offenders. nullr enullmple, one alternative approach seenull to control the spatial density of convicted offenders. This approach uses laws that are designed to reduce the interaction between offenders and the enull- posure of communities to these potentially problematic populations nullrubesic and nullrray, nullnullnull nullecifically, in nulloonullaven Town, nullw nullrnull it is illegal for more than two registered offenders to live in the same single-family home nullnullhittle, nullnullnullnullnull. The locality levies fines against both the offenders and the landlord in this case. nullmilarly, a senate bill in nullinullna that did not pass attempted to limit the number of offenders in multiunit apartment complenulls to at most null percent of the units. null nullr - ther, a different bill in nullinullna sought to prohibit convicted senulloffenders from living in the same residential structure and no closer than nullnullnullfeet of each other. null This type of dispersion strategy is increasingly popular, with similar approaches enacted in California nullanullns, nullnullnullnullnulla, nullnullnull nullhagun, nullnullnull nullw nullrnullnullhittle, nullnullnull and nullsconsin nulloyd, nullnullnull Typological Summary The simple federal, state, and local typological framewornullof senulloffender laws detailed above leads to the following four summary points pertaining to housing. nullrst, residence restrictions can limit housing availability for convicted offenders nullor enullmple, nullrnes et al., nullnullnullChanullwsnull and nullrcado, nullnullnullnullndbergen and nullrt, nullnullnull nullthough this fact is rather obvious, nullnull, by design, are meant to nullep offenders from establishing a permanent residence near a school or other sensitive facility. null a result, the nullnull manull large areas of potential housing in communities off limits. nullcond, after local nullrisdictions begin to modify enullsting offender policies nullor enullmple, enullending or contracting an nullnullbased on distancenullor develop new senulloffender residence restric - tions nullor enullmple, saturation statutes and dispersion ordinancesnull estimating the actual effects on housing availability within a community becomes challenging. nullecifically, because housing avail - ability is now codependent on the distribution of sensitive facilities and on offender distributions, enullected outcomes are neither obvious nor intuitive. Third, this matrinullof local restrictions can be complicated further by the innullence of geographically pronullmate communities and their associated residence restrictions, particularly if no effort is made to coordinate policies across nullrisdictions. nullr enullmple, if nullnull spill over into adnullcent communities, some offenders may be considered non - compliant by one community but compliant by the other. null a similar vein, repercussions are linullly to emerge when one community is aggressively enforcing offender residency restrictions and their neighboring communities are not. This situation can result in an emergence of offender clusters. nullnally, the affordability of available housing must be considered in communities that have enacted senulloffender laws. null noted previously, all communities have unique morphological characteristics and have varied spatial distributions of sensitive facilities and available housing. Therefore, the ways in which these characteristics interact are important to consider when evaluating housing availability and affordability associated with senulloffender laws. null nullinullna nullate nullnate nullll nullnull. nullnull. null nullinullna nullate nullnate. nullendment to nullnull nullnull. nullnull.

12 Crime and Urban Form Grubesic, Murray, and Mack Geographical Regulations This section provides a brief review of the geographic characteristics of senulloffender laws in the nullited nullates. nullre importantly, these spatial approaches to senulloffender management are set within the historical contenull of related socially unacceptable practices. Socially Unacceptable Practices The nullited nullates has a long history of legislative approaches to regulating the spatial distribution of undesirable facilities, socially unacceptable practices, and their negative enullernalities. nullreover, the use of such laws and restrictions are by no means limited to convicted senulloffender residency issues. nullr enullmple, nullder nullnullnull noted that adult entertainment or nullicenulldistricts enullst in virtually every manullr city in the nullited nullates, from nulln nullancisco nullor enullmple, the Tenderloinnull to Philadelphia nullor enullmple, nulluth Columbus nullulevard.null to Portland, Oregon nullor enullmple, nullwer nullrnsidenull These districts are often home to businesses that sell pornographic materials and provide topless entertainment nullapayanis, nullnullnull null many of these districts, drug dealing and prostitution are not uncommon nullameron, nullnullnullnullder, nullnullnull null other instances, adult-oriented districts may give way to smaller urban localesnullodes that serve a specific social purpose but are often deemed socially undesirable. nullr enullmple, group rehabilitation homes for recovering drug addicts or senulloffenders can be viewed as obnullctionable facilities within a community. nullthough these undesirable districts and facilities may, in some cases, be transient, the approaches used to regulate their distributions are fairly standard. null fact, the strategies employed are nearly identical to the residence restriction ordinances implemented in many communities for senull offenders. nullattle, for enullmple, had nullning laws that prohibited group homes from being located within nullnullnullfeet of each other nullac Donald, nullnullnull null effect, this is a dispersion ordinance that seenull to maintain a prespecified distance between facilities. nullreover, regardless of the population being served nullor enullmple, drug addicts, victims of domestic abusenull this type of approach is undeniably a geographic-based management tool. null a slightly different contenull, there has been a relatively long history of communities attempting to control the operation and location of adult video stores, strip clubs, and pornographic movie theaters. nullain, these laws often ban the operation of these businesses from within a prespecified distance of schools and churches nullCameron, nullnullnullnullnull Papayanis, nullnullnull null effect, these ordinances demarcate nullnull, much linull those used to manage senulloffender populations throughout the nullited nullates. The constitutionality of these types of spatial restrictions on undesirable facilities was upheld by the nullpreme Court in nullnull nullapayanis, nullnullnull The legislation in question was the city of Detroitnull nullnullnti-nullnullid nullow Ordinance,null which was developed to deter the local, negative enullternalities generated by the concentration of adult boonulltores and movie theaters. null nullnnulllman nullnullnull nullnullnullnotesnull nullnulltablishments enullibiting material depicting specific senullal activities or nullnatomical areasnullas outlined by the ordinances could not be located within nullnullfeet of a residential area or nullnullnullfeet of two other regulated uses that included such business as bars, pawn - shops, pool halls and shoeshine parlors.

13Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis The written opinion by the nullpreme Court found that Detroitnull nullti-nullid nullw Ordinance and its associated spatial restrictions were acceptable land use tools for managing the spatial distributions of these businesses and preserving the quality of Detroitnulls residential neighborhoods nullnullinnullelman, nullnullnullnullnull. The historical use and application of geographic regulations across a range of social contenulls is significant for senulloffender residency laws for several reasons. nullrst, it effectively set a precedence for spatially separating problematic establishments and practices from residential areas and each other. nullcond, these types of management approaches are community specific. null other words, after the constitutionality of these approaches was established, local community standards could be used to determine how rigorous the geographic restrictions could be. nullr enullmple, while Detroit determined that nullnullfeet from residential areas and nullnullnullfeet from other regulated business was sufficient for restrictions, other communities could stipulate larger nullr smallernulldistances if neces - sary. These two elements of geographic regulation play an important role in residence restrictions for senulloffenders, the specifics of which are detailed in the subsections that follow. Sex Offender Residence Restrictions null noted in the introduction, convicted senulloffender residence restrictions vary substantially among states. nullsidence restrictions for senulloffenders are very similar to the ordinances detailed in the nullcially nullacceptable Practices section for geographically regulating establishments providing socially unacceptable practices. null essence, both types of restrictions seenullto physically separate potentially problematic individuals or activities from sensitive facilities or areas within a commu- nity. nullrther, depending on community standards, the severity of these restrictions can vary. nullr enullmple, California requires that offenders establish a permanent residence at least nullnullnullfeet away from schools or parnull, whereas convicted offenders in Ohio must establish a residence at least nullnullnullfeet away from schools or daycare centers. nullthough both geographic and contenullual differences enullst, the manifestation of nullnull in communi - ties throughout the nullited nullates is quite similar. nullr enullmple, consider the nullnullnullfoot restriction in nullmilton County, Ohio nullnullibit null. nullch nullnullis based on a single parcel, or group of parcels, that correspond to school-owned property in nullmilton County. This includes both public and private schools for the county. null noted by nullubesic, nullrray, and nullcnullnullnullnull, the use of parcel data is critical for establishing nullnull. nullecifically, if schools are represented as a single point for establishing nullnull, one would effectively ignore the presence of parnullng lots, athletic fields, and other school-affiliated grounds where sensitive populations nullor enullmple, childrennullcongregate. null a result, parcel data are critical for both establishing nullnull and estimating their effects in a commu - nity. The interpretation of enullibit nullis relatively simplenullaccording to Ohio state law, areas shaded in darnullgray are effectively off limits to offenders for establishing a permanent residence. nullhibit null provides a larger scale snapshot of how nullnull are structured locally. null this case, residential parcels colored darnullgray are still available to offenders, but those colored light gray are not because some, or all, of the parcel is within nullnullnullfeet of one or more of the three school parcels, highlighted in blacnull null noted by nullrray and nullubesic nulln pressnull there are alternative approaches for delineating the specific geometries associated with nullnull and the parcels identified as off limits. nullr enullmple, instead of using an intersection rule for identifying parcels, one might denote restricted parcels based on centroids within an nullnull a parcelnull furthest edge, or some combination of geographic

14 Crime and Urban Form Grubesic, Murray, and Mack Figure 1: Regional Impacts of Spatial Restriction Zones: Hamilton County, Ohio (SRZ = 1,000 ft.) Highway SRZ Hamilton County criteria. nullgardless, enullibit nullillustrates how nullnull manifest in most urban areas. Clearly, available housing is reduced, but as noted by nullubesic, nullrray, and nullcnullnullnullnull, more than nullnullnullnull housing units, or roughly null percent of the total, remained available when the nullnullnullfoot nullnullis imposed in nullmilton County. This study also found that appronullmately null percent of all available rental units in nullmilton County are located outside the defined nullnull. nullain, housing availability is diminished, but substantial housing options remain in nullmilton County for convicted offenders. The residence restrictions detailed in this section represent the simplest strategy for geographically managing convicted offenders. null the nenull section, more punitive hybrid strategies for managing offenders are reviewed. nullthough similarities enullst between hybrid approaches, nullnull, and the geographic management of socially unacceptable practices, a new layer of geographic complenullty is introduced when dealing with hybrid restrictions. Hybrid Residence Restrictions and SRZs: A Codependency null discussed in the nullcal nullws section, hybrid residence restrictions implemented at the local level in the nullited nullates are growing in popularity. nullbrid restrictions typically manull use of standard nullnull nullor enullmple, nullnullnullfeet from a sensitive facilitynull but also include some other geographic criteria to help manage offender distributions. nullr enullmple, dispersion ordinances require that offenders establish a permanent residence outside of the standard nullnull but also require that of - fenders maintain a prespecified distance between each other nullor enullmple, nullnullnullfeetnull This type Exhibit 1 Regional Effects of SRZs: Hamilton County, Ohio (SRZ = 1,000 ft.) SRnullnullsnulltial restriction nullnenull

15Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis Available residential parcel Restricted parcel School 1,000-foot SRZ Figure 2: Local Geographic Impacts of Spatial Restriction Zones of strategy is designed to miniminull offender clustering within a community. nullturation statutes are structured in a similar fashion. nullandard nullnull remain in effect, but the density andnullr proportion of offenders are included as a secondary criterion. nullr enullmple, a saturation statute may dictate that only null percent of residential units within a multifamily complenullcan be inhabited by offend - ers. Or, as structured in nulloonullaven Town, nullw nullrnull no more than two offenders may live in a single-family residence. The effects of these hybrid residence restrictions are significantly more challenging to estimate because housing availability, as an enullample, is suddenly codependent on the distribution of sensitive facilities and on offenders. Consider the enullample drawn from nullrubesic and nullurray nullnullnullnullnullnull for nulleading, Ohio. The goal of the nullading case study was to determine how the state-level nullnullnullfoot residence restrictions would interact with a supplemental dispersion ordinance of null,nullnullnull feet between offenders. nullecifically, nullubesic and nullrray designed a mathematical programming model to determine how many senulloffenders could reside in nullading while maintaining the stipulated spatial restrictions between offenders. nullnull The results suggested that nulleading could accommodate nullnull offenders while Exhibit 2 Local nulleognullanullhic Effects of SRZs null nulle appendinullfor a full description of this model. SRnullnullsnulltial restriction nullnenull

Unknown

16 Crime and Urban Form Grubesic, Murray, and Mack Figure 3: Maximal Residency Distribution, Reading, Ohio Source: Grubesic and Murray (2008) 0 2,000 4,0001,000 Feet Allowable residence (n = 26) 1,000-foot spatial restriction zone maintaining the criteria outlined by the hybrid strategy nullnullibit null. null nullch an approach provides a benchmarnullto evaluate contingencies associated with implementing hybrid restriction strategiesnull one that can be used to compare enullisting distributions of offenders with other possible arrangement. This case study also highlights the awnullward codependency between nullnullnulls and hybrid strategies and their nulloint impacts on housing availability for offenders. nullot only are offenders required to maintain an awareness of what housing stocnullis available outside the nullnull they must also be cogninullnt of where other offenders have established a permanent residence in an effort to adhere to the hybrid restrictionnullnullthat may be in place. Clearly, without advanced analytical and modeling approaches combined with detailed spatial information, the contingencies of these hybrid strategies are difficult to identify, especially for offenders, landlords, law enforcement agencies, and corrections officials. Exhibit 3 nullnullmal Resinullncy nullstnullnulltion, Reanullng, Ohio SourcenullGrubesic and Murray nullnullnull null null the time of the analysis, nullading was home to null convicted senulloffenders.

17Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis nullven the number of potential variations for residence restrictions and their geographic ramifica - tions, issues associated with policy coordination become more important. null particular, while residence restrictions are contingent upon local community standards, communities do not enullst in isolation. null a result, locales with strinullngly different approaches to managing senulloffenders that are geographically pronullmal to each other may create some unenullected outcomes, particularly where the spatial distribution of offenders is concerned. These issues are enulllored in the nenull section. Cross-Jurisdictional Coordination Perhaps the greatest challenge in developing and implementing senulloffender residence restrictions is coordinating ordinances and statutes between political nullrisdictions. null noted earlier, although many states have established residence restrictions, these laws largely function as a minimum requirement. nullr enullmple, nullorida mandates that convicted offenders cannot establish a permanent residence within nullnullnullfeet of a school, playground, parnull daycare center, designated school bus stop, or other places children regularly congregate, but counties and municipalities throughout the state have passed more restrictive legislation. nulllusia County, nullorida, provides an enullellent enullmple nullnullibit null. null of nullnull, nulllusia County nullhich has a population of nullnullnullnull completely contained the Deltonanullaytona nullachnullrmond nullach metropolitan area, the nullnullt largest by population in the nullited nullates nullensus, nullnullnull nulllusia County has null incorporated cities and towns and nearly null unincorporated areas. nullthin the county, nulldifferent incorporated cities and towns have residence restrictions of nullnullnullfeet. null Oanullnullll, nullorida, maintains a nullnullnullfoot restriction. nulltters are further complicated by Denullry, nullagler nullach, Orange City, and Ormond nullach, all of which added libraries and churches to the list of sensitive facilities. nullnally, nulllly nullll, nullorida, also stipulates that the restricted area is enforceable outside its city limits. This patchwornullof senulloffender laws has significant implications. Consider, for enullmple, how these laws manifest geographically in nulllusia County. null a recent report, nullnga nullnullnull noted that nullnullconvicted offenders had established a permanent residence in the county. Clearly, the complenullmesh of restriction distances and sensitive facilities within the county create challenges for offenders in locating suitable housing that is not in violation of local laws. To better illustrate the complenullties associated with the various regulations in place in nulllusia County, a parcel-level analysis was conducted. Parcel data for all schools, daycare facilities, and parnull were acquired for analysis nullolusia, nullnullnull Parcels that correspond to libraries and churches were also acquired for communities that list these as sensitive facilities for residence restrictions. nullnull were generated for each parcel to provide a generalinulld map of areas designated as off limits for convicted offenders. null The results strongly sug- gest that cross-nullurisdictional coordination is a concern in nullolusia County. nullor enullample, enullhibit null displays the three different residence restrictions and the associated nullnull in effect for the county. Three manullr points need to be made here. nullrst, large portions of nulllusia County are off limits to null Daytona nullach nullores, Denullry, Denullnd, Deltona, nullagler nullach, nulllly nullll, Orange City, and Ormond nullach maintain a nullnullnullfoot nullnull null This analysis is not completely representative of areas that are off limits to offenders. nullhool bus stops and community centers were not included in the analysis.

18 Crime and Urban Form Grubesic, Murray, and Mack Exhibit 4 nullolusia County, nullonullnull, annullnullsociatenullCommunity Resinullnce Restnullctions Figure 4: Volusia County, Florida and Associated Community Residence Restrictions 20,000 20,000 40,0000 Feet 1,000-foot SRZ 1,500-foot SRZ 2,500-foot SRZ convicted senulloffenders. nullny of the areas that are not off limits are primarily agricultural areas or protected wetlands and lanulls. nullcond, when enullmining the nullnull for nulllly nullll null,nullnullfeetnull the community that stipulates its nullnull are enforceable beyond its municipal boundaries, it becomes increasingly clear that both housing selection for convicted offenders and the implementation of SRnullnullsnulltial restriction nullnenull

19Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis residence restrictions for law enforcement agencies are enulleedingly difficult nullnullibit null. nullt only do nulllly nullllnull nullnull clearly spill over into neighboring communities, but neighboring communities that adhere to the baseline nullnullimplemented by nullorida null,nullnullfeetnullare now subnullct to nulllly nullll residence restrictions. Third, only small portions of nullolly nullill that are without sensitive facilities are available for senulloffenders to establish a permanent residence. Only a fraction of this area, however, is nullned residential. Of course, this patchwornullof restrictions is associated with a single county in nullorida. Considering that the state has nearly nullnullunique residence restrictions implemented by counties and municipalities nullillian, nullnullnull understanding effects and implications is not easy. Clearly, the lacnullof cross-nullrisdictional coordination can be problematic both locally and region - ally. The inability to unambiguously demarcate what housing units are available or unavailable to convicted offenders is one issue. nullreover, poor communication between communities also Exhibit 5 nullognullnullic nullnifestation of SRZs in nullolusia County, nullonullnull Figure 5: Geographic Manifestation of Spatial Restriction Zones in Volusia County, Florida 1,000-foot SRZ 1,500-foot SRZ 2,500-foot SRZ Sensitive facilities Hydrology Community SRnullnullsnulltial restriction nullnenull

20 Crime and Urban Form Grubesic, Murray, and Mack 0 2,0002,000 4,000 Feet 1,000-foot SRZ 1,500-foot SRZ 2,500-foot SRZ Sensitive facilities Hydrology Community nullopardinulls the ability for law enforcement agencies and corrections officials to monitor offenders. null the nenull section, we illustrate one significant outcome associated with the patchwornullof offender restrictions and poor cross-nullrisdictional coordinationnullenulloffender clusters. Offender Clusters One of the collateral consequences of the seemingly haphanullrd landscape of residence restrictions is the emergence of senulloffender clusters. nullthough much of the enullsting discourse regarding offender clusters is largely anecdotal nullonnerman, nullnullnullnullloney, nullnullnull the general consensus is that communities with a higher geographic concentration of convicted senulloffenders may be Exhibit 6 Local nullnifestation of Snulltial Restnullction Zones in nullolusia County, nullonullnull SRnullnullsnulltial restriction nullnenull

21Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis enullosed to an inordinate share of recidivistic risnull nullch clusters are, in many cases, the byproduct of varied local laws. nullrubesic nullnullnullnullnullnull provided a methodological framewornull for statistically evaluating offender concentrations, identifying several significant clusters of convicted offenders in nullllinois. One important component to this analysis that directly relates to the problems with cross-nullrisdictional coordination highlighted in the nulllusia County case study is the movement of offenders from communities with rigorous senulloffender laws into unincorporated or politically weanullnullrisdictions that do not have the ability to pass more stringent senulloffender laws or do not have the ability to use human resources nullor enullmple, law enforcementnullto manage offender populations effectively. null noted by nullubesic nullnullnull, the Palace nullbile nullme Parnullin the unincorporated community of nullalman, nullorida nullinellas Countynull is an enullmple of this problem. nullcause it is located nullnullnull feet away from the nearest school and has not passed more stringent restrictions than the state of nullorida null,nullnullfeetnull it enullibits a very high concentration of offenders. null fact, nearly null percent of its nullnullresidents are convicted offenders nullaghunathan, nullnullnull nullsimilar situation enullsted un - derneath the nulllia Tuttle Causeway in nullami, where severe residence restrictions in Dade County forced many convicted offenders to establish a tent community nullarrella and Oppmann, nullnullnull One final aspect of senulloffender clusters relates to the locations of senulloffender rehabilitation pro - grams in a community. null many states, the social and psychological services that paroled offenders are required to receive are found in a very limited number of locales. nullr enullmple, in Ohio, there are only three designated halfway homes for convicted offendersnull one in Cincinnati, one in nullansfield, and one in nullbanon nullandwernulland nulli, nullnullnull nullthough a handful of other options are available in special circumstances, these alternative facilities review offenders on a case-by-case basis and have the right to renullct offender participation. nullven a relatively limited distribution of rehabilitation facilities in Ohio and elsewhere, it is hardly surprising that many offenders concentrate in areas near rehabilitation services. null noted by nullu - besic nullnullnull, the nullmboldt Parnullneighborhood in Chicago was identified as an offender cluster, but it is also home to the largest senulloffender counseling service and rehabilitation facility in the state of nulllinois. nullmply put, one driving force behind this cluster of offenders appears to be related to access of court-mandated rehabilitation programs. nullven the results illustrated in the nullographical nullgulations section and the Cross-nullrisdictional Coordination section, it is clear that residence restrictions for convicted offenders and housing availability in communities are closely intertwined. nullny intricacies are associated with residence restrictions and hybrids that need to be considered when evaluating their potential social, economic, and geographic effects. nullusing is a manullr concern, but so are issues associated with cross- nullrisdictional planning for the geographic outcomes associated with senulloffender laws and access to state, metropolitan area, or local community services. null the nenull section, we pull together all of these threads and illustrate how senulloffender laws, urban morphology, and housing interact. nullthough the interactions are both complenulland community specific, the nenull section further illustrates why spatial analysis is critical to better understanding the ramifications of senulloffender laws and related public policies.

22 Crime and Urban Form Grubesic, Murray, and Mack Urban Morphology and Housing Availability and Affordability One final aspect of senulloffender laws and the effect of their geographic manifestations concerns the morphological structure of communities where residence restrictions are imposed. nulloadly defined, urban morphology refers to the nulltudy of the city as human habitatnullnulloudon, nullnullnullnull. nullecause communities are spatially and temporally dynamic, with their buildings, streets, parnulls, and monuments changing through time, communities represent an accumulation of many individual and collective actionsnull all of which are governed by social, economic, political, and geographic forces. null a result, housing availability and affordability in a community renullct a long history of urban development and governance, innullenced by a wide variety of geopolitical and geoeconomic forces. nullith this history in mind, the somewhat generic and arbitrary residence restrictions for convicted senulloffenders, which state legislatures ratify, may be problematic. null noted previously, restriction distances range from nullnullfeet in nulllinois to nullnullnullfeet in California. nullat remains unclear is why these particular distance restrictions are imposed. nulle these restriction distances based on the morphological structures of these communitiesnullnull so, how is the distribution of sensitive facilities and populations different in nulllinois when compared with California, which has more aggressive statewide restrictionsnullThese questions have no obvious answers, but better ways of framing these policy changes enullst. nullrst, a positive aspect of uniform restriction distances is that they provide law enforcement agencies and corrections departments a regularinulld framewornullfor managing convicted senulloffenders. nullcond, by reducing variations in residence restrictions, uniformity provides offenders with an easily understood target restriction distance during their search for per- manent housing. nullweanulless of this approach is that the one-sinull-fits-all strategy can create spatial inequities. nullcause each community has a unique distribution of housing, rents, and sensitive facilities, generic residence restrictions may predispose certain communities to a disproportionately higher number of convicted senulloffenders nullrubesic and nullrray, nullnullnullestablishing a permanent residence. null noted earlier in this article, the formation of senulloffender clusters is fueled by a combination of generic policies and poor cross-nullrisdictional coordination. nullcomparative analysis of nullannullin and nullmilton Counties in Ohio reveals the effects of urban morphology on housing availability and affordability nullrubesic, nulltisnullw, and nullrray, nullnullnull and demonstrates the need for considering the unique morphology of places when implementing various spatial restriction nullnes in communities. nulling a nullographic nullformation nullstem-based nullnull-basednullapproach for evaluating the geographic implications of different restriction distances for sensitive facilities across a suite of rental cost thresholds produced a number of interesting results. null nullr enullmple, enullibits null through null highlight variations in the availability and afford - ability of housing across four average monthly rent thresholds for each county. null this instance, the percentage of rental units potentially available null to nullnullpercentnullwithin the county is summarinulld on the y-anulls, and the variations in restriction distances null to nullnullnullfeet, in null-foot incrementsnull is summarinulld on the null-anulls. nullhibit null suggests that, with rent levels at nullnull or less, nullmilton null null nullannullin and nullmilton Counties, null.nullpercent of all elementary and secondary schools were used in this analysis. Data were missing largely because of incorrect addresses in the Ohio nullate Department of nullucation databases or indeterminate errors associated with the geographic base files used for analysis.

23Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis County always displays a higher percentage of housing availability than nullannullin County, regard - less of the restriction distances used. nullen the average rent threshold is set to nullnull or less, how - ever, nullmilton County has a higher percentage of units available up to nullnullnullfeet from sensitive facilities. nullyond this restriction distance, nullannullin County has a higher percentage of its housing available. These results are important for several reasons. nullrst, a one-sinull-fits-all strategy to restrict the residency of convicted offenders ignores the community-specific impacts of these policies. null fact, the effects of residency restrictions are highly variable because of the heterogeneous nature of community morphologies nullor enullmple, distributions of sensitive facilities, rental housing, and average rentsnull nullannullin and nullmilton Counties provide an enullmple of this variabilitynulleach county displays a different level of housing availability across average rent thresholds and restriction distances. nullcond, these results suggest that the ability to balance residence restriction parameters with the goal of ensuring the availability of affordable housing for convicted senulloffenders requires significant analytical enullloration. nullecifically, if communities are too aggressive in defining resi - dence restrictions, all housing options for offenders may be eliminated and encourage offenders to abscond or live in violation of local regulations. null communities are too lenient, they may increase the recidivistic risnullfor sensitive populations. Therefore, although a generic, state-mandated nullnull is convenient, restriction ordinances tailored to community characteristics and needs will linullly be more effective. To achieve the delicate balance of protecting vulnerable populations while ensuring the availability of suitable housing for offenders, these policies require empirical evaluation before 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.0 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (a) $500 and below Franklin Hamilton 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,08 0 4,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (b) $650 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (c) $800 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (d) $1,000 and below Franklin Hamilton Exhibit 7a Housing nullnullilanulllity at nullffenullent Contnullct nullnullan Rent Lenullls nullth nullanulling Restnullction nullstances in nullannullin annullHamilton, Ohio: null00 annullnulllonull SourcenullGrubesic, Matisnullnull, and Murray nullnullnull

24 Crime and Urban Form Grubesic, Murray, and Mack Exhibit 7b Exhibit 7c Housing nullnullilanulllity at nullffenullent Contnullct nullnullan Rent Lenullls nullth nullanulling Restnullction nullstances in nullannullin annullHamilton, Ohio: nullnull annullnulllonull Housing nullnullilanulllity at nullffenullent Contnullct nullnullan Rent Lenullls nullth nullanulling Restnullction nullstances in nullannullin annullHamilton, Ohio: null00 annullnulllonull SourcenullGrubesic, Matisnullnull, and Murray nullnullnull SourcenullGrubesic, Matisnullnull, and Murray nullnullnull 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.0 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (a) $500 and below Franklin Hamilton 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,08 0 4,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (b) $650 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (c) $800 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (d) $1,000 and below Franklin Hamilton 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.0 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (a) $500 and below Franklin Hamilton 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,08 0 4,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (b) $650 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (c) $800 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (d) $1,000 and below Franklin Hamilton

25Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis their implementation. nullsearch on this topic thus far clearly suggests that obnullctive evaluations of these policies are an important first-step in developing reasonable and actionable public policies for managing convicted offenders. Discussion and Conclusion nullven the variation in senulloffender laws in the nullited nullates, regulating offenders will continue to challenge federal, state, and local officials for many years to come. This article provided a review of community notification laws, the modern genesis of senulloffender management, and the history of regulation of socially unacceptable practices. The review of senulloffender laws and regulations highlighted both the geographical implications and the nullrisdictional issues confronting policy - manullrs and law enforcement officials now and in the future. The discussion of the effect of urban morphology on housing availability and affordability for offenders clearly illustrated the need for place-specific policies and the need for obnullective evaluations of policies before their implementation. This review also suggested three manullr themes worthy of further discussion. nullrst, implementing residence restrictions is unlinullly to abate in the nullited nullates, regardless of their controversial nature and linullly inefficacy. Currently, these laws receive too much public and legislative support in state and local communities to manull their removal realistic nullancini et al., nullnullnullnullhiavone and Exhibit 7d Housing nullnullilanulllity at nullffenullent Contnullct nullnullan Rent Lenullls nullth nullanulling Restnullction nullstances in nullannullin annullHamilton, Ohio: null,000 annullnulllonull SourcenullGrubesic, Matisnullnull, and Murray nullnullnull 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.0 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (a) $500 and below Franklin Hamilton 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,08 0 4,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (b) $650 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (c) $800 and below Franklin Hamilton 1.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 12 0 24 0 36 0 48 0 60 0 72 0 84 0 96 0 1,0801,20 0 1,32 0 1,44 0 1,56 0 1,68 0 1,80 0 1,92 0 2,04 0 2,16 0 2,28 0 2,40 0 2,52 0 2,64 0 2,76 0 2,88 0 3,00 0 3,12 0 3,2403,3603,4803,60 0 3,72 0 3,84 0 3,96 0 4,0804,20 0 4,32 0 4,44 0 4,56 0 4,68 0 4,80 0 4,92 0 5,0405,16 0 5,28 0 Distance (feet) Percent of rental units available Franklin Hamilton (d) $1,000 and below Franklin Hamilton

26 Crime and Urban Form Grubesic, Murray, and Mack nullglic, nullnullnull null fact, not only are these restrictions more popular than ever, recent evidence sug - gests that these laws are becoming increasingly punitive nullnullacnull and nullrubesic, nullnullnullnullnull, often morphing into hybrid restrictions such as dispersion ordinances and saturation statutes nullrubesic, nullnullnull nullcond, although residence restrictions and their associated nullnull are relatively simple in structure, they are implemented in highly complenull community systems. nulls a result, the outcomes of senull offender laws, both locally and regionally, can be unenullected and challenging to predict without detailed analysis. nullor enullample, the haphanullard landscape of residence restrictions and poor cross-nullurisdictional coordination in nullorida has created a highly confusing environment for both law enforcement of - ficials and convicted senulloffenders. null is enullremely difficult to determine where one set of residence restrictions ends and another begins. null addition, a number of precarious housing outcomes result from these applications of senulloffender policies, including the unintended development of senull offender clusters in unincorporated areas or along the margins of nullnull in urban areas. These types of outcomes can be avoided, but a significant amount of spatial analytical wornullis required to help evaluate the contingencies associated with these public policies before their implementation. nullthird important point is that the one-sinull-fits-all management approach employed by many states is linullly problematic and may not be consistent with local needs and obnullctives. null has already been established that communities and regions that deviate from this approach, including nullorida, create a chaotic landscape of housing availability for convicted offenders. nullmost no effort is made, how - ever, to coordinate variations in local residence restrictions between nullrisdictions. nullrther, because the morphology of each community is different, empirically based decisionmanullng should be more widely used when modifying state-mandated nullnull to better fit local community standards. nullere does this leave senulloffender policiesnullCertainly, the interaction between urban morphology and housing is very complicated and highly variable. nullso, the collateral consequences associated with these policies clearly call for a more balanced approach to ensuring community safety while also ensuring a socially supportive environment for offenders, where housing and rehabilitation needs are met. nullthough the historical approach of proposing laws and regulations without fully understanding and enullloring their implications is problematic, the evaluation of their implications is also no easy tasnull nullven these challenges, there enullsts a clear need to enulllore new analytical methods including nullnull statistical tests, and mathematical models in the evaluation stages of these policies. nullre detailed spatial information for use in these analyses is also needed, so that more effective policies can be formulated in the future. Appendix null the model used for nullading, Ohio, geographic requirements stipulated areas where convicted senulloffenders were not permitted to reside nullhat is, residence restrictionsnulland minimal separation between offenders nullhat is, dispersionnull The model produced ensured that no violations of the separation requirements occur, providing important information to corrections officials and policy- manullrs for benchmarnullng the number of offenders that could potentially reside in a region, under the proposed restrictions. This model is structured as followsnull

27Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis k nullindenullof potential residences for convicted senulloffenders.  nullminimum geographic separation between offender residences. k  nullpotential residences within stipulated separation  of residence null nullif a convicted senulloffender resides at null nullotherwise nullnullminull  k k Z nullnull Subnullct to klk lkZZ  ,1 nullnull   kZ k  1,0 nullnull nullain, this model can be used to benchmarnullthe number of convicted offenders that could potentially reside in a region, as well as where they could reside, while simultaneously ensuring that separation restrictions are maintained. The obnullctive nullnullmanullminulls the number of offenders residing in a region. Constraints nullnullensure that no two selected residences are within the separa - tion requirement. This is based on  that defines the set k  , and is established by a community or local law enforcement agency. nullteger restrictions are imposed in constraints nullnull nullnullonulledgnullnts This material is based on wornullsupported by the nulltional nullience nullundation under nullants null. nullnullnullnulland nullnullnullnull nully opinions, findings, and conclusions or recommendations enullressed in this material are those of the authornullnulland do not necessarily renullct the views of the nulltional nullience nullundation. nulltnullrs Tony null nullubesic is the director of the nullographic nullformation nullstems and nullatial nullalysis nullboratory and an associate professor in the College of nullformation nullience and Technology at Drenulll nulliversity. nullan T. nullrray is a research associate with the nulloDa Center for nullospatial nullalysis and Compu - tation and a professor in the nullhool of nullographical nulliences and nullban Planning at nullinullna nullate nulliversity. nullinullbeth null nullcnullis a research associate with the nulloDa Center for nullospatial nullalysis and Com - putation and an assistant professor in the nullhool of nullographical nulliences and nullban Planning at nullinullna nullate nulliversity. otherwise at residesoffender sex convicted a if 0 1 k Z k    

28 Crime and Urban Form Grubesic, Murray, and Mack References Barnes, J.C., Tony Dukes, Richard Tewksbury, and Timothy M. De Troye. 2009. “Ananullnullnnullthe nullnullct onulla nullatewide Residence Restriction nullw on nulluth Caronullna nullnullnullnullnders,null Criminal Justice Policy Review 20 nullnull 2nullnull. Bedarnull AbrinullR. null9null “nullamininnullnullnullnullnullnder Community nulltinullation nullws,null California Law Review nullnullnullnullnull9. Boyd, nullra. 200null “AnullouenullRestrictions on nullnullnullnullnders nullchannulld,null Green Bay Press Gazette, December 2null Burchnullnull, nullri B., and nullnulliam Minnulls. 200null “nullt in My nullinullborhoodnullAssessinnullRenullstered nullnullnullnullndersnullnullnullriences nullth nullcanullnullcianullCanulltanulland nullcianullContronullnull Criminal Justice and Behavior nullnullnullnullnullnull Cameron, nullmuenull 200null “nullace, Risk, and nullnullrtunitynullThe nullonulltion onullnullid nullnullMarkets,null Urban Studies null nullnull nullnullnullnullnull Catanullno, nullannan M. 200null “Criminanullnullctiminulltion, 200nullnull Bureau of Justice Statistics Bulletin, nullnullember nullCJ 2nullnullnullnullnullnull. Chanullwski, Michaenull and Cynthia Canullins Mercado. 2009. “An nullanullation onullnullnullnullnullnder Residency Restriction nullnctioninnullin Town, County, and Citynullide Jurisdictions,null Criminal Justice Policy Review 20 nullnull nullnullnull Cohen, Michenulle, and nullinullbeth null Jenullic. 200null “nullnullnullnullnder nullnullsnulltion in the nullited nullatesnull nullat Do null nullownull International Journal of Offender nulleranull and Comnullrative Criminolonull null nullnull null9nullnull. Danully, Monica. 200null “nullwanull Residency Runulls Drinull nullnullnullnullnders nulldernullound,null nullw nullrnull nullmes , March null. nullkins, nullunull 200null “nullnnullBeach nullinullborhood nullnullts To Monull nullnullnullnullnders,null Pressnullelenullam , nullnullember null nullnkenullan, nullunull 200null nullcyclonulldia of nullerican Civil Libertiesnullnulllnullnull . Boca Raton, nullnullCRC nulless. nullnnerman, Jenninullr. 200null “The nulluse nullere They nullnull,null nullw nullrnullnullnullzine , December null. nullonulls, nullhnie. 200null “nullnullrnor Doynull Announces nullnullnullnullnder Anullrehension and nullnullny nullnullrcement nullitiatinull.null httnullnullwww.winulloc.comnullDnullnullnullsnull 9null020null00null20 nullnull0nullnullnull0Announcement.nullnull nullccessed December null, 20nullnull nullubesic, Tony null 20null. “nullnullnullnullnder Cnullsters,null nullnullied Geonullanully nullnull2nullnull nullubesic, Tony null, Timothy C. Matisnullw, and Anulln T. Murray. 20null. nullnullnullnullnder Residence Restrictions and nullusinnullAnullinullbinulltynullAssessinnullthe nullnullcts onullnullban Mornullonullnull. nullrkinnullnullnullr nullnull0null0. nullinulldenullhianullDrenullnullnullinullrsity, nullonullanullic nullnullrmation nullstems and nullatianullAnanullsis nullboratory.

29Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Mornullologynull nullRenullenulland Syntnullsis nullubesic, Tony null, and nullan T. nullrray. nullnull. nullethods To nullpport Policy nullaluation of nullnullOf - fender nullws,null nullpers in Regional Science null nullnull nullnullnullnull nullnull nullnull. nullenullOffender nullsidency and nullatial nulluity,null nullplienullSpatial nullalysis annullnulllicy nullnullnull nullnullnullnull nullubesic, Tony null, nullan T. nullrray, and nullinullbeth null nullcnull nullnull. nullenullOffenders, nullusing and nullatial nullstriction nullnes,null nullonullurnal null nullnull nullnullnullnull nullnull nullnull. nulleographic nullclusionnullnullatial nullalysis for nullaluating the nullplications of nullgannull nullw,null Social Science Computer Review null nullnull nullnullnullnull nullandwernull, nulllicia null., and Cynthia nullli. nullnullnullnull. nullnullalfway nullouse Program Overview.null Ohio Department of Corrections and nullhabilitation. httpnullnullww.drc.ohio.govnullebnullnullnullactnulleetnullographical nullsting.pdf nullccessed December null, nullnullnull nullghes, nullrine, and Colleen nulldlecnull nullnull. nullenullOffender Community nulltification and Community nullratification,null nullstice nullarterly null nullnull nullnullnullnull nullllian, nullrnullD. nullnull. nullronberg nullnts To nullwornullnullleased nullnullOffender nullws,null nulle nullorinull Bar News, nullgust null nullvenson, nullll null nullnull. nullollateral Consequences of nullnullOffender nullsidence nullstrictions,null Criminal nullstice Stunulles nullnullnullnullnullnull nullnull nullnull. nullolicy nullterventions Designed To Combat nullnullal nullolencenullCommunity nulltifica - tion and Civil Commitment,null nullurnal onullCnulllnullSenullal nulluse nullnullnullnullnull nullvenson, nullll null, and nullo P. Cotter. nullnull. nullhe nullpact of nullnullOffender nullsidence nullstrictionsnull nullnullnullnullet nullom Danger or One nullep nullom nullsurd,null nullternational nullurnal onullnullnullnnullr nullerapy annull Comparative Criminology null nullnull nullnullnullnull nullnden, nulligh, and nullnah null nullcnullff. nullnull. nullstimates of the nullpact of Crime nullsnullon Property nulllues nullom nullgannull nullw,null nullerican Economic Review null nullnull nullnullnullnullnull nullgan, nullyne null nullnull. nullenullOffender nullgistration and Community nulltificationnullnullerging nullgal and nullsearch nullsues,null nullnals New nullrnullnullanullmy onullScience nullnull nullnullnullnull nullnga, nullda. nullnull. nulllusters of nullame,null nullytona BeacnullNewsnullournal , nullnuary null. nullc Donald, nullather. nullnull. nullig nullother nullD,null City nullurnal . httpnullnullww.city-nullurnal.orgnull articlenull.phpnull aidnullnullnull nullccessed December null, nullnullnull nullcnull nullinullbeth null, and Tony null nullubesic. nullnull. nullenullOffenders and nullsidential nullcationnullnull Predictive-nullalytical nullamewornullnull Environment annullnullanning nullnull nullnull nullnullnullnullnull nullloney, nullnnifer. nullnull. nullnger at nullnullOffender Cluster,null New nullrnullNewsnully , nullptember null. nullncini, Christina, nullan T. nullields, Daniel P. nullars, and nullvin null nullaver. nullnull. nullenullOffender nullsidence nullstriction nullwsnullParental Perceptions and Public Policy,null nullurnal onullCriminal nullstice null nullnull nullnullnullnullnull

30 Crime and Urban Form Grubesic, Murray, and Mack Mazza, Sandy. 2008. “Gardena Gets Tougher on Sex Offenders,” Daily Breeze, July 9. Moudon, Anne null null9null “nullnulln Mornullology As an nullergnullg nullterdnullnullnullnullary nulleld,” Urban Morphology null nullnull. Murray, Alan T., and Tony null Grunullsnull. nulln nullessnull “Snulltnulll nullnullrtanullty and Mathenulltnullal Modelnullgnullnullnullnullatnullns for Sex Offender Managenullnt Strategnulls.” null Community-Based Operations Research: Decision Modeling for Local Impact and Dinullrse nullpulations , ednulled null Mnullhael null Johnson, Jr. nullrlnull, GernullnynullSnullnullgernullerlag. Murray, Alan T., Tony null Grunullsnull, nulln nullnull and nullnullanullth A. Manull. nulln nullessnull “A nullnullnull Geonulldnullg Methodology for Snulltnullnullenulloral nullta,” nullansactions in nullnull . nullnullyannull, Marnullyn A. 2000. “Sex and the nullnullnnullnullt nulltynullnullnnullg Out nullrnogranully null nullnullnullrnull” nullnullronment and nullanning nullnullnullnullnullnullnull nullnull, Jaren null 2008. “nullar of nullnulle and nullusnullg nullnullesnullnullusehold nullanullnullns to Sex Offender nullgnulltrnulls,” nullurnal of Urban nullonomics null nullnull nullnullnullnull nullghunathan, Anullnull 200null “Offendersnullnullnull Trnulls To nullnullge nulleds,” nullnullnulltersburg nullmes , Onullonullr null. nullnullano, Sherry A. 200null “Arnullona Monulls To Tranull nullnull 9null Mnullsnullg Sexual nulledators,” nulle nullizona Republic , July null nullder, Andrenull 200null “The nullangnullg nullture of Adult nulltertanullnullnt nullstrnulltsnullnulltnullen a nullnull and nullrd nullanull or Gonullg nullonullStrength to Strengthnull Urban nulludies null nullnull nullnullnullnullnull Sahagun, nullunull. 2008. “nullsnullents SeenullAnullnulln Aganullst Sex Offender nulluster,” Los nullgeles nullmes , Marnull null Snullnullnullne, Stanully null, and nullnullanullth null Jeglnull. 2008. “nullnullnull nullrnullnullnulln of Sex Offender Sonullal nulllnull null nulles and the nullnullnull on Sex Offenders,” International nullurnal of Offender nullerapy and Comparatinull Criminology nullnullnull9null9null Tenullsnullry, nullnullard. 200null “nullllateral nullnsenullennulls of Sex Offender nullgnulltratnulln,” nullurnal of Contemporary Criminal nullstice 2null nullnullnull Tenullsnullry, nullnullard, and Jnulll nullnullnson. 200null “nullen nullnullennull null nullnorednullnullsnullentnulll nullstrnulltnullns for Sex Offenders,” Corrections nullday null nullnull nullnullnull Turley, nullrnullnulla, and nullaura nullutzel. 200null. “nullest nullnullrgnullnnulla Sex Offender Study.” httnullnullnullnullnullnullnull.dnullnulls.nullnull.gonullnull SAnullnullnullonullunullentsnullsexoffendernull20renullortnull200null.nulldf nullnullessed nullnullnuller null, 20nullnull nullnulled States nullnsus nullreau nullensusnull 200null “nullnulllatnulln nulltnullates.” httnullnullnullnullnullnsus.gonull nullnullstnullrnullnullesnull000snullnulltagenull00null . nullnullessed nullnullnuller null, 20nullnull nulllusnull nullunty Gonullrnnullnt Geogranullnull nullfornulltnulln Sernullnulls. httnullnullnullnullnulllusnull.orgnullnullnull nullnullessed nullnullnuller null, 20nullnull

31Cityscape Sex Offenders, Residence Restrictions, Housing, and Urban Morphology: A Review and Synthesis Whittle, Patrick. 2008. “Brookhaven Enacts Landmark Sex Offender Rnullinnullnull nullnullnenulldanullcom nullccessed nullcemnullr null, 20nullnull nullnnull nullrenullR. 20null. “nulle Emernullnnullnulliminal War on Sex Offenders,null Harvard Civil Rights-Civil Liberties Law Review null nullnull nullnullnullnull nullndnullrnulln, Panull null, and nullmothnullnull nullrt. 200null “RednullinnullnullnullinnullOnullions for nullnvicted Sex Offendersnullnullvestinulltinnullthe nullnullct of ResidencnullRestriction LanullnullinnullnullS,null Justice Research and Policy 8 nullnull null2null nullrrella, nullhn, and Patrick Onullmann. 200null “nullorida nullnullinnullSex Offenders nullder Bridnull.null httnullnull nullnullcnn.comnull00nullLnullnullnull0nullnullidnull.sex.offendersnull nullccessed nullcemnullr null, 20nullnull nullvitnull Richard null, and nullrnullnull nullrkas. 2000. “Sex Offender nullmmnullitnullnulltinullationnullnullnanullnnull nullnull Risk nulliminals or Exactinnullnullrther nullnnullancenull Behavioral Sciences and the Law nullnullnullnullnullnull nullonull, nullisten null, nullll Levenson, and nullacnullnullnulle. 200null “Examininnullthe nullnullct of Sex Offender Residence Restrictions on nullnullinnullnullailanulllitnullnull Criminal Justice Policy Review 20 nullnull nullnullnull. Additional Reading nullvenull nullnica, and nullnull nullodnonullh. 200null “nullnullts Rise null States nullld Sex Offenders nullter Prison,null New York nullmes , nullrch null nullnulles, Lorine, and nulllleen nulldleck. 2008. “Sex Offender nullmmnullitnullnulltinullation and nullmmnullitnullStratinullation,null Justice nullarterly 2nullnullnull nullnullnullnull

32 Crime and Urban Form

33Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape The Coaction of Neighborhood and Individual Effects on Juvenile Recidivism nullilip W. Harris nullrenull Mennis nullran Obradonullc nullan null Inullnnulln Heidi E. Grunnullld Temple nulliversity nullstract Ecological approacnulls to enulllaining nullvenile nulllinnullency empnullsinull tnull importance onullspatial innullences on patterns onullnulllinnullencynullStunulles onullrecinullvism among nullvenile onullnullnnullrs, on tnull otnullr nullnnull nullve rarely tanulln neignullornullonullinnullences into accountnull nullreover, conventional statistical approacnulls anullptenullnullr investigating spatial neignullor null nulloonull enullnullects, sucnull as nullierarcnullical linear monulleling nullnullnullnullnull, are typically subnullect to assumpnull tions regarnullng tnull nature onulltnull spatial relationsnullps unnullr investigation tnullt may, in nullact, masnull relevant neignullbornulloonull innulluences on innullivinullual outcomesnull nullnulle stunully nulliscussenull in tnullis article applienull geograpnullic analysis to tnulle analysis onull anullnullunullicatenull nulluvenile nullelinnulluents assignenullto courtnullrnullrenullprograms by tnull nullmily Court onullnullilanulllpnulla, nullnnsylvanianull nulle enullminenulltnull simultaneous enullnullcts onullneignullornullonullannullinnullvinullal nullnclunullng nullmilynull cnullracteristics on nullvenile recinullvism using local spatial clustering onullprobabilities onullrenull onullnullnnullngnullnullograpnullc nullnullrmation Systems provinullnulltnull tecnullology to integrate nullverse spatial nullta sets, nullantinull spatial relationsnullips, annull visualinulle tnulle results onull spatial analy null sisnull nulln tnulle contenullt onull nulluvenile recinullvism, tnulls approacnullprovinullnullnew insignulls on nullw annull wnull recinullvism rates vary nullrom place to placenull nulle nullounnull not only tnullat recinullivism was concentratenull in specinullc areas onulltnull city, but also tnullt types onullrecinullvism onullnullnses were spatially concentratenull nullportantly, tnull nullnullngs also snullw tnullt prenullctors onullreonullnullnse vary nullom place to placenull

Unknown

34 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld Introduction Criminologists, sociologists, geographers, and psychologists have long nullnown that the environment in which an individual lives shapes behavior patterns, attitudes and preferences, and interpretations of the behaviors of others. On a macrosocial scale, nullaw and nullnully nullnullnull found that spatial pat - terns of crime were associated with neighborhoods occupied by recent immigrants. They theorinulld that families and individuals that had recently arrived in Chicago were disconnected socially, and that social disorganinullation prevented informal social controls from curbing antisocial behavior. nullore recent research supports the conclusion that opportunities to engage in delinquency are greater in neighborhoods with weanullsocial organinulltion and little in the way of social controls nullampson and nulloves, nullnullnull nullonfenbrennernull ecological systems theory nullnullnull also underscored the value of the enullernal social and physical habitat in which an individual lives. nulls theory outlines four nested systems that sur - round individuals, beginning with the most pronullmate, the microsystem nullor enullmple, the familynull and ranging out to the macrosystem, or the larger social and cultural contenull of our immediate environs. nullampson nullnullnullnullnull, nullnullnullnullnull argues that social disorganinullation is not only an attribute of macro - systems, such as cities, but also is a way of loonullng at microsystems. Certain characteristics of microsystems, such as families and neighborhoods characterinulld by frayed relationships, lacnullthe means to inhibit deviant behavior. nullcompeting theoretical perspective, differential association, rests on the wornullof nulltherland and Cressey nullnullnull. This perspective attributes involvement in delinquency to the innullences of norms, values, and enullectations of people most intimately involved in an individualnull life. To the enullent that the values, norms, and behavioral enullpectations communicated by intimates support certain deviant behaviors, the individual will adopt patterns of delinquent behavior that are not considered morally wrong. nullers nullnullnull enullended this theory by adding a social learning component that enulllains the process by which deviant values and behaviors are embraced. The concept of peer contagion is another enullression of this theory, but is one that has primarily evolved to enulllain how residential programs designed to reduce delinquency can produce adverse effects nullishion and Dodge, nullnullnull although recent evidence also suggests cross-neighborhood peer contagion effects in delinquency and recidivism nullennis et al., nullnullnull nullthough these spatial perspectives speanullto the environments in which individuals reside or in which they carry on routine activities, none of them speanullto the concept of place. Place-based analyses regard places as distinct entities that tell their own stories. nullsual representations and spatial clustering of target variables across a geographically defined area can produce information not otherwise observable and facilitate the development of complenulland useful research questions. These questions can then be tested with both qualitative and quantitative social science methods. null is also worth noting that enullminations of recidivism differ in a critical way from research on delinquency. nullen studying delinquency, researchers begin with the general population of youths and predict which youths will become delinquent. nulludies of recidivism, such as this one, begin with nullown delinquents and seenullto differentiate persistent from short-term delinquents. nullny of the factors nullown to predict delinquency may have already been accounted for in the selection of this offender populationnullthus, correlates of delinquency may not be relevant to predicting further offending among nullown delinquents.

35Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull nullnumber of studies have identified individual-level predictors of recidivism. These factors include criminal history, age at first arrest, substance abuse, and education nulllliot, nullinullnga, and nulleton, nullnullnullnullrrington and nullwnullns, nullnullnullnulledericnull nullnullnullnullyder and nullcnullund, nullnullnullnullshinullwa, nullnullnull null addition, current age, negative peer relations, family problems, emotional distress, and previous treatment facility placement have been identified as individual-level attributes that increase the risnullof nullvenile recidivism nullaird, nullnullnullnullrcnullnullet al., nullnullnullnullyder and nullcnullund, nullnullnullnullebush et al., nullnullnull nulludies enullmining the effects of neighborhood-level variables on rates of nullvenile recidivism, how - ever, are less common. nullbrin and nullewart nullnullnull nullnull noted that nulle nullow very little about how the ecological characteristics of communities innullence the recidivism rates of this population.null Previous research on recidivism is largely framed in terms of program evaluation, a literature that rarely acnullowledges neighborhood contenull. nullveral meta-analyses of nullvenile treatment programs have determined that the level of success and effective components of nulluvenile treatment differ between institutional and noninstitutional programs nullipsey, nullnullnullnullpsey and nulllson, nullnullnull nullpsey and nulllson nullnullnull, in their meta-analysis of nullnullprograms for serious and violent nullvenile offenders, concluded that community-based programs garnered larger effects than did institutional programs. The purpose of this study is to address this gap in the literature regarding neighborhood effects on nullvenile recidivism. null enullmine this effect using a variety of methods that each contribute to a more complenulland useful understanding of how place and individual characteristics interact to produce continued involvement in delinquency. Philadelphia Philadelphia is bounded on the east by the Delaware nullver and bisected by the nullhuylnullll nullver in its western environs. null the appronullmate time of the data collection used in this study, the citynull population of appronullmately nullnull million was null percent nullite, null percent nullrican nullerican, nullnullpercent nullspanic, and nullnullpercent nullian. Philadelphia neighborhoods, even those characterinulld by poverty, social isolation, and crime, differ in their ability to protect their young residents from manullng contact with the nullvenile nullstice system. nullidence of these differences is found in a study conducted by nullnes et al. nullnullnull, which reported that nullP Code best predicted which first-time offenders who were enullected to become chronic offenders actually went on to become chronic offenders nullccruing three or more arrestsnull nulling the Program Development and nullaluation nullstem nullroDnullnulldatabase nullescribed in the Data sectionnull the authors developed a risnullinstrument identifying the characteristics of chronic offend - ers at the time of their first contact with the nullvenile court. Of the youths who had chronic-prone characteristics, none of the youths living in the nullnullnullnullP Code nullermantownnullwent on to become chronic offenders. On the other hand, more youths in the nullnullnullnullingsessingnullnullP Code became chronic offenders than were predicted. The authors concluded that these enulltreme differences were linullly a result of differential access to neighborhood resources. nullder nullnullnull, in her study of youths in nullvenile aftercare, used the ProDnull data and an inventory of community-based youth serving programs. nullthough she found no systematic neighborhood effects, she did identify a neighborhood nullorth Philadelphia nullstnullnullnullnullin which aftercare clients

36 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld were at a very high risnullfor unsuccessful transition bacnullinto the neighborhood. nullsecond related finding, because it involves the same general area, is that aftercare programs were not producing the same degree of positive results for nullspanic clients as for youths of other racial groups. The youths in this study were concentrated in a specific area of the city. Our study follows a similar line of inquiry. The two questions addressed in this article are nullnullnully is recidivism more common in some neighborhoods than othersnulland nullnullnully are certain types of reoffending nullffense typenullmore common in some neighborhoods than othersnull null employ a place-based perspective that can yield hypotheses about causes of recidivism linullly overloonulld in past research. Data Data on nulluvenile delinquents were acquired from the ProDnullnull database, which was developed by the Crime and nullstice nullsearch nullstitute at Temple nulliversity under a contract with the city of Phila - delphia. The ProDnull database tracnull nullveniles assigned to court-ordered programs by the nullmily Court of Philadelphia. null was designed to evaluate all programs used by the city of Philadelphia for its delinquent youths, monitoring program outcomes from nullnull to nullnull. ProDnull collected data at four points in timenullnullnullat the point of commitment to a program, nullnullat program intanull, nullnullat discharge from the program, and nullnullnullmonths after program discharge nullharges for new offenses, including adult chargesnull nullouths were ordered to attend null of nullnull community-based programs scattered throughout Philadelphia at the discretion of the nulldge. These programs include after-school programs, alternative schools, and mentoring programs that youths attend while living at home. Of the null programs, null were after-school programs that combined tutoring, group counseling, and recreational servicesnullnullwere alternative schoolsnull null combined alternative schooling with after-school activitiesnull and null were classified as mentoring programs. Of these programs, nullalso provided after-school group counseling. nullnally, nullprograms were classified as counseling programsnullthese programs, which specialinulld in services to senulloffenders or youths with substance-abuse problems, engaged youths in individual and group therapy nullto nullhours a weenull The records in ProDnull were geocoded based on the home address given at the point of program commitment. Of those records, we eliminated records of females from the analysis, because the literature and our own preliminary analyses suggested that the causes of female nullvenile recidivism differ from those of male nullvenile recidivism. null also eliminated from our analysis any period of commitment to a residential treatment program nullwhich would thus render environmental character- istics of the nulluvenilenulls residence location mootnull. nullouths in aftercare programs, however, were included. The data set used for the present analysis included null,nullnullnull case records, where each record comprises a youthnull characteristics and offense history at the point of adnulldication as well as data on the first subsequent offense nullecidivism offensenull if any. The Philadelphia Police Department also provided data for this study. These data included type and location of all crime in the city of Philadelphia from nullnull to nullnull, enullluding rape, and contain nullnullnullnullcrime events occurring during that nullyear period. The data were divided into

37Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull eight crime typesnullhomicide, robbery, assault, burglary, theft, vehicle theft, weapon violation, and drug crime. Of the nullnullnullnullcrime events in the police data, nullnullnullnullwere successfully geocoded, for a success rate of more than null percentnullell above the null-percent minimum success rate for geocoding crime data set forth by nulltcliffe nullnullnull. The neighborhood-level data included nullnull Census data. null addition, we used the neighborhood boundaries delineated by the Philadelphia nullalth nullnagement Corporation nullnullCnull which enullaustively partitions the city into null neighborhood polygons. The PnullC biannually surveys a sample of Philadelphia residents within each of the null neighborhoods and includes items related to neighborhood safety and perceptions. nulle considered several outcome variables based on different recidivating offense typesnull drug offenses, person nulliolentnulloffenses, and property offenses. nullcidivism was defined as the filing of a petition in nullmily Court for a new offense. Tracnullng of recidivism started at the point of disposition for the instant offense and ended nullmonths after program discharge. nullch of the three outcomes is dichotomousnullhether the nullvenile reoffended with an offense of that type. Of the nullnullnullnullveniles in the data set, nullnullnullnullnullpercentnullrecidivated with a drug offense nullelling or possessionnull nullnull nullnullpercentnullrecidivated with a person offense, and nullnullnullnullpercentnullrecidivated with a property offense. nullcidivism with other offense types nullor enullmple, senulloffenses, weapons offensesnullwas less common. The number of nullveniles recidivating with any offense type was nullnullnullnullnullpercentnull nullur types of enulllanatory variables are used in this study to address the theoretical mechanisms described in the literature reviewnullnullnullbacnullround characteristics of the individual nullvenile, nullnullthe initial offense that the nulluvenile committed upon entry to the nullamily Court system nullreferred to as the nullnstant offensenull, nullnullsocial disorganinulltion in the neighborhood within which the nullvenile resides, and nullnullindicators of overall delinquency and recidivism nearby the nullvenilenull residence nulleferred to as nullontagionnullvariablesnull nullhibits nulland nullreport descriptive statistics for all the categorical and continuous predictor variables used in this study, respectively. nulln nulleeping with the theme of this issue of Cityscape, this article focuses primarily on findings related to predictors of recidivism that have to do with place characteristics, as opposed to predictors related to the individual nullvenile. Exhibit 1 Predictor Variable N Percent nullscnullnullinull Statistics fonullCategonullcal nullanullanulles (null= null1null) Race nullite nullnull 11 nullnullcan nullenullcan nullnullnull null Hisnullnic nullnull 1null Othenull 1null null nullnullic assistance nullnull1 null nullnullental cnullme 1,1null 1null nullionullinstitutional linullng anullangement nullnullnull null Outnullfnullome nullacement enullnull nullnull null nullstant nullug offense 1,null1 null nullstant nullnullon offense nullnull1 null nullstant nullonullnully offense nullnull0 null

38 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld Methods null this section, we focus on findings produced by applying hot spot analysis, hierarchical linear modeling nullnullnullnullaudenbush and nullynull nullnullnull logistic regression, and mapping of results, with an emphasis on the usefulness of mapping. nullnullwas used to integrate the individual and spatial data sets, conduct hot spot analyses, and visualinull the results of logistic regression analyses that included neighborhood attributes. null conducted hot spot analysis using the locations of the residences of each youth in the sample. null differentiating youths in terms of recidivism, we revealed concentrations of recidivists. This analysis aided in the development of our research questions. nullnullis form of regression analysis that is used when some data are nested in other data, such as individuals who reside in the same neighborhood. nullcause we can assume neighborhood characteristics affect recidivism of all the delinquent youths in the neighborhood in the same way, we need to account for these factors independently of characteristics of the individuals. null a subsequent analysis, logistic regression was used with neighborhood and local deviant innullences, measured at the individual level as a function of distance from each youthnull place of residence. To aid in the interpretation of the statistical results, we also investigated local spatial clustering of probabilities using the null i nullstatistic nulletis and Ord, nullnullnullOrd and nulltis, nullnullnull which measures the degree to which the observations within a specific distance from a geographic point nullor enullmple, nullnulllometer from each youthnull place of residencenullhave values distinctly similar to, or different from, the mean for all persons in the sample. null Exhibit 2 Predictor Variable Mean Standard Deviation nullscnullnullinull Statistics fonullContinuous nullanullanulles (null= null1null) nulle (yeanull) 1nullnull 1.null nullmnullnullof nullionullanullests 0.null 1.null nullug sale nullnsity (nullnullnull null ) nullnull nullnull nullnullcent female heanullof householnullnullth chilnullen nullnull null nullnullcent nullcant housing 1null 10null nullnullcent high school gnullnullate nullnull 1null nullea nullnullnile count 1null 100 nullea any nullecinullnullsm nullte 0.null 0.null nullea nullug nullecinullnullsm nullte 0.1null 0.null nullea nullnullon nullecinullnullsm nullte 0.10 0.null nullea nullonullnully nullecinullnullsm nullte 0.11 0.null null Consider the spatial weights matrinullnull w inullnullnullnull nullsuch that w inullnullnullnull nullnullif location i is within distance null of location null, and w inullnullnullnull nullnull if it is not. null this study, null nullnullnull, chosen as a compromise between miniminullng the distance over which we hypothesinull peer contagion to occur while also allowing for a sufficient number of observations to be collected for calculation of null i null null null i nullnull � null w inull nullnullnulland Snull nulli null � null w inull nullnullnull and if null and s null denote the sample mean and variance, respectively, then G * i (d) = summation_old j w ij (d)z j – w i * z s{[(nS * 1i ) – W i *2 ]/(n – 1)} 1/2

39Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull Findings Philadelphia is a city of distinct neighborhoods, although the boundaries of these neighborhoods are ambiguously defined. nulle can characterinulle these neighborhoods in a number of ways, but because we refer to the PnullC definitions in this study, the map in enullibit nullshows these null neighborhoods along with their commonly used names. null use these labels in the discussions that follow. Philadelphia is also widely nullown to be racially and ethnically segregated. nullhibit null which shows maps created by the citynull Planning Commission, clearly shows that the nullspanic population is concentrated in an area within and surrounding the neighborhood of nullnting Parnullnullirhill. nullny of the north-center and southwestern neighborhoods have largely nullrican-nullerican populations. nullighborhoods with largely nullite populations are concentrated in the far northeast and far northwest, along the east border, and spanning two neighbor hoods in nullouth Philadelphianull nullreynulls nullerry-Passyunnull and nullastwicnull-nulllm. These racially and ethnically demarcated areas emerged as central to our conclusions regarding the nature of nulluvenile recidivism. Exhibit 3 nullhilanullelnullhianulls nulleighnullonullhoonulls Sourcenullnulliladelnullia HealtnullManagenullnt Cornullration

40 Crime and Urban Form Harris, Mennis, Obradovic, Izenman, and Grunwald Exhibit 4 Racial and Ethnic Segregation of Philadelphia’s Population by Census Tract Percent of Hispanic Population, City of Philadelphia, 2000 Less than 25null 25nullto nullnullnull 50nullto nullnullnull nullnullor nullre null population Less than 25null 25nullto nullnullnull 50nullto nullnullnull nullnullor nullre null population Less than 25null 25nullto nullnullnull 50nullto nullnullnull nullnullor nullre null population Percent of nullacnullPopulation, City of Philadelphia, 2000 Percent of nullite Population, City of Philadelphia, 2000 Source: Philadelphia City Planning Commission at wwwnullhilaplanningnullrg

41Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull nulls indicated by the locations of points in enullhibit null, which maps null-scores of the null i null applied to nulluvenile recidivism rates, the residences of nullvenile offenders are clustered geographically, with null percent of the total from nullunting Parnull, null percent from Paschallnullnullingsessing, and about null percent each from nullll Creenull nullcetownnullioga, Olney, Overbroonull and nullrawberry nullnsion nullee enullibit null. nullrther - more, enullibit nullshows that recidivism is also highly concentrated. nullr enullmple, among delinquent youths living in the adnullcent neighborhoods of nullnting Parnullnullirhill, nullniata Parnullnullrrowgate, and nullper nullnsington, recidivism rates are more than double the rate for the city as a whole. null found the PnullC-defined neighborhoods, which were useful descriptively, to be too large to capture with sufficient granularity differences among the places where these delinquent youths were living. Consequently, much of the analysis involved tracts or local spatial clustering with the null i nullstatistic, a method we used to capture the characteristics of other delinquents within a defined distance from each youthnull place of residence. null addition, we found that when recidivism was enullmined in terms of type of recidivism offense nullerson, property, or drugnull patterns of recidivism were marnulldly more place specific. nullhibit null shows final models from the logistic regression analyses of individual and census tract data. Previ- ous behavior is a strong predictor of future behavior, so the effect of number of previous arrests on recidivism was enullected. nullmilarly, we find a positive relationship between previous institutional placement and any recidivism. Exhibit 5 nullnullnile Recinullnullsm Hot Snullts nullnullnull0 nullnullnull0 to null1.0 null1.0 to 1.0 1.0 to null0 nullnull0

42 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld Exhibit 6 Predictor Variables Recidivism Drnull nullnullnse Person nullnullnse Pronullrtnull nullnullnse StenullisenullonullanullnullLogistic Regnullession of Offensenullnullcinull Recinullnullsm: nullnal nullnullls (null= null1null) (1 of null nulldividnulll nulle null 1.1nullnull (null.0null C.null 1.0null1.1null 0.nullnullnull(nullnull) C.null 0.nullnull.null null nullite 0.null (nullnull) C.null 0.nullnull.0null 0.nullnullnull(nullnull) C.null 0.nullnull.null null 1.00 (0.00) C.null 0.nullnull.null Hisnullnic 0.null (0.0null C.null 0.nullnull.1null 1.null (nullnull) C.null 0.nullnull.null 0.nullnull(null1null C.null 0.nullnull.null null nullnullic assistance 1.0null(nullnull) C.null 0.nullnull.null null null nullnullental cnullme 1.1null (nullnull) C.null 1.0null1.null null 1.nullnullnull(nullnull) C.null 1.1null1.null null nullmnullnullof nullionullanullests 1.nullnullnull(null.null) 1.1null1.null 1.0nullnull (10.null) C.null 1.0null1.1null 1.1nullnull (1nullnull) C.null 1.0null1.1null 1.1nullnull (null.null) C.null 1.1null1.null nullionullinstitutional nullacement 1.null nullnull(null.null) C.null 1.nullnull.null 1.nullnullnull(null.null) C.null 1.nullnull.null null null nullstant nullnullnse nullnulle nullug offense 1.nullnullnull(1nullnull) C.null 1.1null1.null null11nullnull(null.null) C.null 1.nullnull.null null 0.nullnullnull(1nullnull) C.null 0.nullnull.null nullnullon offense null null 1.nullnullnull(nullnull) C.null 1.10null.null null nullonullnully offense 1.nullnullnull(null01) C.null 1.0null1.null null null 1.nullnullnull(null.null) C.null 1.nullnull.null Neinullborhood Social Disornullninulltion nullea nullug sale nullnsity (ln) null null null null nullea nullnullcent female householnullnullth chilnullen (ln) 1.01 (0.0null C.null 0.nullnull.1null null null null nullea nullnullcent nullcant housing (ln) null 0.null (0.0null C.null 0.nullnull.11 null null nullea nullnullcent high school gnullnullate (ln) null null null null nullntanullon Enullnullcts nullea nullnullnile count (ln) null null null null nullea any nullecinullnullsm nullte (ln) null01nullnull(null.null) C.null nullnullnull.null null null null nullea nullug nullecinullnullsm nullte (ln) null nullnullnullnull(null.null) C.null null0nullnullnull null null nullea nullnullon nullecinullnullsm nullte (ln) null null null0nullnull (null.null) C.null nullnullnull.null null nullnullea nullnullonullenullty nullecinullinullism nullate (ln) null null nullnullnullnull(null.null) C.null nullnullnull.null Constant 1.null (nullnull) 0.10nullnull(null.null) nullnullnull (nullnull) 1.null (1.null)

43Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull The pattern for the other three offense types presents us with some indication that the type of offense matters. Our finding that delinquent youths tend to specialinull in the types of offenses they commit is consistent with previous research nullnullrmstrong, nullnullnullnullnull nulllumstein et al., nullnullnullnullnull Piquero et al., nullnullnullnullnull. nullr each offense type, some degree of specialinulltion was found. This tendency, however, is far stronger for those who committed drug offenses. nullprevious drug offense more than doubles the probability of a drug reoffense. These findings suggest that the causal mechanisms underlying drug offending differ from those innullencing other types of offending. nullonullng at environmental effects, we find that our measure of neighborhood disorganinulltion was not innullential after accounting for other predictors, but that high rates of nullvenile recidivism in the neighborhood surrounding individual youths greatly increased the linulllihood of recidivism in in - dividual offenders. This finding suggests a spatial contagion effect that is consistent with the effect of delinquent peers as a factor mediating the effect of neighborhood structural factors and parental behavior nullattarello, nullnullnullChung and nulleinberg, nullnullnull To further investigate the innullence of the contagion variables, we compared maps of the local spatial clustering of probability of recidivism from models that include individual level data only and models that include individual and census tract variables for each outcome variable. null nulleping with the theme of this issue of Cityscape, we focus this discussion on nullst two of the outcomesnulldrug offense recidivism and person offense recidivism. These results were the most interesting of all modeled offense types. nullhibit nullshows two maps of nullvenile delinquents, where each point in the maps represents the home location of an individual nullvenile and the color of the point indicates significant local spatial clustering of the probability of person offense recidivism. These maps were created from the prob- abilities generated by the analysis of two different models of person offense recidivism using the variables shown in enullibit null where the map on the top enullludes the neighborhood and contagion effect variables and the map on the bottom includes them. nulling the resulting probability data, the null i nullstatistic was calculated for each nullvenile location, using a bandwidth of nullnulllometer. nullgnificant local clusters of high and low probability of recidivism are shaded light and darnull respectively. nullee the color version of this map in the online version of Cityscape at www.huduser.org.null These clusters of high and low probability of recidivism stand in starnullcontrast to the locations of programs that serve these youths. nullhibit nullshows program locations and relative program sinulls, Exhibit 6 Predictor Variables Recidivism Drnull nullnullnse Person nullnullnse Pronullrtnull nullnullnse StenullisenullonullanullnullLogistic Regnullession of Offensenullnullcinull Recinullnullsm: nullnal nullnullls (null= null1null) (nullof null ln nullnatural log of tnull nullriablenullROC nullreceinullr onullrating cnullracteristic null nullnullnullnullnullnullnullnullnullnullnullnull nullnullnullnull nulltesnullnulldasnullnullnullindicates a nullriable tnullt nulls allonulld to enter tnullt nulldel but nulls not included by tnull stenullise nullocedurenull Cell nullalues indicate odds ratiosnull Wald statistic snullonulln in nullarentnullesesnull CnullInull indicates connulldence internullal at nullnull nullercent connulldencenull nullgelnullnulle Rnull 0.0null 0.1null 0.0null 0.0null nullea unnullnullROC cunulle 0.nullnullnull C.null 0.nullnull.null 0.nullnullnull C.null 0.nullnull.null 0.nullnullnull C.null 0.nullnull.null 0.nullnullnull C.null 0.nullnull.null

44 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld Exhibit 7 Snulltial Clustenullng of nullnulllenullnullnullon Offense Recinullnullsm nullonullnulllities fonullnullnullls nullthout (tonull annullnullth (nullttom) nullighnullnulloonullannullContagion nullanullanulles Signinullant clustenullof lonullnullonullnulllity Signinullant clustenullof high nullonullnulllity nullt a signinullant clustenull Signinullant clustenullof lonullnullonullnulllity Signinullant clustenullof high nullonullnulllity nullt a signinullant clustenull nullnullon offense nullecinullnullsm nullonullnulllities nullthout neighnullnulloonullannullcontagion nullnullanulles inclunullnull nullnullon offense nullecinullnullsm nullonullnulllities nullth neighnullnulloonullannullcontagion nullnullanulles inclunullnull measured in terms of numbers of clients. null this map indicates, most programs are located in the center city business districtnullone program is situated on the far northeast border of the city. The maps in enullhibit null on the top and bottom are marnulledly different, with the map on the top showing low predicted probability of person offense recidivism for the neighborhood of nullunting Parnull-nullairhill and vicinity nullenullhibit nullnull, with high probabilities in several nullfrican-nullmerican neighborhoods in nullorth and nullst Philadelphia. null contrast, the map on the bottom shows that including the neigh borhood and contagion variables in the model allows for low predicted probability in several other neighbor- hoods, as well as other high-probability neighborhoods in areas of nullorthwest Philadelphia and lower

45Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull nullortheast Philadelphia with predominantly nullhite populations nullsee enullhibit nullnull. These results suggest the inter action of spatial contagion with race in producing person offense recidivism outcomes. nullalogous maps of modeled probabilities of drug offense recidivism demonstrate further evidence of the interaction of contagion characteristics with race nullennis and nullrris, nullnullnull null this case, a single cluster of high probability of drug offense recidivism is located in the nullnting Parnullnullirhill and nullniata Parnullnullrrowgate neighborhoods, enullending down into the Poplar-Temple and nullwer nulln sington neighborhoods. This cluster of drug offense recidivism overlaps neatly with the area of Philadelphia with the highest concentration of nullispanic residents nullenullhibit nullnull, and, without includ- ing neighborhood and contagion variables, nullspanic race at the individual level is a highly innullen - tial factor in the linulllihood of drug offense recidivism. nullthout the inclusion of spatial variables, one could easily conclude that drug selling is an nullspanic phenomenon. nullhibit nullshows that the inclusion of neighborhood and contagion variables renders nullspanic identity nonsignificant, suggesting a mediating causal pathway where neighborhood and contagion effects can be seen to operate through race. nullat is additionally remarnullble from comparing the maps in enullibit nullwith analogous maps of drug offense recidivism is the clear physical separation of drug and person offense recidivism in Philadelphia. nullch spatial pattern is also distinct from spatial patterns of the recidivism rate for all types of offenses nullnullibit null. The drug-offending neighborhood of nullnting Parnullnullirhill referenced previously is also a cluster of very low levels of person offending. nullimilarly, in the areas surrounding this drug-offending area, we find clusters of person offending but little in the way of drug offending. Exhibit null Locations annullnullnulllation Sinull of Communitynullasenullnullognullms Ponulllation 10 null 100 null0 1,000

46 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld null contrast, although drug offending and person offending are somewhat separated in the Cobbs Creenulland nullddington-Overbroonullneighborhoods nullee enullibit null, evidence suggests that violence and drug offending are more linullly to be colocated in these southwestern neighborhoods than in the drug-offending area to the north. Discussions with the Criminal nulltelligence nullit of the Philadelphia Police Department have added more to this picture. nullcording to the chief of this unit, drug selling in the nullnting Parnullnullirhill and nullniata Parnullnullrrowgate neighborhoods of the city is highly structured under one or very few large drug-selling organinulltions. nulldividuals are employed by this organinulltion to wornullregular hours at a specific hourly rate. nullven that this area is largely nullspanic, highly segregated, and economically depressed, employment options for adolescents and young adults are limited. null con - trast, drug selling in the areas of Cobbs Creenulland nullddington-Overbroonullis associated with turf gangs that use violence to protect their marnullts. The population of these economically depressed neighborhoods is largely nullrican nullerican, although the larger area is racially diverse. The interaction of race, neighborhood characteristics such as concentrated disadvantage, and spatialnulleer contagion in producing recidivism outcomes for various offense types can be inter - preted within the framewornullof urban settlement patterns of Philadelphia. nullnull many industrial cities in the nullrtheast and nulldwest nullited nullates, Philadelphia has seen substantial population decline since the nullnulls as many manufacturing industries that provided substantial employment in Philadelphia have either shut down or moved out of the city. Population decline has been as- sociated with nullhite nullght,nullwhere many of the nullite residents who wornulld in manufacturing left the inner city for the suburbs, leaving behind an aging and increasingly dilapidated housing stocnull around the old industrial core of the city. Consequently, nullrican nullericans, and later nullspanics nullrimarily from Puerto nullco until relatively recentlynull moved into these more affordable neighbor - hoods. null nullbs and commercial development nulld these neighborhoods over the ensuing decades, however, housing values stagnated and concentrated poverty became entrenched. The intense pattern of racial segregation observed in enullibit nullis a vestige of this economic and residential history of the city and has been aided by historical redlining and other discriminatory practices that encouraged residential segregation. null shown in enullibit null the nullrican-nullerican population is strongly concentrated in the poor, inner-city neighborhoods of nullrth Philadelphia and parts of nullst Philadelphia and nulluth Philadelphia. Outside of Center City and nulliversity City, which serve as the primary business and university districts, the nullite population is concentrated most heavily in relatively afnullent nullrthwest Philadelphia and in middle- and wornullng-class nullrth - east Philadelphia. nullme nullite wornullng-class neighborhoods in the inner city remain from the manufacturing past, in neighborhoods in the lower northeast, from nullannullord to Port nullchmond, in nullnayunnulland nullnullorough, and in parts of nulluth Philadelphia. null nullssey and Denton nullnullnull pointed out, the intense residential segregation that can be observed in Philadelphia acts to reinforce concentrated disadvantage by limiting access to resources and opportunities for advancement out of poverty. nullr nullveniles, we note that the combination of concentrated poverty and high levels of segregation may act to enhance peer contagion of learned criminal behavior while limiting contact with nondelinquent peers. Thus, neighborhood- and race-specific affiliation with certain offense types among nullveniles may be in part a product of the spatial clustering of concentrated disadvantage and segregation that typify the spatial enullression

47Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull of Philadelphia neighborhoods. The structural economic and discriminatory forces that have produced these highly segregated patterns of residential settlement may thus be seen as a primary mechanism in a chain of causation that produces social norms and practices in which nulluvenile offense specialinullation is allowed to develop and nullourish. These patterns of offense specialinullation can then be detected at the neighborhood level as spatial clustering in offense type. null addition, we have found evidence that such neighborhood-level enullertise in offending can then be enullorted to adnullcent neighborhoods through the process of peer contagion based on pronullmity nullennis et al., nullnullnull Limitations nullmanullr challenge to spatial and place-based analyses is the definition of spatial unit. null nullmpson, nullrenoff, and nullnnon-nullwley nullnullnull and others have noted, the term nulleighborhoodnullcan mean many things. nullsearchers often use census tracts, blocnullgroups, or administratively defined areas such as police districts. nulln this article, we use more than one method of defining spaces for different analyses, but we manull clear for each set of findings reported whether we are using PnullC-defined neighborhoods, census tracts, or a finulld distance from each youthnull home location. null used both census tracts and point-level data in this study, but we recogninulle that the ways people interact daily with their environments shapes their perceptions of what is normal, permissible, and unacceptable. nullecondly, our followup period for tracnulling recidivismnullnull months following program terminationnull may be regarded as too short. On average, these youths were at risnull in the community for nullnull months following disposition by the court, which is clearly sufficient time to be innullenced by local forces. null note that more than null percent of the youths in this study recidivated during this period of time. Certainly, more youths recidivated after more than nullmonths, but it is unlinullly that the absence of these data biased our results. null note, too, that an important social disorganinulltion concept, collective efficacy, was missing even though we had access to this measure from the PnullC data. null chose to use a finer level of spatial aggregation than the null neighborhoods that improved on the overall predictive power of the data, sacrificing this important factor in the process. Thus, although we suggest at several points that social disorganinulltion is not supported by our findings, we have not fully tested social disorganinulltion theory. Other competing enulllanations for our findings are consistent with social disorganinulltion theorynullwe have not ruled these out. nullfourth limitation has to do with offense specialinulltion. nullcent studies of offense specialinulltion have employed longitudinal analyses, enullmining several offense transitions over time. null have analynulld only one offense transitionnullthus, we have not included previous offense transitions that may challenge our conclusion about offense specialinulltion. nullreover, we have not enullmined changes in offending patterns with age or enullerience. Conclusions nullvenile offender recidivism plays an important role in policymanullng and program evaluation. nulltionally, nullvenile reoffending rates have been found to be as high as null percent when measuring recidivism by rearrest and as low as null percent when measuring reoffending by reconvictions

48 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld within a few years of release nullears and Travis, nullnullnull null fact, policymanullrs in nullvenile nullstice are now implementing standards for measuring recidivism that do, in fact, acnullowledge that program outcomes are spatially heterogeneous nullarris, nullcnullood, and nullngers, nullnullnull nullssing from this discussion, however, is a sense of how place determines not only whether recidivism rates are high or low, but also what nullnds of offenses are committed and why youths are pulled bacnullinto further involvement in crime. null draw four broad conclusions from the findings reported abovenull null Delinquent reoffending is spatially dependent rather than spatially diverse. This finding is strongest for drug offending, leading us to conclude that effective research on nullvenile drug offending should incorporate neighborhood contenull. null nullr some types of offending, especially drug selling, nullveniles are linullly to specialinull. This specialinulltion is linullly to be innullenced by opportunities, constraints, and pressures present in the youthnulls neighborhood. null nullcidivism offense type is spatially dependent. nullsiding in a high spatial concentration of any particular type of reoffending increases the chance that a delinquent youth will recidivate with that type of offense. null nullographically defined places provide innullences that can increase and decrease the linulllihood of recidivism, but the nature of these risnulland protective factors vary widely from neighborhood to neighborhood. null have found evidence that delinquent youths tend to specialinull in committing offenses of a particular type, but that specialinulltion is far more linullly among drug offenders than youths committing nondrug offenses. nullreover, we contend that specialinulltion is innullenced by peer con - tagion. That is, youths tend to specialinull in offenses in which other nullveniles in their neighborhood specialinull. This finding of spatially dependent specialinulltion suggests that there are neighborhood dynamics at play that we do not fully understand. The association between ethnicity and drug offending is particularly strong, and we note the effect of historical patterns of segregated nullspanic communities on drug selling discussed elsewhere nullee, for enullmple, nullurgois, nullnullnull Other studies have found that peer innullence plays a critical pronullmal role in decisions by youths to sell drugs. Their perceptions of the acceptability and profitability of drug dealing are innullenced most directly by peers and young adults within their communities nulli and nulligelman, nullnullnullnullcardo, nullnullnull nullitehead, Peterson, and nulllnulle, nullnullnull The spatial concentrations of drug selling are particularly strong, suggesting that youths in those areas are under significant pressure to participate in a busi- ness common to adult and nullvenile neighbors. Their perceptions that nullveryone is doing itnullmay be quite accurate in some of the neighborhoods we identify. The predictors of person offense specialinullation, on the other hand, although also spatially dependent, are less clear. nullmily disruption nulleferring to nullmpson and nulloves, nullnullnull in the form of parental criminality, did affect person reoffending, but we do not nullow if family disruption is more linullly in neighborhoods with high levels of person offending. On the other hand, parental criminality suggests an environment in which antisocial behavior can be learned. null nullow that aggression is a learned behavior nullnullandura, nullnullnullnullnull, suggesting support for a social learning enullplanation for the pattern we see.

49Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull Our findings, not all of which are reported in this article, suggest that neighborhood is a significant predictor of nulluvenile recidivism when offense type is ignored and when enullamining only drug offense recidivism. nullonomic disadvantage alone, however, does not play a significant role in nullvenile recidivism once the individual characteristics of nullveniles are accounted for. null should be noted, of course, that strong relationships enullst between indicators of neighborhood social disorganinulltion, such as crime and socioeconomic disadvantage, and indicators we captured at the individual level, such as race and public assistance. nullreover, we have not tested all of the elements of social disorganinulltion noted in the literature. Philadelphia is a city where historical patterns of industrial development, residential settlement, and suburbaninulltion have created a deeply segregated residential pattern with concentrated pover - ty in inner-city minority neighborhoods. null note that some previous studies that ascribe a causal effect to neighborhood social disorganinulltion used only spatially aggregated data nullor enullmple, nullmpson and nulloves, nullnullnullnullysey and nullssner, nullnullnull thus manullng it difficult to distinguish the effect of those characteristics of social disorganinulltion that may be measured at an individual level nullor enullmple, racenullfrom those that are perhaps more characteristic of a neighborhood as a whole nullor enullmple, vacant housing ratenulllthough no individual lives in a vacant house, the rate of vacant housing in a neighborhood is indicative of its characternull Our findings are inconsistent with those of nullttle and nulleinberg nullnullnull who concluded that nulldolescents who sold the most drugs were more linullly to live in contenulls characterinulld by high physical and social disordernull nullittle and nulleinberg, nullnullnullnullnull. null addition, they found that drug activity increases violence within neighborhoods, net of their measures of social disorganinull - tion. Their conclusion that nullraditional dimensions of social disorganinulltion predict drug activity which, in turn, leads to higher levels of criminal violence,nullserves to tie drug and violent offending together in disadvantaged neighborhoods nullartnullenull nullsenfeld, and nullres, nullnullnullnullnull. null find, instead, that areas with high concentrations of drug recidivism, where adult drug arrests are also concentrated, are not always the same as those areas where violence is concentrated. null would appear that in the area of Philadelphia where drug selling is most concentratednulln area that is isolated by economic disadvantage and ethnicitynulls not an area where violence is prevalent. null did note an area in the southeastern section of Philadelphia where drug selling and violence are colocated. nullain, spatial distinctions linull these will facilitate improved prediction and program. null is linullly that under conditions of specialinulltion, different offense types require different causal enulllanations. null a single causal model were all that was needed, we would not enullect to find spatial dependency of offense types. nullstead, we find areas of Philadelphia in which nullvenile recidivists are enullibiting specialinulltion of a particular offense type. This pattern of specialinulltion not only implies different causal models, but also suggests that neighborhood attributes must be part of the causal picture. nullnumber of our analyses produced findings that demonstrate that drug offending, as a form of recidivism, is different from person or property offending. nullhibit nullindicates that drug offenders, compared with youths committing person and property reoffenses, were more linullly to have com - mitted a previous drug offense, to have had a previous institutional placement, and to have resided in a neighborhood with a high nullvenile drug recidivism rate. null a separate analysis using nullnullnullot shownnull we found that drug offenders were older than the mean at the time of their first arrest.

50 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld Our interpretation of these results is that nullveniles who offend earlier in their lives are more linullly to recidivate with person and property offenses, whereas drug offenders come to the attention of the nullstice system at a later age and are linullly to continue selling drugs even after a period of resi - dential placement. These findings suggest that the causal mechanisms underlying drug offending differ from those innullencing other types of offending. The enullent of specialinulltion among drug offenders, relative to other offender types, indicates a relatively organinulld neighborhood structure that supports involvement in this type of delinquency. That is, opportunities to gain access to drugs must be present, and reinforcement of the behavior must be linullly. null the same time, the combination of poverty, nullspanic culture, and high rates of adult drug selling and specialinulltion imply that opportunities to engage in legitimate employment are less available in nullspanic neighborhoods in Philadelphia. These findings are consistent with the argument raised by nullumer and nullstafson nullnullnull, in reference to drug selling, regarding nullhe crime generating effect of a high level of commitment to monetary success goals combined with a low level of commitment to legitimate means for pursuing such goalsnullnull. nullnull. Our findings on the nullspanic neighborhoods add to this perspective by highlighting the potential for cultural responses to economic deprivation. nullveral studies conclude that the primary attraction of illicit drug selling is the potential income that is rarely attainable for youth in economically depressed neighborhoods nulleuter et al., nullnullnull Implications for Theory Different theories may be needed to enulllain place-based patterns of nullvenile recidivism and to serve as foundations for program design. nullgh levels of involvement in instrumental crimes that are prevalent among adults and nullveniles in an economically disadvantaged neighborhood are indicative of norms and values consistent with those crimes and social learning mechanisms that pull youths into participation in those specific criminal activities. Our findings with respect to drug selling in particular suggest that a differential associationnullocial learning theory such as that proposed by nullers nullnullnull best fits places where drug selling is embedded in the culture and normative behavior of a neighborhood. nullcause high levels of violent offending are more ubiquitous than drug selling and are associated with poverty, it is more linullly that violent offending occurs where social disorganinulltion is great - est. The spatial separation of drug selling recidivism and an absence of social connectedness and informal social controls linullly permit connullcts to escalate to violence and norms of toughness to dominate life on the street. nullmilarly, where violence and drug selling coenullst, such as in the southwestern part of Philadelphia, research on gang involvement becomes relevant. null particular, nullrdon et al. nullnullnull found that youths that nullined gangs were already involved in delinquency and that their involvement in drug selling increased greatly upon nullining a gang. null this case, social disorganinulltion may enulllain the enullstence of gangs and the attraction to gangs, but involvement in drug selling may be facilitated by organinulltion.

Unknown

51Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull Implications for Policy null different offense types require different causal enulllanations and if reoffense type is spatially dependent, then nullvenile nullstice programs should be designed around the causal mechanisms involved and be created with specific neighborhoods in mind. This place-based perspective implies small, neighborhood-based programs, and, in the case of an nullispanic neighborhood, culturally specific in design. nullndoubtedly, recidivism among delinquent youths is tied to other social problems in the same neighborhoods. nullcause these problems go beyond delinquency, policy changes are needed that include local public health, educational, recreational, cultural, and business organinulltions. nullhat we find in Philadelphia, however, is that community programs cluster in center city nullenullhibit nullnull, often far from their young clients, and that several of these programs are quite large and draw nulluveniles from many diverse parts of the city. These services tend to be divorced from the neighborhoods in which their clients reside. The place-based perspective suggested by this study implies that these programs should be decentralinulld, located where their clients reside, tailored to the characteristics of these local environments, and linnulld to other youth- and family-serving agencies in these same neighborhoods. nullnullonulledgnullnts The authors thannullthe following Philadelphia city officials for their support of this pronullct and their assistance in providing access to datanullCommissioner nullne nullrie nullbrose, Philadelphia Department of nullman nullrvicesnullCommissioner Charles nullmsey, Philadelphia Police Departmentnull Deputy Commissioner Patricia nullorgio-nullnull Philadelphia Police Departmentnullnullbert nulllvestuto, chief probation officer, Philadelphia nullult Probation and Parole Departmentnullnulllen nullrtnull director of research, Philadelphia nullult Probation and Parole Departmentnullnullhn Delany, deputy district at - torney and head of the Trial Division, Philadelphia District nulltorneynull Officenulland Denise Clayton, director of the nulluth nullolence nullduction Partnership. The authors also thannullthree Temple nulliversity research assistants who participated in this studynullnullian nullcnullood nullepartment of Criminal nullsticenull nulle nullpin nullepartment of Computer and nullformation nulliencesnull and nullura Chisolm nullepartment of nullographynull nullnally, the authors thannullnullnald null nulllson, program manager at the nulltional nullstitute of nullstice nullurrently at the nullnull Department of nullusing and nullban Developmentnullfor his thoughtful guidance and support during the entire course of this pronullct. This article is based on the final report of a study conducted by Philip nullrris, nullremy nullnnis, nullran Obradovic, nullan nullenman, nullidi nullunwald, nullian nullcnullood, nulle nullpin, and nullura Chisholm nullnullnull and funded by the nulltional nullstitute of nullstice. The pronullct was supported by nullard null. nullnull-null-Cnullnullnull that was awarded by the nulltional nullstitute of nullstice, the Office of nullstice Programs, and the nullnull Department of nullstice nullOnull. The opinions, findings, and conclusions or recommendations enullressed in this article are those of the authors and do not necessarily renullct the views of the DOnull

52 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld nulltnullrs Philip null. nullarris is an associate professor in the Department of Criminal nullustice at Temple nullniversity. nullremy nullnnis is an associate professor in the Department of nullography and nullban nulludies at Temple nulliversity. nullran Obradovic is a professor of computer and information sciences and the director of the Center for Data nullalytics and nullomedical nullformatics at Temple nulliversity. nullan null nullenman is a professor in the Department of nullatistics at the nullnullnullhool of nullsiness, Temple nulliversity. nullidi null nullunwald is deputy director of Public nullalth nullw nullsearch at the nullasley nullhool of nullw, Temple nulliversity. References nullers, nullnald null nullnull. Social nullarning annullSocial Structurenullnullnullneral nulleory onullCrime annullnullviance . nullstonnullnullrtheastern nulliversity Press. nullmstrong, Troy null nullnull. nullre Trends in nullecialinulltion nullross nullrests nullplained by Changes in nullecialinulltion Occurring nullth nullenull nullstice nullarterly null nullnull nullnullnullnull nullird, Christopher. nullnull. Classinullation onullnullveniles in Correctionsnullnullnullnulll Systems nullproacnull . nulldi - son, nullnullnulltional Council on Crime and Delinquency. nullndura, nullbert. nullnull. nullinciples onullBenullvior nullnullnullation . Onullord, nullited nullngdomnullnulllt, nullnehart nullnullnston. nullumer, nullic P., and nullgan nullstafson. nullnull. nullocial Organinulltion and nullstrumental Crimenull nullsessing the nullpirical nulllidity of Classic and Contemporary nullomie Theories,null Criminology nullnull nullnullnullnull nullumstein, nullfred, nullqueline Cohen, nullmnath Das, and nullumyo nullitra. nullnull. nullpecialinulltion and nullriousness During nullult Criminal Careers,null nullurnal onullnullantitative Criminology null nullnullnullnull nullurgois, Philippe. nullnull. null SearcnullonullRespectnullSelling Cracnullin El Barrio . nullw nullrnull Cambridge nulliversity Press. nullonfenbrenner, nullie. nullnull. nulle Ecology onullnullman nullvelopmentnullEnulleriments by Nature annullnullsign . Cambridge, nullnullnullrvard nulliversity Press. Cattarello, nullne null nullnull. nullommunity-nullvel nullnullences on nulldividualsnullnullcial nullnds, Peer null - sociations, and Delinquencynullnullnullltilevel nullalysis,null nullstice nullarterly null nullnull nullnullnull Chung, null nulln, and nullwrence nulleinberg. nullnull. nullelations nulltween nullighborhood nullctors, Parent - ing nullhaviors, Peer Deviance, and Delinquency nullong nullrious nullvenile Offenders,null nullvelopmental nullycnulllogy null nullnull nullnullnullnull

53Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull Dishion, Thomas, and nullnneth Dodge. nullnull. nulleer Contagion in nullterventions for Children and nullolescentsnullnullving Towards an nullderstanding of the nullology and Dynamics of Change,null nullurnal onullnullnormal Cnulllnullnullycnulllogy nullnullnullnullnullnull nullliott, Delbert null, David null nullinullnga, and nullnullnne null nulleton. nullnull. Enulllaining nulllinnullency annull nullug nulle . nullverly nulllls, Cnull nullge. nullder, nullmie null nullnull. nullraveling tnull nullb onullnullvenile nullsticenullnullrmal annullnullnullrmal Supports nullr Reinte null grating nullutnull Returning nullom nullacement . Denver, COnullnullnual nulleting of the nullerican nullciety of Criminology. nullrrington, David P., and null David nullwnullns. nullnull. nullredicting Participation, nullrly Onset and nullter Persistence in Officially nullcorded Offending,null Criminal Benullvior annullnullntal nullaltnull null nullnull. nulledericnull nulluce. nullnull. nullctors Contributing to Recinullvism nullong nullutnullnullacenullnulltnulltnull New nullrnull State nullvision nullr nullutnull nullbany, nullnullnullw nullrnullnullate Division of Criminal nullstice nullrvices, Office of nullstice nullstems nullalysis. nulltis, nullthur, and null nullith Ord. nullnull. nullhe nullalysis of nullatial nullsociation by nulle of Distance nullatistics,null nullograpnullcal nullalysis nullnullnullnullnullnull nullrdon, nullchel null, nullnnullmin null nullhey, nullinull nullwai, and nulllf nulleber. nullnull. nullntisocial nullhavior and nulluth nullng nullmbershipnullnulllection and nullcialinulltion,null Criminology null nullnull nullnullnull nullrris, Philip, nullremy nullnnis, nullran Obradovic, nullan nullenman, nullidi nullunwald, nullian nullcnullood, nulle nullpin, and nullura Chisholm. nullnull. nullvestigating the nullmultaneous nullfects of nulldividual, Program and nullighborhood nulltributes on nullvenile nullcidivism nulling nullnulland nullatial Data nullning. nullnal nullport to the nulltional nullstitute of nullstice, nullard null. nullnull-null-Cnullnullnull. nullpublished paper. Temple nulliversity. nullrris, Philip null, nullian nullcnullood, and nullnullnullngers. nullnull. nullCnullnullnullite nullpernullnullnulling annull nullasuring Recinullvism . nullaintree, nullnullCouncil of nullvenile Correctional nullministrators. nullnes, Peter null, Philip null nullrris, nullmie nullder, and nullri nullubstein. nullnull. nulldentifying Chronic nullvenile Offenders,null nullstice nullarterly null nullnull nullnullnullnull nullbrin, Charis null, and nullic null nullewart. nullnull. nullredicting nullo nulloffendsnullThe nullglected nullle of nullighborhood Contenull in nullcidivism nulludies,null Criminology null nullnull nullnullnullnull null, nullaoming, and nullsan nulligelman. nullnull. nullecent and nulltended Drug Trafficnullng nullong nullle and nullmale nullban nullrican-nullerican nullrly nullolescents,null nullnullatrics null nullnull nullnullnullnullnull nullpsey, nullrnullnull nullnull. nullan nulltervention nullhabilitate nullrious Delinquentsnull nullnals onulltnull nullerican nullanullmy onullnulllitical annullSocial Science nullnullnullnull nullnullnullnull nullpsey, nullrnullnull, and David null nulllson. nullnull. nullffective nulltervention for nullrious nullvenile Of - fendersnullnullnthesis of nullsearch.nullnull Serious annullViolent nullvenile nullnullnnullrsnullRisnullnullctors annullSuccessnulll nullterventions , edited by nulllf nulleber and David P. nullrrington. nullndon, nullited nullngdomnullnullge Publicationsnullnullnullnullnull

54 Crime and Urban Form Harris, Mennis, Obradonullc, Inullnnulln, and Grunnullld nullttle, nullchelle, and nullwrence nulleinberg. nullnull. nullsychosocial Correlates of nullolescent Drug Deal - ing in the nullner Citynullotential nullles of Opportunity, Conventional Commitments, and nullturity,null nullurnal onullResearcnullin Crime annullnulllinnullency null nullnull nullnullnullnull nullrcnullnull nulloffrey null, nullrnullnullilbrun, Tammy nullnder, and David Denulltteo. nullnull. nullredicting null - venile nullcidivism nullth the PCnullnull, nullnullnull and nullnullCnull,null nullternational nullurnal onullnullrensic nullntal nullaltnull nullnullnull nullnull. nullrtnullenull nullmiro, nullchard nullsenfeld, and Dennis nullres. nullnull. nullocial Disorganinulltion, Drug nullrnullt nulltivity, and nullighborhood nullolent Crime,null nullban nullnullirs Review null nullnull nullnullnullnull nullssey, Douglas null, and nullncy Denton. nullnull. nullerican nullartnullinull Segregation annulltnull nullnullng onulltnull nullnullrclassnull Cambridge, nullnullnullrvard nulliversity Press. nullars, Daniel, and nullremy Travis. nullnull. nulle nullmensions, nulltnullays, annullConsenullences onullnullutnull Reentry. nullshington, DCnullThe nullban nullstitute. nullnnis, nullremy, and Philip nullrris. nullnull. nullontagion and nullpeat Offending nullong nullvenile Delinquent nullcidivists,null nullurnal onullnullolescence null nullnull nullnullnullnull nullnnis, nullremy, Philip nullrris, nullran Obradovic, nullan nullenman, nullidi nullunwald, and nullian nullcnull - wood. nullnull. nullhe nullfects of nullighborhood Characteristics and nullatial nullillover on nullban nullvenile Delinquency and nullcidivism,null nulle nullonullssional nullograpnullr nullnullnull nullnullnullnull Ord, nullith null, and nullthur nulltis. nullnull. nullocal nullatial nulltocorrelation nullatisticsnullDistributional null - sues and an nullplication,null nullograpnullcal nullalysis nullnullnullnullnullnull Piquero, nullenullnull, nullymond Paternoster, Paul nullnullrolle, nullbert nullame, and Charles null Dean. nullnull. nullnset nulle and Offense nullecialinulltion,null nullurnal onullResearcnullin Crime annullnulllinnullency null nullnull nullnullnullnull nullatcliffe, nullerry null. nullnullnullnull. nullnulleocoding Crime and a nullirst nullstimate of a nullinimum nullcceptable nullit nullate,null nullternational nullurnal onullnullograpnullcal nullnullrmation Science null nullnull nullnullnull nulludenbush, nullephen null, and nullthony null nullynull nullnull. nullerarcnullcal nullnear nullnulllsnullnullplications annull nullta nullalysis nulltnullnull , nulld ed. Thousand Oanull, Cnull nullge Publications. nulluter, Peter null, nullbert nullcCoun, Patricnullnullrphy, nulllan nullrahamse, and null nullmon. nullnull. nullney nullom CrimenullnullStunull onulltnull Economics onullnullug nullaling in nullsnullngton, null . nullnta nullnica, Cnull nullnull Corporation. httpnullnullww.rand.orgnullubsnulleportsnullnullnull . nullcardo, nullabel null nullnull. nullife Choices of nullrican-nullerican nulluth nullving in Public nullusingnull Perspectives on Drug Trafficnullng,null nullnullatrics nullnullnullnullnullnullnull nullmpson, nullbert null nullnull. nullow Do Communities nulldergird or nulldermine nullman Developmentnull nulllevant Contenulls and nullcial nullchanisms.nullnull nulles null nullnull a VillagenullCommunity Enullects on Cnulllnullen, nullolescents, annullnullmilies , edited by nullan nulloth and nulln Crouter. nullhwah, nullnullnullwrence nulllbaumnull nullnull.

55Cityscape nulle Coaction of nullignullornullod and Indinulldual Effects on nullnullnile Recidinullsnull nullnullnull. nullnullnullnull. nullThe nullmbeddedness of Child and nulldolescent Developmentnull null Community-nullevel Perspective on nullrban nulliolence.null nulln Violence annull Cnullilnullnulloonull in tnulle nullnner City, edited by null. nullcCord. Cambridge, nullited nullngdomnullCambridge nulliversity Pressnullnullnullnull nullmpson, nullbert null, and null nullron nulloves. nullnull. nullommunity nullructure and CrimenullTesting nullcial-Disorganinulltion Theory,null nulle nullerican nullurnal onullSociology nullnullnullnullnullnull nullmpson, nullbert null, nullffrey D. nullrenoff, and Thomas nullnnon-nullwley. nullnull. nullssessing nullighbor - hood nullfectsnullnullcial Processes and nullw Directions in nullsearch,null nullnual Review onullSociology nullnull nullnullnullnull nullaw, Clifford null, and nullnry D. nullnully. nullnull. nullvenile nulllinnullency annullnullban nulleas . Chicagonull nulliversity of Chicago Press. nullyder, nullward null, and nulllissa nullcnullund. nullnull. nullvenile nullnullnnullrs annullVictimsnull200nullNational Report. nullshington, DCnullOffice of nullvenile nullstice and Delinquency Prevention. nulltherland, nullwin null, and Donald Cressey. nullnull. nullinciples onullCriminology, nullth ed. nullnham, nullnull nulltanullra Press. nullysey, nullnita null, and nulleven null nullssner. nullnull. nullurther Testing of nullcial Disorganinulltion Theorynullnull nullaboration of nullmpson and nullovenull Community nullructure and Crime,null nullurnal onull Researcnullin Crime annullnulllinnullency nullnull nullnullnullnull null nullitehead, Tony nullrry, nullmes Peterson, and nullnda nulllnulle. nullnull. nullhe nullustlenull nullcioeconomic Deprivation, nullban Drug Trafficnullng, and nullw-nullcome, nullrican-nullerican nullle nullnder nullentity,null nullnullatrics null nullnull nullnullnullnullnull nullebush, nullchard null, Christopher nullird, nullrry nullisberg, and David Onenull nullnull. nullisnullnullsess - ment and Classification for nullrious, nullolent, and Chronic nullvenile Offenders.nullnull nullSourceboonull Serious, Violent, annullCnullonic nullvenile nullnullnnullrs , edited by nullmes C. nullwell, nullrry nullisberg, null David nullwnullns, and nullhn null nulllson. nullverly nulllls, Cnull nullgenullnullnullnullnull nullshinullwa, nullronullnull. nullnull. nullrevention as Cumulative Protectionnullnullfects of nullrly nullmily nullpport and nullucation on Chronic Delinquency and nulls nullsnull,null nullycnulllogical Bulletin nullnullnullnull nullnullnull nullditional Reading nullder, nullmie null, Philip null nullrris, Peter null nullnes, and nullry null Poulin. nullnull. nullactors nullvolved in Decisions on Commitment to Delinquency Programs for nullrst-Time nullvenile Offenders,null nullstice nullarterly null nullnull nullnullnullnull

56 Crime and Urban Form

57Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Mapping the Spatial Influence of Crime Correlates: A Comparison of Operationalization Schemes and Implications for Crime Analysis and Criminal Justice Practice nullel M. nullplan nulltgers nulliversity nullstract nullcanulls onullcriminological researcnullnullve inullntinullnulla variety onullinnullpennullnt variables tnullt correlate signinullantly witnullparticular crime outcomesnullnullss is unnullrstoonullabout nullw tnullse correlates, or nullctors, onullcrime can be operationalinullnullto maps in ways tnullt best represent tnullir spatial innullences on tnull emergence onullnearby crime eventsnull nullgeograpnullc innullrmation system nullnullnullenables tnull enullloration onullspatial innullence, wnullcnullrenullrs to tnull way in wnullcnullnullatures onulla lannullcape anullnullct places tnullougnullut tnull lannullcapenullnullr enullmple, empirical nullowlenulle tnullt bars correlate witnulllocations onullviolent crimes can be mappenullin several ways to snullw more or less crimenullrone places, sucnull as places witnullbars, places witnulln certain nullstances nullom bars, or places witnullnullgnullr concentrations onullbarsnullRatnullr tnulln nullst a nullaturenulls presence, its innullence on space is important because contenull anullnullcts criminal benullviornullnullS enables analysts to move beyonnullnullst creating maps onullpoints tnullt coenullst witnullcrime to creating visual narratives onullnullw settings become connullcive to crimenullnullitnullgrowing use onullspatial risnullassessments annullprenullctive monullling in tnull criminal nullstice community, operationalinullng crime correlates to geograpnullc units across a lannullcape is an important tasnulltnullt renullires carenulll consinullrationnullnullis article presents nullnulla nulltailenullnullscussion onulltnull tnulloretical nullamewornullrelevant to risnullanalysis annullspatial innullence annullnullnulltnullee primary metnullnull nullr operationalinullng criminogenic nullatures to a geograpnullc mapnullnulle onulltnullse maps is inclunullnullin a risnullterrain monulll nullnullnullalong witnulltnullee otnullr spatially operationalinullnull maps onullnullnullnullrent criminogenic nullatures to pronullce a composite map onullcriminogenic

58 Crime and Urban Form Canullan Introduction nullagine an unfamiliar visitor to an nullerican city. null she stands on the sidewalnull she calls a local friend on the cell phone to meetnullhe asnull where she is. null street signs are nearby. null she walnull to the street corner to figure out the specifics of her location, she nonchalantly describes the area as the nullar districtnullto breanullthe silence over the phone and in hopes that her friend nullows where she is talnullng about. nulle is not in a bar and may not even be directly in front of a bar. nully, then, would she describe this part of the city as the bar districtnullThe simple answer, perhaps, is because she observed a high concentration of bars within the area and defined that quality of the landscape to be a bar district. nullom a criminological perspective, bars and other liquor establishments are nullown to correlate with robberies nullor enullmple, Tilley et al., nullnullnullnullight and Decnullr, nullnullnull The person might be at even greater risnullof being robbed if she is within one blocnullfrom a bar nullmith, nullanulle, and Davison, nullnullnullas opposed to farther away. Therefore, it could be argued with empiri - cal support that she is at greater risnullof robbery where she stands talnullng on the phone compared with other places in the city with a lower concentration of bars and she would not be near any of them. nullnnedy and nulln nullunschot nullnullnull null define risnullas null consideration of the probabilities of particular outcomes.nullnull the previous enullmple, the visitornull risnullof robbery is not only a function of the criminogenic features nullhat is, barsnullthemselves, but also the distribution of those features throughout the landscape, her pronullmity to them, and the distal limits of the innullences those features havenulloth individually and combinednulln the attraction of potential offenders, suitable victims, and crime nullohen and nulllson, nullnullnull nullcation matters when assessing the linulllihood of crime because crimes cluster at certain locations nullcnullet al., nullnullnullnullrries, nullnullnullnulltcliffe, nullnullnullnullerman, nullnullnullnullerman, nullrtin, and nullerger, nullnullnullnullisburd, nullnullnullnullisburd and nullaga, nullnullnull nullvironmental characteristics of these loca - tions innullence and enable the seriousness and longevity of crime problems and ensuing nullotspotsnull nullnullastrofsnulli, nulleisburd, and nullraga, nullnullnullnullnull nullherman, nullnullnullnullnull. Criminal activity concentrates at specific, select spotsnulla trend that is well supported by research and comports with the daily enulleriences of analysts in law enforcement agencies around the world. The identification of crime hotspots nullstract nullontinuednull contenulls nullr snullotings in nullvington, New nullrseynullnulle Rnull is tnulln nullconstructenullto snullw nullw eacnullspatially operationalinullnullmap layer anulls to tnull overall prenullctive valinulltynull nullnally, tnull article nullmonstrates nullw pronullcing an Rnull onulltnulloretically grounnullnull operationalinulltions onullspatial innullence nullom many risnullnullctors can be usenullas a control measure onullenvironmental contenull wnulln evaluating tnull spatial enullnullct onullplacenullasenull interventions on nullture crime eventsnullnullennullng to tnull nulltail onullmapping tnull spatial innullence onullcrime correlates is particularly important annullnecessary nullr manullminullng tnull reliability annullvalinullty onullassessments onulltnull linulllinullonullonullcrime to occur at certain places witnulln a stunull areanull

59Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice informs researchers where illegal behavior is clustered, however, not necessarily why it is clustered at a particular location. nullderstanding the spatial interaction effects of certain correlates, or factors, of crime is nully to assessing and valuing criminogenic place-based risnullnullaplan, nullnnedy, and nullller, nullnullnullnullisburd, nullnullnull nullr many decades, criminologists have identified factors at nullr features ofnullplaces that help enulllain occurrences and distributions of criminal behavior and reported crime incidents. nullt, it is not the mere presence or absence of the factors that attracts or generates crime nullrantingham and nullantingham, nullnullnull null is the spatial innullence of these criminogenic factors on their environments that enables motivated offenders and increases the linulllihood of illegal activity at certain places. nullr enullmple, as presented previously, the spatial innullence of bars as a risnullfactor for robbery could be described as nulleing within a certain distance from a single bar heightens a personnull risnullof vic - timinulltionnull or, nulleing at a place with a nearby high concentration of bars heightens a personnull risnull of victiminulltion.nullnullth of these situations could be true at the same time, or only one or neither, depending on the location within the city nullasta, nullchmond, and nullebe, nullnullnull nulltice, though, that at the microlevel unit of nullhe place where a person is standing,nullthe risnullof being robbed could vary according to the spatial innullences that bars have on the urban landscape. nullth the growing use of spatial risnullassessments and predictive analytics in the criminal nullstice community nullubin, nullnullnullnullisburd, nullnullnullnullisburd et al., nullnullnull operationalinullng the spatial innullence of crime factors to geographic units throughout a landscape is an important tasnullthat requires special consideration and tools nullaines, nullwatnully, and nullnham-Carter, nullnullnullTomlin, nullnullnull null particular, a geographic information system nullnullnullenables analysts to create visual nar - ratives of how environmental settings become conducive to crime. nullnullenables the enullloration of spatial innullence, which refers to the way in which features of a landscape affect places throughout the landscape. This concept can apply to many types of environments, including small and large enullents nullor enullmple, local or globalnulland rural, suburban, and urban areas. This article enulllores ways in which correlates of crime can be operationalinulld to maps, using nullnull to represent the correlatesnulleffects on the environments in which the crimes occur. Tending to this detailed level of mapmanullng is particularly important and necessary for manullminullng the reliability and validity of assessments of the linulllihood of crime to occur at certain places within a study area, and it is something that has been neglected in much of the wornullthat attempts to manull such assessments. nullllowing an enullanded discussion of the theoretical framewornullrelevant to risnullanalysis and spatial innullence, this article presents three primary methods for operationalinullng criminogenic features to a geographic mapnullnullnullpresence of the features, nullnullconcentration of the features, and nullnulldistance from the features. One of these maps is included in a risnullterrain model nullTnullnull framewornullthat Caplan, nullnnedy, and nullller nullnullnull proposed to assess spatial crime risnull, along with three other spatially operationalinulld maps of different criminogenic features. nullter testing the predictive valid - ity of the nullnullfor shootings in nullvington, nullw nullrsey null , the article presents the deconstruction of the model to show how each spatially operationalinulld map layer adds to the overall predictive va - liditynullrimarily because each individual operationalinulltion of spatial innullence is statistically valid by itselfnullwhen combined, they are more precise. nullnally, the article shows how producing an nullnull null The nullw nullrsey nullate Police provided shooting data through the nullgional Operations nulltelligence Center.

60 Crime and Urban Form Canullan of theoretically grounded operationalinulltions of spatial innullence from many risnullfactors yields a value of risnullthat can be used as a control measure of nullnvironmental contenullnullwhen evaluating the spatial effect of place-based interventions on future crime events. Conceptual and Theoretical Framework: The Case for Operationalizing Spatial Influences nullutine activitiesnull popular theory in the criminal nullstice sciencesnulltates that crime is linullly to occur when motivated offenders converge, suitable targets enullst, and capable guardians are lacnullng nullohen and nulllson, nullnullnull nullat is more linullly to occur is that motivated offenders will commit crime against suitable targets at certain places according to the environmental characteristics of those places that manull it easier to complete the crime successfully and reap the rewards without punishment nullor enullmple, not getting caughtnullClarnull and nulllson, nullnullnull nullutine activities theory is event focused. To apply the theory to practice, police need to focus on future events by anticipating and controlling the behavior of individuals no matter where they are or where they are traveling. This endeavor is very difficult. nullat is more manageable for police agencies is to allocate resources to places that are most attractive to motivated offenders and to places where crime is most linullly to occur given certain environmental characteristics nulleisburd, nullnullnull These places have the greatest crime risnull null the long-standing debate in criminology concerning what promotes crime, it is not enough to say that crime risnullincreases when the number of criminals increases. nullat is more linullly is that the crime risnullat places that have criminogenic attributes is higher than at other places because these locations attract motivated offenders nullr more linullly concentrate them in close locationsnulland are conducive to allowing certain events to occur. This assessment is different from saying that crime concentrates at highly dense hotspots. null suggests, instead, that individuals at greater risnullto commit crime will congregate at locations that are best suited for perpetrating it. This statement does not imply that more or better targets of crime enullst nullecause there may be less to steal and fewer rewards from robbing individualsnull but rather that the conditions for criminal behavior nullor enullmple, lower risnullof apprehension or retaliationnullare better at these places than at others. Paul and Patricia nullantingham provided important conceptual tools for understanding relation - ships between places and crimes. They referred to the nullnvironmental bacnulllothnullthat emerges from the connullence of routine activities and physical structures overlaying areas nullrantingham and nullantingham, nullnullnull This bacnullloth is dynamic and can be innullenced by the forces of nullrime at - tractorsnulland nullrime generatorsnullwhich contribute to the enullstence of crime hotspots nullrantingham and nullantingham, nullnullnull nulltractors are those specific things that attract offenders to places to commit crime. nullnerators refer to the greater opportunities for crime that emerge from increased volume of interaction occurring at these areas. The concentration of crime at specific places or hot- spots is consistent with the idea of an environmental bacnullloth, is well supported by research nullor enullmple, nullnull nullnullnullnullnullet al., nullnullnullnullrries, nullnullnullnullerman, nullrtin, and nullerger, nullnullnull and comports with the daily findings of crime analysts in law enforcement agencies around the world nulleisburd, nullnullnull Crime hotspots indicate where behavior is clustered. Connecting criminal behavior to precursory environmental contenull is more challenging, but important for comprehen - sive crime analysis and forecasting efforts. null nullbott nullnullnull nullnullnullstates about the central tenets

61Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice of the human ecologists who were the first to systematically study crime in space, nullhe Chicago nullhool thought that no social fact manulls any sense abstracted from its contenull in social nullnd often geographicnullspace and social time. nullvery social fact is situated, surrounded by other contenullual facts and brought into being by a process relating it to past contenulls.nullnullologists ranging from nullrgess nullnullnull to nullaw and nullnully nullnullnull sought out ways to enullract social indenulls from data on communities for use in their enulllanations of crime occurrence and distribution. Operationalinullng the spatial innullence of a crime factor tells a story about how that landscape feature affects behaviors and attracts or enables crime occurrence at places near to and far from the feature itself nullreundschuh and nullenhofer, nullnullnull nullen certain motivated offenders interact with suitable targets, the risnullof crime and victiminulltion conceivably increases. nullen motivated offenders interact with suitable targets at certain criminogenic places, however, the risnullof criminal victiminulltion is even higher. nullmilarly, when certain motivated offenders interact with suitable targets at places that are not conducive to crime, victiminulltion risnullis lowered. nullnullcan produce maps that visually articulate these environmental contenulls in which certain crimes are more or less linullly to occur as a result of the combined innullence of one or more criminogenic features affecting the same place nullaplan, nullnnedy, and nullller, nullnullnull null this way, criminal behavior is modeled as less deterministic and more functional of a dynamic interaction that occurs at specified places. Risks of Crime at Places Opportunities for crime are not equally distributed across places, or nullmall micro units of analysisnull nulleisburd, nullnullnullnull, and so the analytical approach to studying criminogenic places plays a critical role in the reliability and validity of efforts to assess vulnerabilities and future crime hotspots. Opportunity theorists nullor enullmple, Cohen, nulluegel, and nullnd, nullnullnullnullmon, nullnullnullhave suggested that variations in crime are enulllained by opportunities to commit crime at locations that are accessible to the offender. nullthough well developed theoretically, research has been constrained in its ability to operationalinull nullpportunitynulland to develop a metric for assessing it. Cohen and nulllson nullnullnull nullnull admitted that, although crime can be more easily facilitated if motivated offenders converge, suitable targets for victiminulltion enullst, and an absence of capable guardians occurs, nullhe risnullof criminal victiminulltion varies dramatically among the circumstances and loca - tions in which people place themselves and their property.nullCohen, nulluegel, and nullnd nullnullnull refashioned the routine activities theory, renaming it nullpportunitynulltheory, to include concepts of enullosure, pronullmity, guardianship, and target attractiveness as variables that increase victiminulltion risnull nullcommon thread among opportunity theorists and related scholarly thinnullrs is that the unit of analysis for nullpportunitynullis a place, and that the dynamic nature of that place constitutes opportunities for crime. nullr enullmple, nullnullnullnullnull nullnullnull nulle and nullshalan nullnullnull, nullars, nullott, and nullati nullnullnull, nullsta, nullchmond, and nullebe nullnullnull, and nullantingham and nullantingham nullnullnull all directly state or imply the place-based nature of criminogenic opportunities. Crime control and prevention activities must consider not only who is involved in the criminal events, nullut also the nature of the environments in which these activities tanull placenullnullennedy and nulln nullunschot, nullnullnullnullnull because opportunity for crime is an attribute of all places. null an attribute of places, op - portunity is not an absolute value, a dichotomous variable, or a static quotient. null is rarely or never nullro. Opportunity varies in degrees and changes over space and time as public perceptions about environments evolve, as new crimes occur, as police intervene, or as motivated offenders and

62 Crime and Urban Form Canullan suitable targets travel. nullsessing spatial criminogenic opportunity requires a conceptual framewornull that is attuned to incorporating multiple dynamic factors and producing intelligence that serves strategic decisionmanullng and tactical action. This intelligence production can be achieved through risnullassessment. nullennedy and nullan nullrunschot nullnullnullnullnullnull nullnull define risnull assessment as nulla consideration of the probabilities of particular outcomes.null The concept of risnull is not new or unique to the criminal nullustice community nullndrews, nullnullnullnullrgess, nullnullnullnulluecnulland nulluecnull nullnullnullnullttfredson and nullriarty, nullnullnull and risnullassessment has a long history of being used to identify, prevent, or control crime nullennedy and nulln nullunschot, nullnullnull nullsnullmodels provide tools for identifying hanullrds and vulnerabilities that can lead to crime outcomes. nullennedy and nullan nullrunschot nullnullnullnullnullnull nullnullnull surmised that risnull provides a metric that can help tie different parts of the crime problem together and offers a probabilistic interpretation to crime analysis that enables one to suggest that certain things are linullly to happen and others are preventable, based on risnullassessments. nullen nullpportunity for crimenullis thought of in terms of nullisnullof crime,nullplaces can be evaluated in terms of varying degrees of criminogenic risnull relative to certain nearby or far away criminogenic features of the environment. nullthough risnullassessment provides an efficient way to analynull crime opportunities, challenges appear in the operationalinulltion of crime opportunity in a nullnull The way that criminogenic features have been modeled in a nullnullis often contrary to how people enullerience and conceptualinull their environments nullouclelis, nullnullnullnullannulland nullrnull nullnullnull nullographers suggest that regions, such as cities, are learned by humans piecemeal over time, an assertion that is grounded in the view from psychology that nullerceptions of space, spatial cognition, and spatial behavior are scale-dependent and enullerience-based,nullenulllained nulleundschuh and nullenhofer nullnullnull nullnull. nullee also nullntello, nullnull.nullnull, when assessing the risnullof crime to occur at conceivably any location throughout a city, the use of vector points, lines, and polygons in a nullnullare poor representations of criminogenic features on a map because they bear no particular relationship to the dynamic surrounding envi- ronment. nullouclelis, nullnullnull nullhere are difficulties with this view of the world,nullenulllained Couclelis nullnullnullnullnullnull nullnullnull, nullmainly that points, lines, and polygons that define vector obnullects do not have naturally occurring counterparts in the real world.nullThe vector obnullcts are appronullmations of environmental features, but lacnull any theoretical or empirical linnull to their geographies nullnullreundschuh and nullgenhofer, nullnullnull nulload inattention to different spatial conceptualinulltions of criminogenic features has led to misrepresentations of these urban, suburban, and rural features in nullnulland resulting maps nullnullreundschuh and nullgenhofer, nullnullnullnullnull. The way people nullfor enullample, motivated offenders and suitable victimsnullconceptualinull and operate in space is an important consideration for the mapping of crime risnullthroughout landscapes. Cartographically modeling these conceptualinulltions and the spatial innullence of criminogenic features using a nullnullin a way that renullcts the actorsnullviews is an impor - tant part of what nulleundschuh and nullenhofer nullnullnull nullnull describe as null Naive nullograpnull , a set of theories of how people intuitively or spontaneously conceptualinull geographic space and timenullnullee also nullenhofer and nullrnull nullnullnull and can yield more meaningful and actionable spatial intelligence for use by public safety professionals nullnullrannull, nullnullnullnullnull nullreundschuh and nullgenhofer, nullnullnullnullnull nullarnull, nullnullnullnullnull. nullderstanding these crime-prone places, however, requires more than nullst a snapshot of how offenders and victims interact at a point in space. null shown in the following sections, the best way to map these factors for the articulation of criminogenic nullacnulllothsnullnullrantingham and

63Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice nullantingham, nullnullnullis to operationalinull the spatial innullence of each factor throughout a common landscape rather than atheoretically mapping the factors as points, lines, or polygons in a manner that nulleps them disconnected from their broader social and environmental contenulls. To succeed at this tasnull information needs to be incorporated about these places with enullected increased crime risnull. nullrtunately, decades of criminological research have identified a variety of independent variables to be significantly correlated with a variety of crime outcomes that can be used to inform enullectations. nullr enullmple, Caplan, nullnnedy, and nullller nullnullnull studied gang-related shootings and operationalinulld the spatial innullence of nullown gang membersnullresidences as nullreas with greater concentrations of gang members residing will increase the risnullof those places having shootingsnull and depicted this risnullfactor as a density map created in a nullnullfrom nullown addresses of gang membersnullresidences. nullnnedy, Caplan, and Pinull nullnullnull operationalinulld the spatial innullence of several nullown correlates of shooting events in nullwarnull nullw nullrsey, such as public housing. The spatial innullence of public housing was mapped to show all places within two blocnull from public housing to be at higher than normal risnullof shootings compared with all other places throughout the city nullor enullmple, nullnull nullnullnullnullD, nullnullnullnullwman, nullnullnullPoyner, nullnullnullnullncenulland nullancinull nullnullnull Operationalinullng the spatial innullence of crime risnullfactors addresses various theoretical and methodological issues concerning the use of nullnullfor crime forecasting and assessing place-based victiminulltion risnullnullreundschuh and nullenhofer, nullnullnull The most basic utility of this innovation is that it manullminulls the validity of cartographic models and empirical measures used for statistical tests. The nenull section demonstrates these effects to crime analysis when the same crime correlate is modeled in a nullnullaccording to a variety of spatial innullences types. Operationalizing the Spatial Influence of Criminogenic Features Three primary ways to operationalinull the spatial innullence of criminogenic features are nullnullpresence or absence of features, nullnulldensity of features, or nullnulldistance from features. nullch approach is dem - onstrated in turn. Two general lessons to be learned from the forthcoming discussionnullnullrst, it is reasonable to rely on theory and empirical research to identify crime correlates and to operational- inull their spatial innullencenullsecond, many ways enullst to operationalinull the spatial innullence of crime correlates about their environments, but some methods are more appropriate and efficacious than others. These insights reinforce the notion that approaches to crime analysis, spatial risnullassess - ment, and crime forecasting cannot be atheoretical and must be evidence based. The approaches must be grounded in ways that account for the dynamic interaction of all criminogenic features throughout a landscape. Presence or Absence of Features Crime analysis can yield different results when the same crime correlate is modeled in a nullnullnull according to a variety of empirically nullstified spatial innullences. nullhibit nullshows point symbols representing the locations of shooting incidents nullhe crimenulland bars, clubs, fast-food restaurants, and liquor stores nullhe crime risnullfactorsnullin nullvington. These crime risnullfactors, or criminogenic features of the landscape, are correlated with shooting incidents in several empirical research studies, both in nullvington and other settings nullor enullmple, nullocnulland nullocnull nullnullnullnullantingham and nullantingham,

64 Crime and Urban Form Canullan nullnullnullCaplan, nullnnedy, and nullller, nullnullnullClarnull and nullnull nullnullnullnullnull Clarnull, and nullerette , nullnullnullnullnnedy, Caplan, and Pinull, nullnullnull nullpresented on the map in enullibit nullare null features and null shooting incidents. nullsual inspection of the map suggests that shooting incidents occur near criminogenic features. Only one shooting incident was at the enullct same address as a fast-food restaurant, however, as shown in the enullent rectangle at the bottom right of the map. nullven the state of nullowledge from enullsting empirical research, it might be said that nullvington as a whole is at greater risnullfor shootings compared with other municipalities because it has higher numbers of criminogenic features. nullort of revonullng business licenses and forcibly closing all or some of the most problematic establishments, however, this fact would be less than useful for nullvingtonnull public safety practitioners who must operate at microlevel places within their municipality to suppress and prevent shootings and other violence. nullcepting that their city is empirically more crime-prone compared with other cities and doing nothing about it is not an option. Exhibit 1 nullnullnullington, nullnull, null00null: Conullnullelation of Ennullinullonmental nulleatunulles Renullnullesentenull as nullectonull nulloints Density of Features nullrs, clubs, fast-food restaurants, and liquor stores are often not the enullct locations where associat - ed crimes happen. nullther, shootings occur at places that are in some way defined or innullenced by them. null shown in enullibit null the spatial innullence of these features on shooting incidents is related to their concentration at places throughout the municipality and was operationalinulld as a raster density map. nullooting incidents are cartographically modeled as more linullly to happen at places nullnull, clunull fastnulloonullnullestaunullnt, onulllinullonullstonulle Shooting incinullnt

65Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice where bars, clubs, fast-food restaurants, and liquor stores are most concentrated. nulllacesnullare defined in the raster map as cells sinulld nullnullfeet by nullnullfeet and the distal limits of nearby features nullhat is, search radiusnullused to define the density of each place was set at nullnullnullfeet. Technically, cell sinulls determine how coarse or smooth the raster map will appearnullthe smaller the cell sinull, the smoother the map will be nullmagine pinullls on a television screennull Conceptually, raster cells are the microlevel places for which risnullis being assessed and their sinull should be meaningful for operational purposes. nullr enullmple, nullnnedy, Caplan, and Pinull nullnullnull used a cell sinull of nullnullfeet by nullnullfeet for risnullterrain modeling in nullwarnullbecause that was one-half the median blocnulllength in the city. They reasoned that about one blocnullwas a meaningful area for crime events to happen within and was small enough for targeted police interventions should risnullin these areas be found to be high. null null this study, the nullnullfoot cell nullhat is, placenullsinull was selected based on the average blocnulllength in nullvington nullnull feetnullso that the microlevel places depicted on the map in enullibit null Exhibit 2 nullnullngton, null, null0null Conullelation of Ennullnullonmental nullatunulles Renullesentenullas nullnsity of nullints null null enullreme enullmples, a cell sinull of nullinches would be unreasonable because a person cannot fit in that space and a cell sinull of null miles would not be meaningful for operational policing given the vast area within each cell. nullceptionally large cell sinulls could also create problems regarding the phenomenon of nulldge effects.nullnulltimately, the cell sinull is a subnullctive decision that should be based on how precise the risnullassessment needs to benullhe smaller the cell sinull the better the precision nulls long as the risnullfactor datanullor enullmple, point-level datanullre also precisenull The general rule of thumb is to select a cell sinull that will enable actionable interpretations of risnullterrain maps. Densitnullonullbarnulllnullnullastnullood restanullantnullinullor store nullatnulles nullnull1Snull(lonull null1 Snullto nullnullSnull nullnullSnullnull(high) Shooting incinullnt SD = standard deviation.

66 Crime and Urban Form Canullan distinguish the spatial innullence of criminogenic features as precisely as one corner or the middle of a street blocnull The nullnullnullfoot search radius for the density calculation was selected because it equaled appronullmately four blocnull nullhat is, nullnullfeet by four blocnullnull a meaningful sphere of innull - ence for these criminogenic features as attributes of places within the radius. null real terms, and to continue with the enullample from the introduction section, a person might describe the place where she is standing as the bar district because she observed many bars within a four-blocnullradius. The density map in enullhibit null is symbolinulled according to standard deviational breanulls, with all places colored in darnullr gray having density values greater than null standard deviations from the mean density valuenullhich statistically puts these places in the top nullpercent of the most densely popu - lated with criminogenic features. The lighter gray represents places with density values between null and null standard deviationsnullwhite places have density values below null standard deviation. null shown on the map, places with greater concentrations of criminogenic features appear to be more frequented by shootings than the bars, clubs, fast-food restaurants, and liquor stores themselves nullwhen compared with enullhibit nullnull. nulln fact, nullnull out of nullnull nullnullnull percentnull shootings during nullnullnullnull occurred at places with density values above null standard deviations. null is realinulld that this result is arguably due to simply identifying a larger nullatchmentnullarea to which shootings are aggregatednullompared with the feature points themselves. nullch an argument is valid and needs to be addressed for statistical tests, as will be done in later sections of this article. nullr now, however, the intention is to demonstrate operationalinullng the spatial innullence of criminogenic features in different ways. nullven the state of nullowledge from enullsting empirical literature and the spatial innullence of criminogenic features to be operationalinulld as the concentration of these features throughout the environment, it might be said that places in nullvington with higher concentrations of bars, clubs, fast-food restaurants, and liquor stores are at greater risnullfor shootings compared with all other places within the municipality. This fact might encourage nullvington police to prioritinull and allocate resources to the densest places. null this way, police could preemptively target a few high-density areas of criminogenic features rather than allocating resources to every individual feature in an effort to control and prevent shootings. Distance From Features nullrs, clubs, fast-food restaurants, and liquor stores may be considered attractors or generators of shootings nullrantingham and nullantingham, nullnullnull Perhaps they are the venues where most suitable victims hang out or where the most linullly and motivated offenders visit, become intonullcated, or lose self-control nulllarnull and nulllson, nullnullnull Due to increased police presence or other capable guardians such as bouncers, witnesses, or closed-circuit television cameras, however, offenders do not shoot their victims inside or directly outside of such facilities. nullther, shootings are more linullly to occur at certain distances away. Thought of in this way, the spatial innullence of bars, clubs, fast-food restaurants, and liquor stores on shootings is more a function of distance from the closest criminogenic feature rather than the presence or absence of the feature at the shooting incident location. nullhibit nullshows a map that operationalinulls this distal spatial innullence with a nullnullfoot buffer nullbout nullblocnull around all features. nullthough only nullshooting out of null during nullnull happened enullctly at or in a bar, club, fast-food restaurant, or liquor store, and null happened at places defined by high concentrations of these

Unknown

67Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice criminogenic features, null shootings occurred within one blocnullfrom these features. null fact, null out of the null shootings that happened at places with high-density values occurred within one blocnull from a criminogenic feature. The average nearest distance of a bar, club, fast-food restaurant, or liquor store to a shooting incident was nullnullfeet, with a standard deviational distance of nullnullfeet. nullatistically, therefore, about null percent of all shooting incidents in nullvington during nullnull could have been enullected to occur within null standard deviation, or nullnullfeet, from these criminogenic features. The operationalinulld spatial innullence of the highest risnullcreated by these features to be all places within about one blocnull, or nullnullnull feetnulla definition that was grounded in theory and empirical research, identified places with more than one-half nullnullpercentnullof all shooting incident locations. nullt is realinulled that this result is arguably due to identifying a larger catchment area to which shootings are aggregated. Compared with feature points themselves, this argument is true. The coverage area of places with density values above null standard deviations nulls shown in enullibit null is nullnullnullsquare miles, however, and the coverage area of places within one blocnull of a criminogenic feature nullas shown in enullibit null is nullnullnullsquare miles, which equals about nullnull percent of nullvingtonnull total area. nullre shootings occurred in a smaller area that was deemed affected by nearby criminogenic features in a conceptually meaningful way. nullthough empirical research suggests that bars, clubs, fast-food restaurants, and liquor stores are correlated with shootings, the most meaningful cartographic model of places that are at the greatest risnullof shootings is one that operationalinulls the spatial innull - ence of these features on shooting incidents to be up to a certain distance away. This cartographic Exhibit 3 nullnullngton, null, null0null Conullelation of Ennullnullonmental nullatunulles Renullesentenullas nullstance nullom nullints nullnull, clunull fastnulloonullnullestaunullnt, onulllinullonullstonulle null0nulloot nullffenullof featunulles Shooting incinullnt

68 Crime and Urban Form Canullan model of the spatial innullence of bars, clubs, fast-food restaurants, and liquor stores might not be the case in other nullrisdictions or for other crime types, but it is corroborated by enullsting research nullor enullmple, nullith, nullanulle, and Davison, nullnullnullTilley et al., nullnullnullnullight and Decnullr, nullnullnull and it reinforces the importance of modeling these criminogenic features in commensurate ways on a map. Modeling the Interaction of Crime Risks Throughout an Urban Landscape nullw consider the role that the interaction of spatial innullences of criminogenic features has on increasing the risnull that crime will tanull place. Caplan, nullnnedy, and nullller nullnullnull and nullnnedy, Caplan, and Pinull nullnullnull measured the place-based interaction of several criminogenic features using a technique called risnullterrain modeling. nullsnullterrain modeling is an approach to risnullas - sessment whereby separate map layers representing the innullence and intensity of each risnullfactor at every place throughout a landscape is created in a nullnull Then all risnullmap layers are combined to produce a composite risnullterrain map with values that account for all risnullfactors at every place throughout the landscape. nullsnullterrain modeling builds on principles of hotspot mapping, environ - mental criminology, and problem-oriented policing to produce maps that show where conditions are ideal or conducive for crimes to occur in the future, given the enullsting environmental contenulls. nullrther, it offers a new and statistically valid way to articulate and communicate crime-prone areas at the microlevel according to the spatial innullence of criminogenic features. nullsnullterrain modeling comprises three concepts. nullsnullsuggests the linulllihood of an event occurring given what is nullown about the correlates of that event, and it can be quantified with positive, negative, low, or high ordinal values. The measures are ordinal because it is not necessarily nullown whether a risnullvalue of null is twice as risnull as a value of null but it is higher. nulling risnullas a metric, it is possible to model how risnull evolves spatially and temporally, accounting for the different stages of a crime event nullennedy and nulln nullunschot, nullnullnull nullterrain is a grid of the study area made up of equally sinulld cells that represent a continuous landscape of places where values of risnullenullst. nullster maps are used to represent terrains in risnullterrain modeling because of their ability to model con - tinuous landscapes. Places are defined in raster maps by cells of sinull null null nullor enullmple, nullnullfeet by nullnullfeetnull nulldeling broadly refers to the abstraction of the real world at certain places. nullecifically within the contenull of risnullterrain modeling, modeling refers to the process of attributing qualities of the real world to places within a terrain, and combining multiple terrains together to produce a single composite map in which the newly derived value of each place represents the compounded criminogenic risnullof that place. Crime enulllanations can be accounted for in an nullnullby different factors that tie different compo - nents of risnulltogether to enulllain individual, group, and institutional innullences and effects on crime events. nullsnullterrain maps paint a picture, so to speanull of the combined effects of spatial innullences of different factors at every place throughout a landscape. Clustering of illegal activity in particular areas is enulllained in an nullnullby the unique combination of criminogenic factors that manull these areas opportune locations for crime. This clustering occurs where the potential for, or risnullof, crime comes as a result of all the attributes found at these places.

69Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice nullalities of places themselves do not create crime. They simply point to locations where, if the conditions are right, the risnullof crime or victiminulltion will be high. nullsnullterrain modeling assumes a step that is basic to the development of nullnullin assuming that certain locations can acquire at - tributes that when combined in prescribed ways can create contenulls in which certain outcomes are made more linullly. nullr enullmple, the attributes of open space, presence of children, and pronullmity to schools may indicate a playground. These attributes combined can be used to anticipate the types of behavior that would be enullected in a playgroundnulleducing the uncertainty that forecasts about what would transpire at a playground are wrong. null this way, risnullterrain modeling uses the spatial innullence of environmental features as a means of assigning linulllihood nullr risnull that certain events will happen at particular places. Outcomes may be benign nullor enullmple, children playingnull or they may tanull on a more sinister character in which a combination of certain types of factors related to crime creates a contenull in which the risnullof crime events can occur. nullnully component of risnullterrain modeling is to operationalinull the spatial innullence of crime risnull factors, as discussed in the previous section. The obnullctive, enulllain Caplan and nullnnedy nullnullnull, is to create separate maps of the study area, each representing the innulluence of a risnull factor throughout the landscape. null least one raster map is created for each risnullfactor to represent the intensity of its criminogenic innullence at every microlevel place throughout the study area. The resulting raster map shows where crime is most linullly to occur as a result of particular conditions within the urban form. Cells within a raster map are the unit of analysis for risnullterrain modeling. nullhibit nullpresents a risnullterrain map for shootings in nullvington. null was produced according to the steps outlined by Caplan and nullnnedy nullnullnull and includes four risnullfactors that previous empirical research found Exhibit 4 nullnullngton, null, null0null Risnullnullenullain annullShooting nullcinullnts Shooting incinullnt Risnullvalnull 0 1 null null null

70 Crime and Urban Form Canullan to be correlated with shooting incidentsnull nullang members nullnullennedy, Piehl, and nullraga, nullnullnullnullnull Topalli, nullight, and nullrnango, nullnullnull bus stops nullounullitou-nullderis, nullnullnull schools nulloncenulland nullggiani, nullnullnullnullncenulland nullbosco, nullnullnull and facilities of bars, clubs, fast-food restaurants, and liquor stores nulllocnulland nullocnull nullnullnullnullantingham and nullantingham, nullnullnullClarnull and nullnull nullnullnullnullnull Clarnull and nullerette, nullnullnullnullnnedy, Caplan, and Pinull, nullnullnull Data on gang members were com - posed of addresses of all nullown gang membersnullresidences, null and was operationalinulld as a density map because the spatial innullence of these features was understood as areas with greater concentra - tions of gang members residing will increase the risnullof those places having shootings. nulldresses of all public bus stops were obtained from null Tnullnullnull nullew nullrseynull public transportation corpora - tionnull and operationalinulled as a distance map up to nullnullnull feet away because the spatial innulluence of these features was understood as up to one and one-half blocnull away from bus stopsnullransportation resources that motivated offenders and targeted victims frequent and use regularlynullre at greater risnullfor shootings because targeted victims are most vulnerable when they arrive at or leave these destinations nullounullitou-nullderis, nullnullnull nulldresses of all public and private school buildings were obtained from the nullw nullrsey Department of nullucation through the nullw nullrsey nullographic nullfor- mation nulltwornulland operationalinulld as nullistances up to three blocnull nullp to nullnullnullfeetnullare at the greatest risnullfor shootingsnullnullu, nullnnedy, and Caplan, nullnullnull nullrs, clubs, fast-food restaurants, and liquor establishments were operationalinulld in a distance map in accordance with enullibit nulland the previous related discussion. The nullnullnullnullibit null of places in nullvington that share the locations and spatial innullences of all aforementioned shooting risnull factors has high predictive validity. nullogistic regression results presented in enullibit nullsuggest that for every increased unit of risnull the linulllihood of a shooting more than doubles nullnullnullnullnullnullnull, p nullnullnullnull nullated another way, the linulllihood of a shooting happening at particular nullnullfoot-by-nullnullfoot places in nullvington during nullnull increases by nullnullpercent as each additional risnullfactor affects that place. nullonulld at in a different way, as shown in enullibit null more than null percent of all shooting incidents occurred in the top null percent of the highest risnullplaces during calendar year nullnull nullearson chi square nullnull.nullnull df nullnull p nullnullnullnull The highest risnullplaces were designated as such with a first-tier sorting of the risnullvalues of all cells null nullnullnullnull in descending order and then a second-tier sorting null The nullw nullrsey nullate Police provided data through the nullgional Operations nulltelligence Center and the many data sets they maintain, validate, and update regularly to support internal crime analysis and police investigations. Exhibit 5 null Snullnull nullald dnull Sinull Exnullnull nullnullnullnull nullr Exnullnull nullnullr nullnullr Logistic Regnullessions fonullnullnullonull1 Risnullnullalue on nullnullonullnullShooting Risnullnulllue 0.nullnull 0.1null null.nullnull 1 null0.001 nullnull1 1.null0 nullnull0 B = Beta. C.I. = Confidence Interval. df = degrees of freedom. S.E. = standard error. Sig. = significance. Wald = Wald coefficient. Nagelkerke R Square = 0.0null.

71Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice by random number to randominull the sorting of cells with the same risnullvalues. null Then the first nullnull cells were designated as nullighest risnull and all other cells were designated as nullot highest risnullnull null sum, this risnullterrain map produced with thoughtfully operationalinulld criminogenic features yielded a valid and reliable forecasting model that was empirically and theoretically grounded. The criminogenic features were selected according to findings from previous empirical research, and they were included in the model in a manner that accounted for their spatial innullence at all nearby and faraway places throughout the landscape. The resulting risnullterrain map articulates environ - mental contenulls of places that are most linullly to attract, enable, and generate criminal shooting incidents as a function of the combined innullences of criminogenic features within nullvington. This nullnullis deconstructed in the nenull section to enulllain why it is valid, and to elucidate the nully role that operationalinullng the spatial innullence of criminogenic features had on its predictive validity. Valid Operationalizations of Spatial Influence Make Risk Terrain Modeling Work nullshooting or other crime incident could conceivably occur at any location in nullvington. That is, criminals do not generally offend with regard to census tract or other geographic units of aggrega- tion. nullvictim who was shot at nullnullnullin nullreet could nullst as linullly been shot at nullnullnullin nullreet if he stopped to tie his shoe, walnulld slower, or was delayed for any number of other reasons. nullster mapping in nullnull specifically developed to model continuous landscapes nullomlin, nullnull, nullnullnull captures this reality of how people populate, operate within, and travel through a landscape and models how crime can occur at microlevel places better than vector maps nullaplan, nullnnedy, and nullller, nullnullnullnulloff and null nullgne, nullnullnull Cells within a raster map are the standard unit of analysis for statistical testing across individual map layers and the final risnullterrain map. nullr each map layer in the nullnulldiscussed in the previous section, each cell nullhat is, placenullhas attributes that account for whether or not it is affected by a criminogenic feature, respectively. nullor enullample, enullhibit null shows the map layer of nullpatial innullence of bars, clubs, fast-food restaurants, and liquor storesnull all places within one blocnullfrom any of these features were operationalinulld as highest risnullfor shootings to occur. Cells that were located within the spatial innullence of these features were given a value of nullnull all other cells were given a value of null.nullnullsecond attribute was also assigned to each cell noting the presence nullnullnullor absence nullnullnullof one or more shooting incidents during nullnull. This null The random number ensured that every cell had an equal chance of being sorted above or below the null-percent cut point. nullr enullmple, if null out of nullnullcells had a risnullvalue of null and they were sorted in descending order, the top null percent of cells to be designated as nulligh risnull would all have risnullvalues of null The null-percent cell, however, would be enullluded due to a rather arbitrary standard sorting algorithm. Exhibit 6 Place Desinullated as nullonull1null onullnullnullest Risnull null null3null75null nullnullShootinnull Present Dnullinnull2null7 nulles null N null47null Shootings in Highest RisnullCells null null.null nulles null.null nullarson Cnullnullquare = null.nullnull df = null nullnull0.0null

72 Crime and Urban Form Canullan procedure was repeated for all four map layers of criminogenic features included in the nullnullso that the statistical validity of the operationalinulld spatial innullence of each feature could be assessed with nullby-nullcross tabulation tables and chi-square tests. null shown in enullibit null all four cartographic representations of the spatial innullence of criminogenic features in nullvington were statistically significant at p nullnullnull. Of all shootings that occurred during nullnullnullnull, nullnull percent happened at places defined by their innulluence from bus stopsnull nullnull percent happened at places defined by their innulluence from bars, clubs, fast-food restaurants, or liquor storesnull nullnull percent Exhibit 7 nullnullngton, null: Snulltial nullnullence of nullnull, Clunull, nullstnulloonullRestaunullnts, annullLinullonull StonullesnullOnullnulltionalinullnullas null0 nullet nullom nullatunulles null Highest Risnull Exhibit null nulliminonullnic nullatnulle nullerationalinulld Snulltial nullnullence onullnullnullest Risnull nullnullShootinnull Present Dnullinnull2null7 nulles null Pearson nullinullnullare Valnull Statistical nullalinullty of OnullnulltionalinullnullSnulltial nullnullence Schoolsnull nullthin nullnullocnull null.null nullnullnull nulls stonullnull nullthin 1 nullocnull null.1 nullnullnull nullnull, clunull, fastnulloonullnullestaunullnts, annulllinullonullstonullesnull nullthin 1 nullocnull null.null 1nullnullnull nullonull gang memnullnullnull nullesinullncesnull nullnsity nulllue gnulleatenullthan null stannullnullnullnullnullations null.null null.nullnull Shooting incinullnt nullatnullenulls snulltial innullence 0 = nullt highest nullsnull 1 = Highest nullsnull nullnullnull0.0null null nullnull0.0null

73Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice happened at places defined by their innullence from schoolsnulland null percent happened at places defined by their innullence from nullown gang membersnullresidences. nullain, nulllacesnullare cells sinulld nullnullfeet by nullnullfeet. Combining the four maps of the spatial innullence of criminogenic features into a single composite risnullterrain map shows how compounded risnullis distributed throughout nullvington and becomes a better predictor of shooting locations. null shown in column nullof enullibit null the compounded risnull of shootings gets larger in more confined areas of nullvington with each additional unit value of risnull nullen the four maps are combined with risnullterrain modeling, null percent nullnullout of nullnullof shootings happened at places that were affected by two or more criminogenic features nullhat is, risnull values were null null or null. Of the shootings, null percent nullnullout of nullnullhappened at places affected by three or more criminogenic features. Only null out of nullnullnullplaces nullellsnullhad risnullvalues of nulland were considered places where shootings could occur. null Of all shootings, null percent happened at these high-risnullplaces representing barely more than nullpercent of the area of nullvington. nullhibit null shows that places with risnullvalues of nullwere quite coalesced into relatively few clusters. Police com - manders and other community partners could prioritinull these areas nullollowed by areas with values of null null and null respectivelynullfor targeted evidence-based interventions to prevent shootings with a high level of confidence in the efficaciousness of their resource allocation strategy. null would appear from column nullof enullibit nullthat bus stops are the most important feature to affect crime because nullnull percent of all shootings happened nearby. nullany bus stops are located in nullrvington, however, and nearly one-half nullnullnull percentnull of the places in the township are cartographically modeled Exhibit null Snulltial nullnullence Manullonullnull nulllnulln 1null nullonull Shootinnull Present nulllnulln 2null nullonull nullvinnullonnulls nullea a

nulllnulln 3null Shootinnull nullonulltsnull nullr Snullare Mile b Relatinull Snulltial nullnullence of Cnullminogenic nullatunulles nulls stonull null null null.null nullnull, clunull, fastnulloonullnullestaunullnts, annulllinullonullstonulles null null null.null Schools null null null.null nullng memnullnullnullnullesinullnces null 11 1null.null Comnullsite nullsnullnulllue of null null onullnull null null null.null Comnullsite nullsnullnulllue of nullonullnull null 10.null 1null.null Comnullsite nullsnullnulllue of null 1null 1 null0.null a Calculated null tnull numnullr of cells nullving tnull attrinullte of tnull snulltial innullence of tnull criminogenic feature or tnull risk valuenullresnullctivelnull. nullr enullmnullenullnullnullnullcells nullre included in tnull statistical analnulles. Sonullif null cells nulld a risk value of nullnull tnulln tnull area nulluld null nullnullnullnull= 0.0null or nullnullnullrcent. null Square mile is nullased on tnulle numnuller of cells nullaving tnulle attrinullute of tnulle snullatial innulluence of tnulle criminogenic feature or tnull risk valuenullresnullctivelnull. Cells are null0 feet null null0 feetnullor nullnull00 square feet. Sonullif nullnullcells nulld a risk value of eitnullr nullor null tnulln nullnull cells nullnullnull00 square feet = nullnull0null00 square feetnullnullnullnullnull feet nuller mile null nullnullnullnull0 = nullnullnullnullnullnullnullnull00null = 0.nullnull square mile. nullnull snullootings in tnullese resnullective cells null 0.nullnull square mile = nullnullnull snullotings nullr square mile. null nullootings could be tracnulld only to street segments because the address level data were geocoded to street segments. null, the number nullnullnullrepresents the number of cells that intersect with a street centerline segment.

74 Crime and Urban Form Canullan as being located within the spatial innullence of risnullposed by the bus stops. This fact that nullore ground encompasses more shootingsnullrelates to the modifiable area unit problem and requires that relative shooting counts be calculated to control for the square area of featuresnull innulluences. nulls shown in column nullof enullibit null places innullenced by gang membersnullresidences actually have the most shooting incidents per area and could be a principal feature of the landscape that attracts shootings. To assert that places where gang members reside are definitely places where shootings will happen, however, is linull saying the presence of motivated offenders, suitable victims, and no capable guard - ians always results in crime nullhat is not truenullCohen and nulllson, nullnullnull nullat is more accurate, and what risnullterrain maps articulate visually, is that places affected by gang membersnullresidences and one or more additional criminogenic features have the greatest linulllihood of shootings to occur. null shown in column nullof enullibit null places innullenced by all four criminogenic features hosted meaningfully more shooting incidents per square area compared with all other places. nullveral criminogenic features may innullence smaller numbers of places, but as a whole, these places have a very high relative frequency of shootings. Operationalinullng the spatial innullence of criminogenic features to maps is an important tasnullfor manullminullng the construct validity of the effect these features have on crimes. Combining these maps with an nullnullgives such cartographic modeling practical utility for policing operations by articulating a continuous landscape of compounded risnull at all placesnullresulting maps can be used to efficiently allocate resources and target interventions. Evaluate Place-Based Interventions While Controlling for Environmental Context nullindsight is nullnullnullnullnull, as the saying goes. nullt this point, after analynulling old data, it may seem a bit unfair to say that interventions to suppress and prevent shootings could have been strategically targeted in nullvington to manullminull benefits. Police agencies operating in nullvington, however, did in fact have a targeted strategy to suppress and deter shootings. nullw with risnullterrain modeling and the understanding of the spatial innullence of criminogenic features, a spatial analytic method is used for evaluating the shooting suppression strategy. The township of nullvington is relatively small nullbout nullnullsquare milesnull sandwiched between a slightly bigger suburban township and the larger city of nullwarnull nulls murder rates for nullnull, however, were null.nullper nullnullnullnullpeople compared with the national average of nullnullfor similar sinulld cities across the country nullnull, nullnullnull Primarily for this reason, nullvington drew a considerable amount of attention from political leaders and law enforce - ment officials, who set up a special tasnullforce to assist the smaller municipal police force with policing this nullrisdiction. During nullnull, the tasnullforce consisted of uniformed state troopers who patrolled targeted areas in both a highly visible saturation capacity and in aggressive undercover operations. Drug arrests at these targeted areas were the tasnullforcenull principal intervention methodnull the purpose was to incapacitate linullly shooters and linullly victims nullecause shootings were mostly gang and drug relatednullby means of arrest, conviction, and incarceration. nullthough this strategy yielded an overall reduction of violence null in nullvington since before the tasnullforcesnullinception and the number of shootings has remained fairly constant since nullbout null biannuallynull its effect on the spatial distribution of new shootings remained unnullown. null nullcording to the nullw nullrsey nullate Police nulliform Crime nullports, the violent crime rate in nullnullnullefore the tasnullforcenull was null.nullper nullnullnull with a murder count of null. null nullnull, the violent crime rate was null.nullper nullnullnull with a murder count of null. The total number of violent crimes in nullnull was nullnullnulland in nullnull was nullnullnull

75Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice nullowing that certain criminogenic features attract and enable shootings above and beyond the routine activities of offenders, victims, or police, nullrvingtonnulls contenulltual landscape must be controlled for when evaluating geographically targeted interventions such as the tasnullforcenull aforementioned drug arrests. null validated nullTnull of nullrvingtonnulls finulled criminogenic contenullts is especially useful for such an evaluation. The risnullterrain map in enullibit nullwas nullnulldnullbecause it was produced from crimino - genic features that did not change during the year. nullr enullmple, the count and locations of school buildings did not change during nullnullnullnull. nulln nullrvington, nullprobably linulle many other nullurisdictionsnull, schools, bus stops, bars, and so on are not demolished or erected on a regular basis. Therefore, the afore- mentioned nullnullwith predictive validity nullnullibit null articulates the finulld criminogenic contenulls for shootings at microlevel places throughout nullvington. nullsnullterrain modeling has not yet been used to create a contenullual control measure of environmental crime risnull for the purpose of evaluating police interventions at places, but it could. The finulld contenull risnullterrain map can be used as a base mapnull bacnullloth, so to speanullnullrantingham and nullantingham, nullnullnullto evaluate the spatial effect of the tasnullforcenull targeted drug arrests during the first half of nullnull on subsequent shooting locations during the second half of nullnullnullnull. null reasonable time frame to measure this effect is null months because the nullew nullersey nulltate Police routinely report null-month crime trends to the public nullfor enullample, www.nnullp.orgnullnfonulltats.htmlnullit nulland revise policing operations and strategies accordingly. The first step to evaluate the spatial effect of targeted drug arrests on shooting incident locations is to operationalinull the intended spatial innullence of drug arrests and produce a respective map layer to be added to the finulled contenullt nullTnull. The basic information sought from evaluating this intervention is whether it was successful, defined by an overall reduction of shooting incidents without displace- ment or reemergence. This spatial evaluation seenull to answernullnullat effect did targeted drug arrests during nullnuary through nullne nullnull nulleriod null have on the locations of shooting incidents during nullly through December nullnull nulleriod nullnull nullhootings occurred in nullnull nullnown locations during Period null and nullnull locations during Period null, which was a slight reduction. null shown in enullibit null, the spatial distribution of shootings during the two time periods changed, with more incidents clustered in northeastern nullrvington during Period null than had occurred there during Period null null is hypothesinulld that places with higher concentrations of targeted drug arrests had a mitigating effect on the criminogenic contenullts at these places and, there - fore, shootings would have been less linullly to occur. Drug arrest locations were geocoded to street centerline shapefiles and then used to produce the density map in enullibit null, showing the places with density values greater than null standard deviations from the mean drug arrest density value throughout nullvington. These nullighest densitynullplaces were reclassified with a value of nullnulland all other places were assigned a value of nullnull.null Parameters for this map were similar to map layers already included in the finulld contenull risnullterrain map, described previouslynullnulllacesnullwere defined by a cell sinull of nullnullfeet and density was calculated with a search radius of nullnullnullfeet, about three blocnull. To test the hypothesis that places with higher drug arrest concentrations deterred future shootings, the drug-density map was subtracted from the finulld contenull risnullterrain map to produce a new risnull terrain map with values ranging from -nullto null. null shown in enullibit null, this new nullnullnullereafter called the nullypothesis nullnullnullis statistically significant, although not as much as the original finulld- contenull nullnull The nullgelnullrnull nullnulluare of the nullpothesis nullnullwas nullnullnull the finulld-contenull nullnull was nullnullnull The nullgelnullrnull nullnulluare provides a measure of how well future outcomes are linullly to

76 Crime and Urban Form Canullan Exhibit 11 nullnullngton, null: nullnuanullnullune null0null Highest nullnsity nullug nullnullest nullaces Exhibit 1null nullnullngton, null: nullannual Snulltial nullstnullnulltion of Shooting nullcinullnts nullnuanull to nullne null0nullshooting incinullnt nullly to nullcemnullnullnull0nullshooting incinullnt nullt highest Highest (onullnullnullnullSnull Densitnullvalnull SD = standard deviation.

77Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice be predicted by the model. nulllues of nullgelnullrnull nullnulluare mean that the nullpothesis nullnullonly enullplains about null percent of the variance in future shooting locations compared with about null percent enulllained by the finulld-contenull nullnull nullter further renullction, it is apparent that comparing the nullpothesis nullnullto the finulld-contenull nullnullis linull comparing apples to oranges. null with any other statistical tests, one must consider only the results of the hypothesinulld model against the alternative, or null, hypothesis. The null hypothesis for the tasnullforcenull intervention is that places with higher drug arrest concentrations attract future shootings. To adnullst to this null hypothesis, the drug-density map was added to the original finulld-contenull risnullterrain map to create a nullull nullpothesis nullnull with values ranging from nullto null null shown in enullibit null, the nullll nullpothesis nullnullhas statistically significant predictive validity, with a nullgelnullrnull nullnulluare value of nullnullnull Compared with the nullpothesis nullnull the nullll nullpothesis nullnullenulllains the variance of shooting locations much better. Targeted drug arrests at certain areas appear to have actually attracted new shooting incidents to these same places. This result seems counterintuitive and many enulllanations are linullly for this happenstance. nullthodologically, a limitation of the nullnullis that it does not ac - count for very small increments of time, so it is possible that shootings were deterred when police were in an area, but then ensued shortly after they left. Or perhaps many targeted drug arrests created new open turf that other drug dealers fought to control. Targeted drug arrests may simply not be an appropriate response to gun shooting crimes. nullcall that the nullnullwith the best predic - tive validity and most enulllained variance of future shooting locations was the finulld contenull nullnull which did not include any risnullfactors directly related to drug arrests. nullt police sought to suppress and deter shootings with targeted drug arrests. nullmore effective intervention might have been to use evidence-based practices to mitigate one or more of the criminogenic risnullfactors that were included in the finulld contenull nullnull thereby reducing the availability of places that are conducive to shootings. nullch an intervention might include increasing shootersnullapprehension risnull through strategic interventions at places within one blocnullfrom bus stops, bars, clubs, fast-food restaurants, and liquor storesnullincreasing police patrols along travel routes to and from schools, especially within the immediate three-blocnullareasnullor tanullng civil actions to shut down the most historically problematic barsnulliving priority to establishments within places having risnullvalues of null Exhibit 12 RisnullValnull null Snullnull nullald Sinull Exnullnull nullnullnullnull nullr Exnullnull nullnullr nullnullr Logistic Regnullessions fonullnullnullonull1 Hynullthesis Rnull on nullnullonullnullShootings Hypothesis RTM: nullug anullests as mitigating factonull a 0.nullnull 0.nullnull nullnullnull 0.00null 1.nullnull 1.nullnull nullnullnull Null hypothesis RTM: nullug anullests as aggnullnullting factonull null 0.nullnull 0.1null null.nullnull null0.001 null0null 1.nullnull nullnullnull B = Beta. C.I. = Confidence Interval. df = degrees of freedom. S.E. = standard error. Sig. = significance. Wald = Wald coefficient. a Nagelkerke R square = 0.0nullnulldf = null null Nagelkerke R square = 0.0nullnulldf = null

78 Crime and Urban Form Canullan nullcase was presented for measuring and mapping criminogenic features not as points, lines, or polygons, but as qualities of space throughout a landscape. Thinnullng about crime correlates not as finite obnullcts, but rather as centers of radiating criminogenic innullence across the urban landscape enables cartographically modeling environments in terms of microlevel place-based risnull that are more enduring than nullst the characteristics of the people who frequent these places. Cartographic models of spatial innulluence are consistent with ideas that were popular among ecologists nullfor enullample, nulllland, nullnullnull repeated by environmental criminologists when nullantingham and nullantingham nullnullnull talnulld about environmental bacnullloths, enullressed in the nully elements of problem-oriented policing nullenter for Problem-Oriented Policing, nullnullnull and are now appearing in terms of risnull terrains or opportunity structures nullaplan, nullnnedy, and nullller, nullnullnullnulloff and null nullgne, nullnullnull nulldividual risnullfactors are important, such as those owned by motivated offenders or potential victims nullohen and nulllson, nullnullnull but environmental contenulls relative to certain criminogenic features are also very important in assessing crime-prone places. nullny places probably abound daily with motivated offenders, suitable victims, and no capable guardians, but crimes do not always occur there. nullynullnullcause these elements must simultaneously enullst at enabling places to yield criminal events. null few researchers have connected environmental factors to crime through simulation models, includ- ing recent wornullby nullantingham and Tita nullnullnull, Tita and nulliffiths nullnullnull, and nulloff nullnullnull, bnull suggesting that the criminal nullstice community may be closer in its ability to show not nullst that certain factors are important in creating criminogenesis, but also to enullplain how these factors interact to produce the crime outcomes that appear. This article advances these efforts to measure envi- ronmental contenull as an element of criminal events by proposing a technique for operationalinullng crime risnullfactors in cartographic form so that their otherwise intangible innullences on places can be statistically tested, used to better inform resource allocation, and controlled for when evaluating place-based interventions. Three operationalinulltions of spatial innullence of criminogenic features within a landscape were presentednullpresence of features, concentration of features, and distance from features. nulll are shaped by the urban form or physical layout and design of a city, which is the structural setting for people to interact and commit crimes. The most appropriate operational- inulltion can differ by crime type and research setting, but it should always be derived from theory and enullert nullowledge. Cartographically modeling the linulllihood of crime at all places throughout a landscape according to the spatial innullences that certain criminogenic features have on those places is a more realistic depiction of criminal opportunity nullouclelis, nullnullnullnullannulland nullrnull nullnullnullnulleundschuh and nullenhofer, nullnullnull Operationalinullng the spatial innullence of criminogenic features considers crimi - nal behaviors as less deterministic and more as a function of a dynamic interaction that occurs at places. nullalities of space themselves do not create crimenullthey simply point to locations where, if the conditions are right, the risnullof crime or victiminulltion will go up. null cannot be assumed, although, that because one or more criminogenic features affect a location that crime will definitely happen. nullat is more linullly is that the linulllihood of crime at places that have several criminogenic attributes is higher than at other places because these locations attract motivated offenders nullr more linullly concentrate them in close locationsnulland are conducive to allowing certain events to occur. nullthough physical structure and public activities can have an effect on the ways in which crime occurs nullasta, nullchmond, and nullebe, nullnullnullnulloff nullnulla, bnull it has been difficult to show

79Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice empirically how the connection wornull because of data problems and the complenullty of the issue. nullasuring and modeling criminogenic features according to their spatial innullences addresses the empirical difficulty. Operationalinullng spatial innullence also lends itself to multilevel modeling in nullnullwith techniques such as risnullterrain modeling, which models the combined innullences of criminogenic features at places. nullen risnullterrain modeling is performed with finulld criminogenic features, it can serve as an environmental control measure for evaluating spatial effects from place- based interventions. The allure of mapping spatial innullences of criminogenic features is that doing so can closely tie information to strategic planning. null addition, it provides a means by which police and other com - munity leaders can evaluate interventions. null comports with the idea that the public has annulleties that translate into demands for prevention strategies to reduce crime risnull nullnd crime fearsnull null also addresses the idea that certain areas can be more dangerous than other areas, even if criminogenic features are not present, and, therefore, that those places nullstifiably demand greater attention. Combined with risnullterrain modeling, it also provides an evaluation approach that can determine a programnull effectiveness and efficacy, or wastefulness of certain resource types used. nulltnullr nullel null Caplan is an assistant professor in the nullhool of Criminal nullstice at nulltgers nulliversity and associate director of the nulltgers Center on Public nullcurity. References nullbott, nulldrew. nullnull. nullf Time and nullacenullThe Contemporary nulllevance of the Chicago nullhool,null Social nullrces nullnullnullnullnullnullnull nulldrews, Don null nullnull. nulle-nullrest null Predictable and Can null nullnullencednullnulling nullsnullnullsessments To nullduce null-nullrest,null nullrum on Corrections Researcnull null nullnullnull nullsta, nullnull null, Therese null nullchmond, and Douglas null nullebe. nullnull. nulleighborhoods, Daily nulltivities, and nullasuring nullalth nullsnull nullperienced in nullban nullvironments,null Social Science annull nullnullcine nullnullnullnullnullnullnull nullocnull nullchard, and Carolyn nullocnull nullnull. nullpace, Place, and Crimenullnulltspot nulleas and nullt Places of nullquor nulllated Crime.nullnull Crime annullnullacenullCrime nullevention Stunulles , nulll. null edited by nullhn null nullnulland David nullisburd. nullnsey, nullnullCriminal nullstice Pressnullnullnullnullnull nullantingham, Paul null, and Patricia null nullantingham. nullnull. nullriminality of PlacenullCrime nullnerators and Crime nulltractors,null European nullurnal on Criminal nulllicy annullResearcnull null nullnull. nullnull nullnull. Environmental Criminology. nullverly nulllls, Cnull nullge Publications. nullantingham, Paul null, and nullorge Tita. nullnull. nullffender nullbility and Crime Pattern nullrmation nullom nullrst Principles.nullnull nulltinullial Crime nullalysis Systemsnullnulling Computer Simulations annull nullograpnullc nullnullrmation Systems , edited by nulln nullu and nullhn nullnull nullrshey, Pnull nullea Pressnullnullnullnullnull

80 Crime and Urban Form Canullan nullrgess, nullnest null nullnull. nullactors Determining nullccess or nullilure on Parole.nullnull nulle nullrnullngs onull tnull nullnullterminate Sentence nullw annulltnull nullrole System in nulllinois , edited by nulldrew null nulluce, nullnest null nullrgess, and nullbert null nullrno. nullringfield, nullnullnulllinois nullate nullard of Parolenullnullnullnullnull Caplan, nullel null, and nullslie null nullnnedy. nullnull. Risnullnullrrain nullnullling nullnualnullnulleoretical nullamewornullannullnullcnullical Steps onullSpatial Risnullnullsessment . nullwarnull nullnullnulltgers Center on Public nullcurity. Caplan, nullel null, nullslie null nullnnedy, and nullel nullller. nullnull. nullisnullTerrain nulldelingnullnullonullring Criminological Theory and nullnullnullthods for Crime nullrecasting,null nullstice nullarterly null nullnull nullnullnullnull Center for Problem-Oriented Policing. nullnull. nullhe nully nullements of Problem-Oriented Policing.null httpnullnullww.popcenter.orgnullboutnullpnulllements nullccessed nullvember nullnull Clarnull, nullnald, and nullhn nullnull nullnull. Crime nullalysis nullr nulloblem Solvers in null Small Stepsnull nullshington, DCnullnullnull Department of nullstice, Office of Community Oriented Policing nullrvices. Clarnull, nullnald null, and nullrcus nulllson. nullnull. Routine nulltivity annullRational Cnullice, Volnullnull nullvances in Criminology nulleory . nullw nullunswicnull nullnullTransaction. Cohen, nullwrence null, and nullrcus nulllson. nullnull. nullocial Change and Crime nullte Trendsnullnullnullutine nulltivity nullproach,null nullerican Sociological Review nullnullnullnullnullnull Cohen, nullwrence null, nullmes nulluegel, and nullnneth nullnd. nullnull. nullocial nullequality and Predatory Criminal nullctiminulltionnullnull nullposition and Test of nullrmal Theory,null nullerican Sociological Review nullnull nullnullnullnull Couclelis, nulllen. nullnull. nulleople nullnipulate Obnullcts nullut Cultivate nulleldsnull nullyond the nullster- nullctor Debate in nullnullnullnull nulleories annullnulltnullnull onullSpationullemporal Reasoning in nullograpnullc Spacenull nullcture Notes in Computer Science nullnull , edited by nulldrew null nullannull nullene Campari, and nullaldo nullrmentini. nullw nullrnull nullringer-nullrlagnullnullnullnull nullnull nullhn. nullnull. Drug nullrnullts and Drug PlacesnullnullCase-Controlled nulludy of nullatial nullructure of nulllicit Dealing. nullpublished Ph.D. diss. nulliversity of nullryland, College Parnull nullnull nullhn, nullnald Clarnull, and nullb nullerette. nullnull. nullicnull nullcilitiesnullCrime Concentration in nullmogeneous nullts of nulltablishments and nullcilities,null Crime nullevention Stunulles nullnullnullnullnullnull nullnull nullhn null nullnull. nullreventing Crime at Places.nullnull EvinullncenullasenullCrime nullevention , edited by nullwrence null nullerman, David P. nullrrington, nullandon C. nulllsh, and Doris null nullcnullnnulle. nullw nullrnull nullutledgenullnullnullnullnull nullnull nullnull. nullolicing and Crime nullent Concentration.nullnull nulle nullocess annullStructure onullCrimenull Criminal Events annullCrime nullalysis , edited by nullbert null nullier, nullslie null nullnnedy, and nullncent null nullcco. nullw nullunswicnull nullnullTransactionsnullnullnullnullnull nullnull nullhn null, nullencer Chainey, nullmes null Cameron, nullchael nullitner, and nullnald null nulllson. nullnull. nullpping Crimenullnullnullrstannullng nullt Spots . nullshington, DCnullnulltional nullstitute of nullstice.

81Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice nullenhofer, nullnull and David nullrnull nullnull. nullanulle nullography.nullnull Spatial nullnullrmation nulleorynull nullnulleoretical Basis nullr nullS, nullternational Connullrence Cnullnull nullnull Semmering, nullstrianullnullcture Notes in Computer Science nullnull , edited by nulldrew null nullannulland nullrner nullhn. nullw nullrnull nullringer-nullrlagnull nullnull. nullderal nullreau of nullvestigation nullnullnull nullnull. Crime in tnull nullitenullStates 200null nullshington, DCnullnullnull Department of nullstice. nullannull nulldrew. nullnull. nullhe nulle of nullographical nullformation nullstemsnullThe nuller nullterface null the nullstem.nullnull nullman nullctors in nullograpnullc nullnullrmation Systems , edited by David nulldycnullnullnullott and nulllary null nullarnshaw null nullndon, nullited nullngdomnullnulllhaven Pressnullnullnull. nullannull nulldrew, and David nullrnull nullnull. nullanguage nullsues for nullnullnullnull nullograpnullc nullnullrmation Systemsnullnullinciples , edited by David nullguire, nullchael null. nullodchild, and David nullind. nullndon, nullited nullngdomnullnullngmannullnullnullnullnull nulleundschuh, nullott null, and nullnullnull nullenhofer. nullnull. nulluman Conceptions of nullacesnullnullplications for nullographic nullformation nullstems,null nullansactions in nullS nullnullnull nullnullnullnull nulluecnull nulleldon, and nulleanor nulluecnull nullnull. nullraveling nullvenile nulllinnullencynull nullw nullrnull Commonwealth. nullttfredson, nullephen D., and nullura null nullriarty. nullnull. nulltatistical nullsnullnullsessmentnullOld Problems and nullw nullplications,null Crime annullnulllinnullency nullnullnullnullnullnull nulloff, nullinullbeth null nullnulla. nullimulation for Theory Testing and nullperimentationnullnull nullample nulling nullutine nulltivity Theory and nullreet nullbbery,null nullurnal onullnullantitative Criminology nullnullnullnullnull. nullnull nullnullb. nullnulltuatingnullnullmulation to nulldel nullman nullatio-Temporal nullteractionsnullnull nullample nulling Crime nullents,null nullansactions in nullS null nullnull nullnullnullnull nulloff, nullinullbeth null, and nullncy null null nullgne. nullnull. nullorecasting the nullture of Predictive Crime nullpping,null Crime nullevention Stunulles nullnullnullnullnull nullrries , nullith. nullnull null nullpping Crime null nullinciple annullnullactice . nullshington, DCnullnullnull Department of nullstice, Office of nullstice Programs. nulllland, nullhn null nullnull. Emergencenullnullom Cnullos to nullnullrnull Cambridge, nullnullPerseus. nullnnedy, David, nullne Piehl, and nullthony nullaga. nullnull. nullouth nullolence in nullstonnullnulln nullrnullts, nullerious nullouth Offenders, and a nullse-nulleduction nulltrategy,null nullaw annull Contemporary nullroblems nullnullnull nullnullnullnullnullnullnull. nullnnedy, nullslie null, nullel null Caplan, and nullic Pinull. nullnull. nullisnullClusters, nulltspots, and nullatial nulltelligencenullnullsnullTerrain nulldeling as an nullgorithm for Police nullsource nulllocation nullrategies,null nullurnal onullnullantitative Criminology 2null nullnullnullnullnull null nullnnedy, nullslie null, and nullin null nulln nullunschot. nullnull. nulle Risnullin Crimenull nullnham, nullnullnullwman nullnullttlefield. nulle, nulltthew null, and nulldullah nullshalan. nullnull. nulleographic nullriation in Property Crime nulltesnull nullTest of Opportunity Theory,null nullurnal onullCrime annullnullstice nullnullnullnullnullnull

Unknown

82 Crime and Urban Form Canullan nullunullitou-nullderis, nullastasia. nullnull. nullot nullots of nulls nullop CrimenullThe nullportance of nullvironmental nulltributes,null nullurnal onulltnull nullerican nullanning nullsociation null nullnull nullnullnullnull nullarnull, David. nullnullnullnull. nullnulluman nullpatial Cognition.null nulln nulluman nullactors in nulleograpnullical nullnnullormation Systems, edited by David nulldycnullnullnullott and nulllary null nullarnshaw. nullndon, nullited nullngdomnull nulllhaven Pressnullnullnullnull nullstrofsnull, nullephen D., David nullisburd, and nullthony null nullaga. nullnull. nullethinnullng PolicingnullThe Policy nullplications of nullt nullots of Crime.nullnull Contemporary nullsues in Criminal nullstice nulllicynullnulllicy nulloposals nullom tnull nullerican Society onullCriminology Connullrence , edited by nulltasha null nullost, nullshua D. nulleilich, and Todd null Clear. nulllmont, Cnull nulldsworthnullnullnullnullnull nullars, Daniel P., nullchelle null nullott, and nullinash null nullati. nullnull. nullpportunity Theory and nullricultural Crime nullctiminulltion,null Rural Sociology nullnullnullnullnullnull nullntello, Daniel. nullnull. nullcale and nullltiple Psychologies of nullace.nullnull Spatial nullnullrmation nulleorynull nullnulleoretical Basis nullr nullSnullnullcture Notes in Computer Science nullnull , edited by nullephen C. nullrtle and nulldrew null nullannull null nullw nullrnull nullringer-nullrlagnullnullnullnullnull nullewman, Oscar. nullnullnullnull. nullenullensible Spacenull Crime nullrevention nullnullrougnull nullrban nullesign. nullew nullornullnull nullacmillan. Poyner, nullarry. nullnullnullnull. Crimenullnullree nullousing in tnulle 21st Centurynull nullondon, nullnited nullingdomnull nullroonull nullouse. nullines, nullry null, Don null nullwatnully, and nullaeme null nullnham-Carter. nullnull. nullncorporating nullpert nullowledgenullnullw nullnully nullgic Tools in nullcnullnullnull,null nullcnuller nullringnull nullnull. httpnullnullww.esri.comnull newsnullrcusernullnullnullfilesnullunullylogic.pdf nullccessed nullvember nullnull nulltcliffe, nullrry. nullnull. null Temporal Constraint Theory To nullplain Opportunity-nullsed nullatial Offending Patterns,null nullurnal onullResearcnullin Crime annullnulllinnullency null nullnull nullnullnullnull nullncenull Dennis null, and Donald nullggiani. nullnull. nulligh nullhools and Crime,null Sociological nullarterly nullnullnullnullnullnull nullncenull Dennis null, and nullffrey nullnull nullancinull nullnull. nullousing Pronullcts and Crime,null Social nulloblems nullnullnullnullnullnull nullncenull Dennis null, and nulltionette nullbosco. nullnull. nullhe nullfect of nullgh nullhools on Crime in Their nullighborhoods,null Social Science nullarterly nullnullnullnullnullnull nullbin, nullel. nullnull. nulltopping Crime nullfore null nullarts,null nulls nullgeles nullmes , nullgust null. nullhaw, Clifford, and nullarvey nullcnullay. nullnullnullnull. nulluvenile nullelinnulluency annull nullrban nullreas. Chicagonull nullniversity of Chicago Press. nullerman, nullwrence. nullnull. nullotspots of Crime and Criminal Careers of Places.nullnull Crime annullnullacenull Crime nullevention Stunulles, nulll. null edited by nullhn null nullnulland David nullisburd null nullnsey, nullnullCriminal nullstice Pressnullnullnullnull nullerman, nullwrence null, Patricnullnull nullrtin, and nullchael null nullerger. nullnull. nullot nullots of Predatory Crimenullnullutine nulltivities and the Criminology of Place,null Criminology nullnullnullnullnull

83Cityscape Manulling tnull Snulltial Innullence of Crinull CorrelatesnullnullConullarison of Onullrationalinulltion Scnullnulls and Inulllications for Crinull nullalysis and Crinullnal nullstice nullactice nullmon, nullta. nullnull. nullmen annullCrimenull nullnullngton, nullnullnullnullngton nullonull. nullith, nulllliam null, nullaron null nullanulle, and nullinullbeth null Davison. nullnull. nullurthering the nulltegration of nullutine nulltivity and nullcial Disorganinulltion Theoriesnullnullall nullits of nullalysis and the nulludy of nullreet nullbbery null a Diffusion Process,null Criminology nullnullnullnullnullnull Tilley, nullcnull nullnathan nullith, nullephen nullner, nullsie nullol, Corrine Charles, and nullhn Dobby. nullnull. nulloblem Solving Street Crimenullnullactical nullssons nullom tnull Street Crime nullitiativenull nullndon, nullited nullngdomnullnullme Office nullsearch, Development and nullatistics Directorate. Tita, nullorge, and nullinullbeth nulliffiths. nullnull. nullraveling to nullolencenullThe Case for a nullbility-nullsed nullatial Typology of nullmicide,null nullurnal onullResearcnullin Crime nulllinnullency nullnull nullnullnullnull Tomlin, C. Dana. nullnull. nullap nullgebranullOne Perspective,null nullnnullcape annullnullban nullanning nullnullnullnull. nullnull nullnull. nullartographic nulldeling.nullnull nullograpnullcal nullnullrmation Systemsnullnullinciples annull nullplications, edited by David null nullguire, nullchael null nullodchild, and David null nullind. nullrlow, nullited nullngdomnullnullngmannullnullnullnullnull Topalli, nulllnulln, nullchard nullight, and nullbert nullrnango. nullnull. nullrug Dealers, nullbbery, and nulltaliationnullnulllnerability, Deterrence, and the Contagion of nullolence,null Britisnullnullurnal onullCriminology nullnullnullnullnullnull nullnull Department of nullusing and nullban Development nullnullnull nullnull. null tnull Crossnullenullnulle nullpact onullnulln Violence on nullblic nullusing Communities . nullshington, DCnullnullnull Department of nullusing and nullban Development. nullisburd, David. nullnull. nulllace-nullsed Policing.null nulleas in nulllicing Series . nullshington, DCnullPolice nullundation. nullisburd, David, and nullthony null nullaga. nullnull. nullot nullots Policing null a nulldel for Police nullnova - tion.null nulln nullolice nullnnovationnull Contrasting nullerspectives, edited by David nulleisburd and nullnthony null. nullraga. nullw nullrnull Cambridge nulliversity Pressnullnullnullnullnull nulleisburd, David, nulltephen nullastrofsnulli, nullnn nullarie nullcnullally, and nullosann nullreenspan. nullnullnullnull. Compstat annull nullrganinullational Cnullangenull nullinnullings nullrom a National Survey. nullashington, DCnull Police nulloundation. nullight, nullchard T., and nullott null Decnullr. nullnull. nullmenullRobbers in nulltionnullSticnullps annullStreet Culture . nullstonnullnullrtheastern nulliversity Press. null, nulle, nullslie null nullnnedy, and nullel null Caplan. nullnull. nullarching for Conditional nullcational nullter - dependence nullnullnullof Crime Concentrationnullnulltending nullsnullTerrain nulldeling of nulln nullootings in an nullban nullvironment. nullpublished manuscript. nullditional Reading nullhnson, nullane D., nullte null nullwers, Dan nullrnull, and nulln Pease. nullnull. nullredictive nullpping of Crime by Pronullpnullnullcuracy, nullits of nullalysis, and the nullvironmental nullcnullloth. null nulltting Crime in nulls nullacenullnullits onullnullalysis in nullograpnullc Criminology , edited by David nullisburd, nullm nullrnasco, and nullrben nulluinsma. nullw nullrnull nullringernullnullnullnullnull

84 Crime and Urban Form

85Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape HOPE VI Resident Displacement: Using HOPE VI Program Goals To Evaluate Neighborhood Outcomes RodericnullW. nullnes nulldiana nulliversity nullrenullnull nullulsen nullstern nullntucnull nulliversity nullstract nullis stunull evaluates tnull neignullornullonull selectenullby nullnull Vnullresinullnts in nullnullngton, nullntucnull, nullllowing tnull nullmolition onulltnull Cnullrlotte Court nullusing pronullctnullnullnull Vnullis a public policy program nullsignenullto anullress problems associatenullwitnullseverely nullstressenull public nullusingnullnullcentral routine onullnullnull Vnullis nullmolisnullng nullusing pronullcts annull renullveloping tnull original site into new minullnulluse nullusingnullnullmolition nullsplaces a large percentage onullresinullnts to otnullr neignullornullonull, using Section nullvoucnullrsnullnull tnulls stunull, using tnull goals onullnullnull Vnullas a conceptual nullamewornull we nullvelopenullmeasures to nulltermine inullnullnull Vnullresinullnts improvenulltnullir circumstances at tnull neignullornullonulllevel nullllowing tnullir nullsplacementnullnulle nullunnullno signinullant nullnullnullrence between tnull original Cnullrlotte Court neignullornullonullannulltnull new neignullornullonull tnullt resinullnts selectenull nulling negative binomial regression, tnull results onulltnull stunull snullw tnullt crime annulltnull percent onulltnull population tnullt is nullrican nullerican are signinullant prenullctors onulltnull number onull nullsplacenullnullusenulllnull witnulln a neignullornullonullselectenullby nullsplacenullresinullntsnullnulle stunull conclunulls witnulla nullscussion onullpolicy implications annullan onullnullring onullpotential solutions to tnull many problems associatenullwitnullnullnull Vnullresinullnt nullsplacementnull

86 Crime and Urban Form nullnes and nullulsen Introduction nullOPnull nullnull is a public policy program designed to address problems associated with severely distressed public housing. nullcentral nullPnullnull routine is the demolition of enullsting housing pronullcts and re - development of new minulld-use housing on the original site. nullsearch on nullPnullnull has shown that demolition displaces a large percentage of residents to other neighborhoods through the use of nullection null vouchers nullnullbt, nullnullnullnullnull Comey, nullnullnullnullnull nulloetnull, nullnullnullnullnull nullingsley, nullohnson, and Pettit, nullnullnull Popnulln et al., nullnullnull nullny nullPnullnull residents report that their new neighborhoods are improve - mentsnullhowever, many also report that their quality of life is only moderately enhanced nullbt, nullnullnull nulletnull nullnullnullPopnulln et al., nullnullnull nullPnullnull obnullctives center on improving housing quality on the original site and decreasing the concentration of crime, poverty, unemployment, and racial segrega- tion nullnullnullD nullnullnullnullnull Popnullin et al., nullnullnullnullnull Popnullin et al., nullnullnullnullnull. nullOPnull nullnull research has yielded disparate findings in study sites across the nation. This article adds to the nullPnullnull literature by enullmining the nullPnullnull redevelopment of the Charlotte Court housing pronullct in nullnullngton, nullntucnull. nullngsley, nullhnson, and Pettit nullnullnull identified nullnullngton as having among the most concentrated distribution of displaced residents when compared with null redevelopment pronullcts in null cities. This study is designed to gauge the efficacy of nullPnullnull in nullnullngton by evaluating measures that are consistent with goals and obnullctives identified from the nullnull Department of nullusing and nullban Development nullnullnulldocumentation and past studies evaluating nullPnullnull. The study enullmined whether nullPnullnull residents selected their new home because it offered an opportunity to improve their lives, or because it was the nenull best available housing. nullnully of any enullmination of nullPnullnull is that nearly any location outside of the most severely distressed public housing is linullly to be an improvement for the residents nullbt, nullnullnull Determining the efficacy of nullPnullnull is difficult, because a clear set of measures for program outcomes is neither clearly stated, nor openly available. The study therefore identifies several factors believed to be adequate measures of nullPnullnull goals and obnullctives. One hypothesis is that neighborhoods that nullPnullnull residents selected in nullnullngton were better in overall neighborhood conditions such as crime, pov - erty, unemployment, and racial segregation. nullsecond hypothesis is that, despite the improvement, the number of relocated residents in a neighborhood is significantly associated with measures of reported crime, poverty, unemployment, and racial segregation. null these hypotheses are supported, some weight can be given to the argument that nullPnullnull has qualitatively achieved its goals, but simultaneously failed, because new neighborhoods are still significantly associated with the meas- ures identified as problematic in the nullPnullnull literature. HOPE VI Goals and Objectives The goals and obnullctives of nullPnullnull have been notoriously opaque. The nulltional nullusing nullw Pronullct nullnullnull mentioned that one nully problem with nullPnullnull is its uncertain obnullctives. The goals and obnullctives from past studies associated with neighborhood quality generally pertain to improving the quality of public housingnulldecreasing the concentration of crime, poverty, unem - ployment, and racial segregationnulland building local partnerships with community organinulltions

87Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls nullnull, nullnullnullnullnuller, nullnullnull The goals are unlinullly to capture the full range of initiatives set forth by nullPnullnullnullhowever, the goals do represent nully factors associated with the social and economic improvements enullected for nullPnullnull residents. The first identified nullOPnull nullnull goal is to improve living conditions of severely distressed public housing through demolition and renewal pronullcts nullilderbloom, nullnullnullnullD, nullnullnullPopnulln et al., nullnullnull The construction and design methods vary across the nation, where some cities have rebuilt public housing on old sites, and others have used the old public housing sites as land for building new minulld-use or affordable housing. null many instances the demolition of public housing sites has left many former residents with few housing options. null a result, many residents turned to nullction null vouchers or were forced to move to other housing pronullcts nulluron et al., nullnullnull The present study reveals similar relocation patterns as residents either moved to a different public housing location or to private apartment housing in a different neighborhood. The second goal of nullPnullnull is to revitalinull communities in an effort to reduce the concentration of poverty. This effort often includes limiting the capacity of the public housing rebuilt at the original site and, subsequently, relocating residents to various neighborhoods through nullction null vouchers nullopnulln et al., nullnullnullnullnuller, nullnullnull nullericanull housing pronullcts are home to the most economically and socially disadvantaged population nullchill and nullchter, nullnullnull nullgh-poverty neighborhoods have multiple layers of problems, ranging from health-related issues to high rates of homicide. nullcause of these problems, nullPnullnull has aimed to reduce the spatial concentration of poverty by dispersing residents to areas with lower poverty levels. null this goal is achieved, new neighborhoods will have significantly lower poverty levels. The third goal of nullPnullnull is to create opportunities for residents to become self-sufficient through the use of services that provide various types of nullob training and employment. These services include computer training, daycare, after-school programs, and nullb referrals nullopnulln et al., nullnullnull null this goal is achieved, neighborhoods selected by displaced residents would not have high unemployment levels. nullen new neighborhoods selected by displaced residents have insignificant unemployment levels, available nullobs, and nullob training, nullOPnull nullnull can be considered successful in moving residents to neighborhoods that, at the very least, enullose residents to more employment opportunities. The fourth goal of nullPnullnull is to reduce public housing residentsnullenullosure to incidents of crime nullilderbloom, nullnullnullnullD, nullnullnullPopnulln et al., nullnullnull nullveral studies identified problems with crime as a nully concern of many residents nullopnulln et al., nullnullnullnullith, nullnullnull null is well nullown that housing pronullcts have become havens for drug dealing, robbery, and homicidenulla central focus of nullPnullnull was to eliminate these problems from the lives of public housing residents. null this goal is achieved, the new neighborhood that a displaced resident selected should have lower crime levels than the old neighborhood. nullfinal goal of nullPnullnull identified in the literature is to reduce the amount of racial segregation. nullusing pronullcts in the nullited nullates traditionally have high rates of nullrican-nullerican residents nullroonull et al., nullnullnullnullscher, nullnullnullnullngsley, nullhnson, and Pettit, nullnullnull nullndenhall, Denullca, and Duncan nullnullnull found that women who were relocated to neighborhoods with lower percentages of nullrican nullericans were able to find and hold on to nullbs for a longer period of time and were less

88 Crime and Urban Form nullnes and nullulsen linullly to accept welfare benefits. Peterson and nullivo nullnullnull found that racial segregation leads to higher levels of homicide among blacnullresidents. null this goal is achieved, the new neighborhoods that displaced residents select should have low levels of nullrican-nullerican population. The goals of nullPnullnull are important, because they identify obnullctives the program is designed to achieve. nullghlighting program obnullctives provides a way of measuring the efficacy of the programnull outcomes. This study uses nullPnullnull goals as a conceptual framewornullfor selecting enulllanatory variables used in the final analysis. The goals identify several nully themes used to study the overall effect that nullPnullnull has had on displaced residentsnullliving conditions. Outcomes for HOPE VI Residents nullfairly consistent finding of nullPnullnull research is that residents tend to move to locations with substantially fewer problems when compared with the original housing site nulloetnull nullnullnull nulludies have also indicated, however, that outcomes for residents are generally minulld nullational nullusing nullw Pronullct, nullnullnull nullnully finding in this study is that, despite the many improvements observed in past studies, the residents affected by nullPnullnull continue to struggle in their everyday lives. The findings presented in the following sections indicate that new neighborhoods have only slight improvements over the original nullPnullnull neighborhood. nullsults also indicate, however, that the new neighborhoods may still be areas that are troubled by crime, poverty, unemployment, and racial segregation. nullmittedly, other outcomes for neighborhoods, aside from crime, poverty, unemployment, and racial segregation, have been identified in the literature. nullr enullmple, nulletnull nullnullnull identified other problem areas such as childrennull school and social enullerience, health and behavior outcomes, housing neighborhood characteristics, and social networnull. Currently, no enullsting reliable measures of these other factors can be analynulld at the census blocnullgroup level. Despite this limitation, poverty, crime, unemployment, and racial segregation are enullellent pronulles for the goals previously outlined. Crime nullny studies on nullPnullnull have shown that a nully concern of nullPnullnull residents is their enullosure to crime nullnullarbrey, nullnullnullnullnull nulluron et al., nullnullnullnullnull nullilderbloom, nullnullnullnullnull Popnullin, nullnullnullnullnull Popnullin et al., nullnullnullnullnull. Popnullin nullnullnullnullnullnull found that residents of inner city public housing complenulles are linullely to be offenders and victims of violent crime. nullron et al. nullnullnull reported that many nullPnullnull residents wanted their new neighborhoods to have less drug trafficnullng and violent crime. nullsearch on nullPnullnull has generally found that residents have reported increases in perceived safety levels within their new neighborhoods nulluron, nullnullnullnullron et al., nullnullnullnulletnull nullnullnullPopnulln and Cove, nullnullnullPopnulln et al., nullnullnull nulldwig, Duncan, and nullrschfield nullnullnull found that moving out of public housing reduces the risnullfor children to engage in nullvenile crime. nullst studies that enulllore crime in nullPnull null neighborhoods have used survey measures to determine how residents perceive safety in their new neighborhood. nullw studies have enullmined the association between displaced nullPnullnull resi - dents and neighborhood crime levels. One enulleption to this gap in the literature is nulllderbloomnull nullnullnull study of a nullPnullnull redevelopment in nullwport, nullntucnull. null found a null-percent drop in crime at the original site over a nullyear period. This finding was enullected because of the large drop

89Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls in population through resident displacement. nulllderbloom did not describe crime levels in the displaced residentsnullnew neighborhoods. nullrbrey nullnullnull also evaluated crime at the original public housing location in nullonullille, Tennessee, but found that nullPnullnull had little effect on overall crime levels, suggesting that nullPnullnull did not substantially improve safety levels for residents. Poverty nulltudies show that poverty levels in new neighborhoods are lower than in the original neighborhood nulloston, nullnullnullnullron, nullvy, and nullllagher, nullnullnullnullron et al., nullnullnullClampet-nullndquist, nullnullnull nullscher, nullnullnullnulletnull nullnullnullnullngsley, nullhnson and Pettit, nullnullnullPopnulln et al., nullnullnull nullron et al. nullnullnull showed that nearly null percent of relocated residents moved to areas that were classified as low poverty. The same study also showed, however, that null percent of residents moved to high- poverty areas. nullngsley, nullhnson, and Pettit nullnullnull showed that residents who used nullction null vouchers moved from neighborhoods with nullnull-percent poverty rates to neighborhoods with average poverty rates of nullnull percent. Despite the improvement in overall neighborhood economic circumstances, research has shown that many residents are unable to tanulle advantage of the economic improvement. Clampet-nullndquist nullnullnull showed that residents who moved to lower poverty neighborhoods were unable to benefit from their new circumstance because they were unable to connect to local social and communal networnulls. Overall, it appears that poverty in new neighborhoods is substantially lower when compared with the original neighborhood, but it is not clear if this change means a direct improvement for displaced residents. nullsidents continue to live impoverished lives, and continue to have many of the same economic problems that were present in their original neigh- borhoods. Unemployment nullsearch on unemployment and nullPnullnull generally indicates that residents do not improve their employment circumstances nullarrett, nullisel, and nullhnston, nullnullnullClampet-nullndquist, nullnullnullnulletnull nullnullnullnullvy and nullolley, nullnullnull Clampet-nullndquist nullnullnull showed that the residents displaced by using nullction nullvouchers reported few opportunities for employment in their new neighbor - hoods. nullvy and nullolley nullnullnull also reported that residents who relocated have been unable to improve their employment circumstances largely because of health problems. nullrrett, nullisel, and nullhnston nullnullnull showed that issues with transportation and childcare limited opportunities for employment among nullPnullnull residents. nullron et al. nullnullnull found that many residents were em - ployed. nullsidents who were not employed, however, reported barriers such as lacnullof nullb training and few opportunities for employment. The authors also reported that many residents who moved using nullction nullvouchers had trouble paying rent or other bills in the new private-marnullt housing because of lacnullof income from unemployment and higher bill payments required in the private housing marnullt. Thus, the research on nullPnullnull and employment indicates that residents continue to struggle to find wornullafter moving to homes in new neighborhoods. Racial Segregation nullcial segregation is a well nullown aspect of public housing in the nullited nullates, and is defined as a high percentage of residents within a neighborhood being from a minority population. nullsearch indicates that nullOPnull nullnull neighborhoods have high percentages of nullfrican-nullmerican residents nullnullroonulls

90 Crime and Urban Form nullnes and nullulsen 0null nullnull null1null 1null1null nullnullnull 1.nullmi Relocation nullnullts et al., nullnullnullnullnull nulluron et al., nullnullnullnullnull nullischer, nullnullnullnullnull nullingsley, nullohnson and Pettit, nullnullnullnullnull Popnullin et al., nullnullnullnullnull. nullscher nullnullnull concluded that nullPnullnull residents who relocate using nullction nullvouchers end up in neighborhoods that are racially segregated. nullngsley, nullhnson, and Pettit nullnullnull conducted a nationwide study and found that nullPnullnull has generally failed to decrease the amount of racial segregation that displaced residents enullerienced. nullron et al. nullnullnull found that change in racial segregation for most residentsnull neighborhoods was minimal. nulln general, it appears that neighborhoods where nullPnullnull residents move continue to be racially segregated, despite nullPnullnull obnullctives. HOPE VI in Lexington, Kentucky nullPnullnull relocations from the Charlotte Court nullusing Pronullct in nullnullngton are especially unique when compared with most other cities across the country. nullngsley, nullhnson, and Pettit nullnullnull showed that nullnullngton had the second highest average null.null of nullction nullrelocations per census tract. Their finding that displaced residents are tightly clustered is confirmed by our data mapped at the blocnullgroup level nullee enullibit null. The average number of nullction nullhouseholds per census tract across the country was only two households per tract. null addition, nullnullngton was nullof only nullcities, out of null total, where more than null percent of the relocatees moved to census tracts with large clusters of other relocated residents. This finding suggests that a small number of census Exhibit 1 Relocation Counts nullnullnullocnullnullounullin Lenullngton, nullntucnull

91Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls tracts in nullnullngton have absorbed a large percentage the nullPnullnull residents. nullsearch has shown that high concentrations of displaced residents will linullly affect housing stocnull crime, and social disorder in the clustered areas nulloulton and Pandey, nullnullnullCrane, nullnullnullnullngsley, nullhnson, and Pettit, nullnullnull Therefore, nullnullngton provides a unique opportunity to study the neighborhoods of displaced nullPnullnull residents. nullnother reason nullenullington is ideal for studying nullOPnull nullnull is that the relocations in nullenullington occurred at the end of nullnullnullnull, appronullimately the same time period that the nullnullnullnull Decennial Census was conducted. This timing means that the nullnull Census provides data about the neighborhoods at appronullmately the same point in time as the Charlotte Court nullPnullnull redevelopment. The cross-sectional nature of this study is an advantage because it provides a loonullat the neighborhoods that residents selected at the same time as their initial relocation. The typical neighborhood types that residents moved to directly after being displaced from the original public housing site can, thus, be determined. Summary and Hypotheses The quality of neighborhoods chosen by displaced residents is an important determinant of whether or not nullPnullnull goals have been achieved. The goals provide language that can be used to identify variables that measure nullOPnull nullnull efficacy. nullmong many nullOPnull nullnull obnullectives, nulley aims are to decrease the enullosure of residents to crime, decrease the concentration of poverty, improve opportunities for employment, and reduce racial segregation. nullsearch on the outcomes of nullPnullnull residents has been inconclusive, but has generally found that new neighborhoods are an improvement for residents nullnulloetnull, nullnullnullnullnull. nullearly all potential housing available outside of severely distressed housing pronullcts will be an improvement for nullPnullnull residents. null is linullly, however, that neighborhoods that nullPnullnull residents selected have similar problems to those found in the original nullPnullnull redevelopment neighborhood. nullnullngton, nullntucnull, was selected as the research site because of data availability, the unique characteristics of relocations within the city, and the time association be tween the relocations and the nullnull Decennial Census. nullighborhoods that displaced nullPnullnull residents selected are linullly to be significantly different from the original neighborhood. Hypotheses nullased on the goals previously listed, a series of hypotheses were developed to test whether displaced nullOPnull nullnull residents moved to better neighborhoods. nullirst, the neighborhoods that displaced nullOPnull nullnull residents selected are enullpected to be significantly lower on measures of crime, poverty, unemployment, and racial segregation than the original Charlotte Court neighborhood. null the new neighborhoodsnull levels are significantly lower than Charlotte Courtnull, then it could be argued that nullPnullnull achieved its goals. nullcond, it is enullected that, despite the improvement in neighborhood conditions when compared with the Charlotte Court neighborhood, the neighborhoods that displaced nullOPnull nullnull residents select will continue to be positively and significantly associated with rates of crime, poverty, unemployment, and racial segregation net of controls that might predict high rates of households using nullction null vouchers. null supported, the results would indicate that neighborhoods that displaced nullPnullnull residents select continue to be plagued by the same problems that nullOPnull nullnull is designed to alleviate.

92 Crime and Urban Form nullnes and nullulsen This article proceeds with the understanding that nullPnullnull redevelopments are highly contenullual, and using one enullmple to generalinull to all nullPnullnull sites would be inappropriate. nulludies that incorporate redevelopment sites not previously researched, however, provide opportunities for researchers to consider how nullPnullnull has affected different cities at different times. nullPnullnull goals are to improve the lives of individuals who live in public housingnulltherefore, evaluations should consider nullPnullnull goals and obnullctives when evaluating how the program has affected residentsnull lives. This study enullends the current body of research by employing official police data, framing the hypotheses based on the program goals, and using a study site that has a unique distribution of relocated residents. Data The data for this pronullct were collected for a sample of nullnullblocnullgroups in nullnullngton, nullntucnull. nullenullington is located in central nullentucnully and in nullnullnullnull had a population of nullnullnull,nullnullnull, manulling it the nullnullth largest city in the nullited nullates. The data used for analysis were obtained from three sourcesnull nullnullngton nullusing nullthority, the nullited nullates Census nullreau, and the nullnullngton Police Depart - ment. The studynull variables are described in the nenull section. Dependent Variable The dependent variable for this study is a count of the number of displaced residents per census blocnullgroup. The data were obtained from the nullnullngton nullusing nullthority. The data included nullnulladdresses that were geocoded using Cartographicanull nullng nullocoder and achieved a null-percent match rate. The addresses that did not match were either incorrectly recorded, or were located within another city. Consistent with nullngsley, nullhnson, and Pettit nullnullnull, displaced residents were highly concentrated in a small number of city blocnull groups. nullnullhibit null illustrates the positively snullewed relocation distribution and the large number of blocnullgroups that received nullro relocations. Exhibit 2 nullstnullnulltion of nullnullnnullnt nullanullanulle 100 null null null nullocnullnull onull nullnullt null 0 0 1 null null null null null null Relocation nullnullt null 10 1null 1null 1null null null null

93Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls Independent Variables The main independent variables in this study are reported crimes, poverty, unemployment, and nullrican-nullerican population. The crime data were obtained from the nullnullngton Police Department for the year nullnull. The reported crimes were provided at the address level and included assaults, auto theft, burglary, robbery, and disorderly conduct. The data were geocoded using Cartographicanulls nulling nulleocoder and achieved a nullnull-percent match rate. Data from nullnullnullnull were used because they match the temporal timeframe of the census data used for the other independent variables. Poverty rates were measured by the nullnull Census and were defined as the percentage of the population living below the poverty line. nullemployment was also measured by the census and was defined as the percentage of the population over age null who are unemployed. The final independent variable collected from the nullnull Census was the percentage of the population that was nullrican nullerican. nullcial segregation has been shown to be a continuing problem for nullPnullnull and, therefore, it is assumed that neighborhoods with high percentages of nullrican-nullerican population may be more linullly to have displaced nullPnullnull residents move there. Control Variables The control variables used in this study are necessary because it is linullly that displaced residents selected neighborhoods based on housing availability nullmith, nullnullnull nullcentral argument of this article is that, although improvements may enullst for residents, it is linullly that difficulties still enullst at the neighborhood level because residentsnull housing choices are limited. That is, residents are most linullly to choose places to live that are willing to tanull on residents using nullction nullhousing vouchers or that provide other forms of public housing. Therefore, controls were used for the rental vacancy rate, which is defined as the amount of vacant and available rental housing. nullother important control is the percentage of the population that receives public assistancenullbecause nullPnullnull is a form of public assistance, a control was used to account for the tendency of displaced residents to move to locations where other residents receive some form of public assistance. The variables used to measure nullPnullnull and the controls for housing availability are described in enullibit null Exhibit 3 Variable Mean Standard Deviation nullighnullnulloonullConnulltion nullscnullnullinull Statistics Relocations 1.null nullnull Renullnullenullcnullmes null.null null.1null nullnullcent nullnullnully 1nullnull 1nullnull nullnullcent unemnulloyment nullnull nullnull nullnullcent nullnullcan nullenullcan 1nullnull 1nullnull nullnullcent nullnullic assistance nullnull nullnull Rental nullcancy nullte nullnull nullnull

94 Crime and Urban Form nullnes and nullulsen Spatial Autocorrelation nullcause of the spatial nature of the distribution of nullPnullnull residents, it was prudent to test for spatial autocorrelation. nullatial autocorrelation is based on the idea that spatial events are arranged in a random or nonrandom manner due to factors associated with geographic location. nullatial autocorrelation causes biased estimates because the assumption of independence required by most statistics is violated. null the current study, it is linullly that residents selected homes in specific areas because of the availability of rental housing that was eligible for rent using nullction nullvouchers. Thus, it is logical that characteristics of specific places might affect the distribution of the displaced residents. nulleoDa was used to test spatial autocorrelation. nullesults indicate that the nullorannulls null statistic was not significant nullorannull nullnullnullnull, p nullnullnullnull which leads to the conclusion that spatial autocor - relation is not any more present in the spatial distribution of displaced residents than what would be enullected by random chance. null a result, the data were modeled with a nonlinear count model rather than a spatial-dependence model. Methods The analysis process for this study proceeded in two steps. To test the first hypothesis, the study compared the original Charlotte Court neighborhood with the new neighborhoods that displaced residents selected. null used rates of crime, poverty, unemployment, and racial segregation to com - pare the neighborhoods. null enullected to find that the new neighborhoods that displaced residents selected would be significantly lower on each of the measures listed above. nullnull, to test the second hypothesis, the study used regression analysis to determine if crime, poverty, unemployment, and racial segregation could significantly predict counts of displaced residents net of controls that could enulllain the presence of households using nullction nullvouchers. null discuss special problems with the regression models in the nenull section. Poisson and Negative Binomial Regression Models The dependent variable in this study is a count of the number of displaced nullPnullnull residents per census blocnullgroup. nullandard linear regression models, in general, are inappropriate for count data due to overdispersion. The Poisson regression model better estimates data that are counts by assuming a nonlinear distribution. null is typical for count variables to be overdispersed, however, due to the presence of a high number of nullero counts. Overdispersion describes the situation in which the vari- ance of the dependent variable enulleeds the mean nullong, nullnullnullOsgood, nullnullnull nullnully assumption of the Poisson regression model is that the variance must equal the mean for the model to accurately predict the dependent variable. null the social sciences it is rare for the Poisson regression model to fit the data distribution of a count variable. nullen data are overdispersed, the Poisson regression model underestimates nullro counts, which is the case in the present study. To formally test for over- dispersion, a linulllihood ratio test was conducted comparing the Poisson model with the negative binomial model. There is significant evidence of overdispersion null null nullnullnullnull, p nullnullnullnull therefore, the negative binomial regression model is more appropriate for the analysis than the Poisson model. nullnullhibit null compares the fit of Poisson regression model with that of the negative binomial regression model. Points greater than nullro indicate that the model underpredicts the observed values, and

95Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls points less than nullro indicate that the model overpredicts the observed values of the dependent variable. nulltice that the Poisson model drastically underpredicts nullro counts for the dependent variable. To account for this problem, the negative binomial regression model includes a parameter that enables the variance of the dependent variable to enulleed the mean. nullhibit nullshows a much stronger fit for the negative binomial regression, especially when the number of relocations within a blocnullgroup is nullro. Findings The following sections describe the results of a t-test for neighborhood comparisons and the nega- tive binomial regression analysis enullmining pronull variables for nullPnullnull goals and controls for neighborhood choice among displaced residents. Comparing Charlotte Court With Selected Neighborhoods To enullmine the first hypothesis, the Charlotte Court neighborhood was compared with the rest of the neighborhoods that nullPnullnull residents selected. nullt-test was used to enullmine the mean differences between the selected neighborhoods and the Charlotte Court neighborhood. The number of neighborhoods that received at least one displaced resident was null. nullhibit nullshows the findings. Overall, the average levels of crime, poverty, and racial segregation were lower, and unemployment was higher, in the new neighborhoods. The differences were not significant, how- ever. These findings do not support the initial hypothesis that neighborhoods selected by displaced residents would be significantly better than Charlotte Court, which leads to the conclusion that the neighborhoods that residents selected were not substantial improvements when compared with the conditions in their original neighborhood. Exhibit 4 Comnullnullng nullisson annullnullgatinull nullnomial Regnullession nullnullls nullRM nullnullgatinull nullnonullal Regression ModelnullnullM nullnullisson Regression Modelnull 0.null 0.null 0.1null 0.10 0.0null 0 null0.0null null0.10 null0.1null null0.null nullservednullr edicted null nullnull nullRnull null 1null 1nullnull0 nullnullt

96 Crime and Urban Form nullnes and nullulsen nullcause of the wide distribution of displaced residents around the city nullee enullibit null, the study included a secondary analysis, conducted between Charlotte Court and the neighborhoods that received a large number of displaced residents. null cutoff was selected for neighborhoods that received nullnull or more displaced households. nulleighborhoods that received nullnull or more households were selected because they were public housing sites or places that were linullly to accept nullction nullvouchers. Of the sample, nullneighborhoods received more than null displaced residents, and, overall, these null neighborhoods accounted for nullnullnull relocationsnullnullnearly nullnull percent of the total displaced residential households. nullhibit nullshows that the nullneighborhoods had higher nullut not statistically significantnull levels of crime, poverty, unemployment, and nullrican-nullerican population than the Charlotte Court neighborhood. Exhibit 5 nullarlotte nullnullt Selected Neinullborhoods null null4null Sinullinullance Comnullnullng Chanullotte Counull nullth Selectenullnullighnullnulloonull (n = null) Renullnullenullcnullme null.0 null.0 0.null nullnullcent nullnullnully null.0 null.0 0.null nullnullcent unemnulloyment nullnull nullnull 0.null nullnullcent nullnullcan nullenullcan null.0 null.0 0.null Exhibit 6 nullarlotte nullnullt Selected Neinullborhoods null nullnull Sinullinullance Comnullnullng Chanullotte Counull nullth Selectenullnullighnullnulloonull (n = null Renullnullenullcnullme null.0 null.0 0.null nullnullcent nullnullnully null.0 null.0 0.null nullnullcent unemnulloyment nullnull nullnull 0.null nullnullcent nullnullcan nullenullcan null.0 null.0 0.null Negative Binomial Regression Results The first baseline model included only the control variables. nulldel nullin enullibit nullshows the results from the negative binomial regression analysis. nullPnullnull resident presence in new neighborhoods was significantly associated with public assistance levels present in the neighborhood and with the amount of housing that was for rent and vacant. nullr every one unit increase in public assistance, the enullected count of displaced residents increases by null.nullpercent null nullnullnullnull p nullnullnullnull nullr every one-unit increase in rental vacancy, the enullected number of displaced residents increases by null.nullpercent null nullnullnull, p nullnullnullnull. nullodel null in enullhibit null shows the innulluence of the main enullplanatory variables. The number of reported crimes and amount of racial segregation were significant. nullr every one unit increase in reported crime, the enullected count of displaced residents increases by nullnullpercent null nullnullnull, p nullnullnullnull. nullr every one unit increase in nullfrican-nullmerican population, the count of displaced residents is enullpected to increase by nullnullpercent. Poverty and unemployment were not significantly associated with the number of displaced households within a neighborhood net of the other enulllanatory variables.

97Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls nulldel null in enullibit nullincludes both the enulllanatory and control variables. nullported crime and the nullrican-nullerican percentage of the population were significant indicators when holding all other variables constant. The percent changes in crime and nullrican-nullerican population were almost identical to the results from nulldel null The effect for poverty became negative when all of the variables are includednullhowever, this effect is small and insignificant. nullterestingly, the control variables, which were significant in nulldel null lose significance in nulldel null Consistent with past research, nulldel nullappears to show that the amount of public assistance and vacant rental housing within a neighborhood affects residentsnulldecisions in their move. Despite the effects that these factors had on residentsnulldecisions, however, it appears that neighborhoods that residents selected were still significantly associated with reported crime and with the percentage of nullrican nullericans in the neighborhood population. nullny enulllanatory and control variables are correlated. nullhibit nullshows the variables correlation matrinull nullthough none of the variables have very strong associations, many are significantly associ - ated with one another. To account for the collinearity of the enulllanatory variables, each variable was evaluated independently with the control variables to remove any possibility of multicollinear- ity disrupting the results. nulldel nullin enullibit nullshows that crime continues to be significantly associated null nullnullnull, p nullnullnullnull with displaced nullPnullnull households and continues to have a similar enullected percent change. nulldel nullindicates that poverty is significant when considered independent of other enulllanatory Exhibit 7 Variables Model 1 Model 2 Model 3 q SE null nullnull q SE null nullnull q SE null nullnull nullgatinull nullnomial Regnullession Results Renullnullenullcnullmes 0.0null 0.00null 0.000 nullnull 0.0null 0.00null 0.000 nullnull nullnullnully 0.00null 0.01null 0.nullnull 0.null null0.01null 0.1null 0.null1 null1.null nullemnulloyment 0.01null 0.0null 0.nullnull 1.null 0.0null 0.0null 0.nullnull nullnull nullnullcan nullenullcan 0.0null 0.010 0.000 nullnull 0.0null 0.00null 0.01null nullnull nullnullic assistance 0.1null 0.0null 0.01null 1nullnull 0.0null 0.0null 0.1null nullnull Rental nullcancy 0.null0 0.0null 0.000 null.null 0.0null 0.0null 0.10null nullnull nullnulll null null (df) null.null(null null.null(null null.null(null Exhibit null Relonull cations Renullrted nullimes Povertnull nullemnullonullnull ment nullrican nullerican Pnulllic nullsistance Rental Vacancnull nullanullanulle Conullelations Relocations 1 Renullnullenullcnullmes 0.nullnull 1 nullnullnully 0.nullnull 0.nullnull 1 nullemnulloyment 0.0null 0.nullnull 0.nullnull 1 nullnullcan nullenullcan 0.nullnull 0.nullnull 0.nullnull 0.1null 1 nullnullic assistance 0.nullnull 0.nullnull 0.nullnull 0.0null 0.nullnull 1 Rental nullcancy 0.nullnull 0.nullnull 0.nullnull 0.0null 0.nullnull 0.nullnull 1 nullnullnullsigninullance lenulll nullgnullnullnullrcent cnullngenullnullnullnullnullluenullSE nullstandard errornull

98 Crime and Urban Form nullnes and nullulsen variables. null addition, nulldel nullshowed that when all of the variables were considered simultane - ously, the effect of poverty on relocation was negative. nulldel nullindicates that, after controlling for the public assistance level and the percent of rental vacancies, poverty is a significant factor present in new neighborhoods. The fact that poverty is no longer significant when the other enulllanatory variables are included indicates that, while poverty continues to be a factor in new neighborhoods, crime and percent nullrican nullerican are stronger predictors of displaced residents in the neighbor - hoods. nullodel null indicates similar results as previous analyses. nulleighborhoods that nullOPnull nullnull residents selected do not have significant problems associated with unemployment. nulldel nullindicates that the percentage of population that is nullrican nullerican continues to be significant when considered independent of the other enulllanatory variables. Summary and Implications The findings presented in this study suggest that displaced nullPnullnull residents in nullnullngton, nullntucnull, chose to move to areas where crime and racial segregation are significant problems. The reason behind these choices is unclear, but indications from past research on nullPnullnull displace - ment suggest that housing choice is highly dependent on the housing marnullt at the time of the relocation nullingsley, nullhnson, and Pettit, nullnullnullPopnulln et al., nullnullnullnullith, nullnullnull The findings from this study are consistent with those previous studies. Displaced residents chose neighbor- hoods that had high levels of public assistance available and that had a high number of vacant rental units. These findings suggest that residents moved to neighborhoods that were the nenull best available housing beyond the most severely distressed public housing. Exhibit null Variable Model 4 Model 5 q SE null nullnull q SE null nullnull Variable Model 6 Model 7 q SE null nullnull q SE null nullnull nullgatinull nullnomial Regnullession Coefnullients Renullnullenullcnullmes 0.0null 0.00null 0.000 nullnull nullnullnully 0.0null 0.0null 0.01null nullnull nullemnulloyment nullnullcan nullenullcan nullnullic assistance 0.1null 0.0null 0.000 1nullnull 0.0null 0.0null 0.nullnull nullnull Rental nullcancy 0.1null 0.0null 0.0null 1null0 0.null0 0.0null 0.001 null.0 nullnulll null null (df) null.null(null null.null(null Renullnullenullcnullmes nullnullnully nullemnulloyment 0.0null 0.0null 0.nullnull null1 nullnullcan nullenullcan 0.0null 0.01null 0.000 nullnull nullnullic assistance 0.1null 0.0null 0.01null 1nullnull 0.0null 0.0null 0.nullnull null0 Rental nullcancy 0.nullnull 0.0null 0.000 null.0 0.1null 0.nullnull 0.000 null.null nullnulll null null (df) null.null(null null.null(null nullgnullnullnullrcent cnullngenullnullnullnullnullluenullSE nullstandard errornull

Unknown

99Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls Two important considerations must be understood before determining the effects of nullPnullnull redevelopments. The first consideration is that pronullct site evaluation results have little value in describing the outcomes of the original residents unless a high percentage of them returned to the redeveloped housing. null the present study, no previous residents returned to the original site. Thus, to gain the best understanding of the outcomes for the residents, it is important to focus attention on the types of places where nullPnullnull residents move. The second consideration that must be made is that determining the level of improvement for nullPnullnull residents is very dif - ficult. nullthin practically any enullmination of nullPnullnull, it will be difficult for researchers to argue that relocation housing is not at least somewhat better than the original public housing. null seems inevitable that a program designed to identify and redevelop the most severely distressed public housing would have high levels of residents reporting better living circumstances. Two questions remainnullnulle the new housing and neighborhoods areas that can sustain social cohesionnullnullre the areas that absorbed hundreds of displaced residents prepared to do sonull The purpose of this article is not to indicate that these neighborhoods were troubled because of the residents, but instead that the residents moved to neighborhoods with preenullsting problems, and combining these preenullsting problems with an already struggling population may be the recipe for future failure. This studynull main finding is that crime levels and racial segregation in a city blocnull group significantly predict the number of displaced residents after controlling for neighborhood choice factors. These findings raise serious questions for the city of nullnullngton about how effective the implementation of the Charlotte Court nullPnullnull pronullct was and if better planning would have positively affected the outcomes of residents involved in the relocation process. This studynull methodology is an enullmple of how readily available data can be used to inform and guide public policy decisionmanulling. nullocal police departments, local housing agencies, and a number of federal agencies have data available for analysis, which can greatly aid in predicting governmental program outcomes. null logical relocation method would be to provide residents with housing services in neighborhoods with lower crime and racial segregation levels. One nullPnullnull goal, to create local partnerships that can provide additional funding and services for the residents, was not analynulld in this study. nullgood use of nullPnullnull funds would be to pro - mote relationships between nullction nullvoucher users and local property owners that are located in areas with better schools, lower crime, lower racial segregation, and, in general, more opportunities for success. This service would enable the city to provide a safeguard from increasing problems in neighborhoods on the brinnullof becoming troubled and would provide more economic and social opportunities to residents. nullother good use of local partnerships would be to develop design initiatives that are conducive to returning residents to the original neighborhood. nullny original resident households do not have enough money to move bacnull into minulled-use, affordable housing typical of nullOPnull nullnull redevelopments. nullcal partnerships should wornullwith designers to develop new housing that is designed for the original residents. Designs should include considerations for social cohesion and crime prevention as well as for social and physical sustainability. Designing nullPnullnull redevelopments for the original residents does more to attacnullthe problems that most concern the individuals whom nullPnullnull is supposed to help. null addition, wornullng to return original residents to the nullPnullnull sites eliminates problems associated with resident displacement. nullighborhoods selected by displaced residents in

100 Crime and Urban Form nullnes and nullulsen this study were already struggling with issues related to crime, poverty, unemployment, and racial segregation. Thus, wornullng to help residents avoid these areas is mutually beneficial for both the displaced residents and the potential new neighborhoods. null promoting partnerships in the local housing marnullt, the concentration of displaced residents in already struggling neighborhoods can be reduced and more can be done to return residents to their original neighborhoods where more social capital is available. nullnullonulledgnullnts The authors thannullthe nullnullngton nullyette nullban County nullvernment Division of Police and the nullnullngton nullyette nullban County nullusing nullthority for their assistance in providing data for this research. nulltnullrs nulldericnullnull nullnes is a PhD student and associate instructor in the Department of Criminal nullstice at nulldiana nulliversity. Derenullnull Paulsen is an associate professor of criminal nullstice and the director of the Center for Crime and the nullilt nullvironment at nullstern nullntucnull nulliversity. References nullt nullsociates, nullc. nullbtnull nullnull. null nullstorical annullBaseline nullsessment onullnullnull Vnull Volume 1 Crossnull Site Report. nullport prepared for the nullnull Department of nullusing and nullban Development, Office of Policy Development and nullsearch. nullshington, DCnullnullnull Department of nullusing and nullban Development. nullrbrey, nullhn null nullnull. nulleasuring the nullfectiveness of Crime Control Policies in nullonullillenull Public nullusingnullnulling nullpping nullftware To nulllter Part nullCrime Data,null nullurnal onullContemporary Criminal nullstice null nullnull nullnull. nullrrett, nullith null, Paul nullisel, and nulln nullhnston. nullnull. nulle Ramona nullti Reportnullnullpacts onullRipley nullnolnullRelocation nullogramnullnullar 3 null00nullnullnull Paper prepared for the city of nullrt nullrth, Tenulls. nullrt nullrthnullnullpublished manuscript. nullston, Thomas D. nullnull. nullhe nullfects of nullnulld-nullcome nullvitalinulltion and nullsidential nullbility on Public nullusing nullsidentsnullnullCase nulludy of the nulllanta nullusing nullthority,null nullurnal onulltnull nullerican nullanning nullsociation null nullnull nullnull. nulloonull, nulled, Carole nullganullga, nullmes nulllnull and nullry nullne nullams. nullnull. nullesident Perceptions of nullusing, nullighborhood, and nullonomic Conditions nullter nulllocation nullom Public nullusing nulldergoing nullPnullnull nulldevelopment,null Researcnullon Social nullrnullnullactice null nullnull nullnullnullnull nulluron, nullarry. nullnullnullnull. nulln nullmprovenull nulliving Environmentnull Neignullbornulloonull nullutcomes nullor nullnullnullE Vnull Relocatees, nullrief nullo. null. nullashington, DCnull The nullrban nullnstitute, nulletropolitan nullousing and Communities Center.

101Cityscape HOnull null Resident nullsnullacenullntnull Using HOnull null nullogranullGoals nullo Enullluate nullignullornullod Outconulls nullron, nullrry, Diane null nullvy, and nullgan nullllagher. nullnull. nullusing Cnullice Voucnullrsnullnullw nullnull Vnull nullamilies nullarenull in tnulle nullrivate nullarnullet, nullrief nullo. null. nullashington, DCnull The nullrban nullnstitute, nulletropolitan nullusing and Communities Center. nullron, nullrry, nullsan Popnulln, Diane nullvy, nullura nullrris, and nullll nulladduri. nullnull. nulle nullnull Vnull Resinullnt nullacnullng Stunullnull nullshington, DCnullThe nullban nullstitute. Clampet-nullndquist, nullsan. nullnull. nulloving Over or nullving nullnullnullort-Term nullins and nullsses for nulllocated nullPnullnull nullmilies,null nullurnal onullnulllicy nullvelopment annullResearcnull nullnullnull nullnullnull Comey, nullnnifer. nullnull. nullnull Vnullnullannullnull tnull nullve , nullief null. null nullshington, DCnullThe nullban nullstitute, nulltropolitan nullusing and Communities Center. Coulton, Cladia, and nullanta Pandey. nullnull. nulleographic Concentration of Poverty and nullsnullto Children in nullban nullighborhoods,null nullerican Benullvioral Scientist nullnullnullnullnullnull Crane, nullnathan null nullnull null nullhe nullidemic Theory of nullettos and nullighborhood nullfects on Dropping Out and Teenage Childbearing,null nullerican nullurnal onullSociology null, 122nullnull12null . nullscher, Paul. nullnull. nullction nulland the Public nullusing nullvolutionnullnullere nullill the nullmilies nullnull nullpublished manuscript, nullnull nullrest College. nulllderbloom, nullhn. nullnull. nullvisible Citynullnullverty, nullusing, annullNew nullbanismnull nullstin, Tnull The nulliversity of Tenulls Press. nullnull nullnull. Newport nullnull VnullEvaluationnullnullnal Reportnull nulluisville, nullnullCenter for nullstainable nullban nullighborhoods. nulletnull nullward null nullnull. nulletter nullighborhoods, nulltter Outcomesnullnullplaining nulllocation Outcomes in nullPnullnull,null Cityscape null nullnull nullnull. nullngsley, null Thomas, nullnnifer nullhnson, and nullthryn null Pettit. nullnull. nullatterns of nullction null nulllocation in the nullPnullnull Program,null nullurnal onullnullban nullnullirs 2null nullnull nullnullnullnull nullvy, Diane null, and nullrnullnullolley. nullnull. Employment Barriers nullong nullnull Vnullnullmilies , nullief null. null nullshington, DCnullThe nullban nullstitute, nulltropolitan nullusing and Communities Center. nullng, nullott. nullnull. Regression nullnullls nullr Categorical annullnullmitenullnullpennullnt Variables . Thousand Oanull, Cnull nullge Publications. nulldwig, nullns, nulleg null Duncan, and Paul nullrschfield. nullnull. nullrban Poverty and nullvenile Crimenull nullidence nullom a nullndominulld nullusing-nullbility nullperiment,null nulle nullarterly nullurnal onullEconomics nullnullnullnull nullnullnullnull nullndenhall, nullby, nullefanie Denullca, and nulleg Duncan. nullnull. nulleighborhood nullsources, nullcial nullgregation, and nullonomic nullbilitynullnullsults nullom the nullutreaunullProgram,null Social Science Researcnull null nullnull nullnullnullnull nulltional nullusing nullw Pronullct. nullnull. nulllse nullnullnullnullCritical nullsessment onulltnull nullnull Vnullnullblic nullusing Renullvelopment nullogramnull Oanulland, Cnull nulltional nullusing nullw Pronullct.

102 Crime and Urban Form nullnes and nullulsen Osgood, D. nullyne. nullnull. nulloisson-nullsed nullgression nullalysis of nullgregate Crime nulltes,null nullurnal onull nullantitative Criminology null nullnull nullnullnull Peterson, nullth D., and nulluren null nullivo. nullnull. nullacial nullgregation and nullacnullnullban nullmicide,null Social nullrces null nullnull nullnullnullnullnull Popnulln, nullsan null nullnull. nulleyond Crime Preventionnullnullw the Transformation of Public nullusing nulls Changed the Policy nulluation ,nullCriminology annullnullblic nulllicy nullnullnull nullnullnull Popnulln, nullsan null, and nullinullbeth Cove. nullnull. Sanullty null tnull nullst nullportant nullingnullnullw nullnull Vnull nulllpenullnullmilies , nullief null. null nullshington, DCnullThe nullban nullstitute, nulltropolitan nullusing and Communities Center. Popnulln, nullsan null, nullctoria null nulliasda, nullnn null Olson, Dennis P. nullsenbaum, and nullrry nullron. nullnull. nulle nullnullen nullrnullCrime annulltnull nullagenull onullnullblic nullusing in Cnullcagonull Piscataway, nullnullnulltgers nulliversity Press. Popnulln, nullsan null, nulluce nulltnull nullry null Cunningham, nullren D. nullown, nullremy nullstafson, and nullrgery null Turner. nullnull. nullnullcanull onullnullnull Vnull Researcnullnullnnullngs annullnulllicy Cnullngesnull nullshington, DCnullThe nullban nullstitute. Popnulln, nullsan null, Diane null nullvy, nullura null nullrris, nullnnifer Comey, nullry null Cunningham, and nullrry nullron. nullnull. nullnull Vnullnullnel StunullnullBaseline Reportnullnullnal Reportnull nullshington, DCnullThe nullban nullstitute. nullhill, nullchael null, and nullsan null nullchter. nullnull. nullhe nullatial nullas of nullderal nullusing nullw and PolicynullConcentrated Poverty in nullban nullerica,null nulliversity onullnullnnsylvania nullw Review nullnull nullnullnullnullnull nullith, nullbin. nullnull. nullusing Cnullice nullr nullnull VnullRelocateesnull nullshington, DCnullThe nullban nullstitute. nullnull Department of nullusing and nullban Development nullnullnull nullnull. nullbout nullPnullnull.null httpnullnull www.hud.govnullfficesnullihnullrogramsnullhnullopenullaboutnullndenullcfm nullccessed nullnuary null, nullnullnull nullnuller, nullrry null nullnull. nullOPnullnullnullnullrnullt nullansnullublic nulldsnullhe nullals, nullrategies, and nulldterm nullssons of nullDnull nullban nullvitalinulltion Demonstration Program,null nullurnal onullnullnullrnullble nullusing null nullnull nullnullnullnull nullditional Reading nullllagher, nullgan, and nullata nullnullnull nullnull. nullving nullnullBenenulls annullCnullllenges onullnullnull Vnullnullr Cnulllnullen, nullief null. null nullshington, DCnullThe nullban nullstitute, nulltropolitan nullusing and Communities Center.

103Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Using the Weighted Displacement Quotient To Explore Crime Displacement From Public Housing Redevelopment Sites Meagan nullnullll The nullban nullstitute nullstract nullnull Vnull a nullnullral nullnnullng initiative begun in 1null2, is nullsignenullto eranullcate severely nullistressenull public nullousingnull nullnulle program implicitly recogninullenull tnulle importance onull geograpnully to its enullnullrts, also aiming to improve connulltions in areas surrounnullng sites targetenullnullr improvement unnullr nullnull Vnull nullis article consinullrs cnullnges enullnullctenullby nullnull Vnullrenull null velopment nullom tnull perspective onullgeograpnull, enullmining tnull spatial movement onullcrime in annullarounnullnulle public nullusing sites in nulllwaunulle, nullisconsin annullnullasnullngton, nullCnull tnullougnullut tnullir renullvelopment, using tnull nulleignullenullnullsplacement nullotient nullnullnull nulle results nullom nulllwaunulle were minullnull witnullevinullnce onullcrime nullsplacement provinullnullby some onulltnull nullnullnullnull nulllwaunulle, tnull renullvelopmentnulls renullction enullnullcts on crime grew witnulltime annullwere stronger later in tnull stunull perionull nulle results nullom nullasnullngton, nullCnulls Capitol nullteway annullCappernullarrollsburg sites were mucnullmore consistent tnulln tnullse nullom nulllwaunulle, because tnull nullnull nullr nullnullnullrent areas annulltime perionull pronullcenull similar results, innullicating a nullinullnullusion onull benenulltsnull Because onull its simplicity, tnulle nullnullnull snulloulnull be attractive to practitioners wnull are stunulling tnull enullnullcts onulltnulls type onullrenullvelopment, as long as tnull metnullnullis usenullwitnulltnull unnullrstannullng tnullt tnull results are nullscriptivenull Introduction nullnce nullnull, nullPnullnull has been the largest and perhaps the most visible federal effort to improvenull or rebuild altogethernullhe nationnulls most decayed public housing and most dire living conditions. null its inception, nullPnullnull represented a radical change in housing policy, with the idea that the

104 Crime and Urban Form Canullll worst public housing developments needed enullreme changesnullmost nullPnullnull sites are demolished and replaced with new, redesigned housing. The program also aims to reduce the concentration of enullreme poverty that is typical in targeted public housing developments by creating minulld-income and minulld-use spaces, and by minullng renters and owners. The reauthorinulltion of nullPnullnull in nullnull cited sinullmain goals of the program, three of which were improving the living conditions and lives of public housing residents, deconcentrating poverty and low-income families, and manullng those changes sustainable. nullfourth goal, nullontributing to the improvement of the surrounding neighborhood,null null enulllicitly recogninulld that redevelopment plans could not ignore what was going on around each site, and that improving the surrounding area was nully to sustaining changes brought about by redevelopment. nullography, therefore, was understood to be an important element of nullPnullnull efforts. This article considers changes effected by nullPnullnull redevelopment from the perspective of geography, enullmining the spatial movement of crime in and around five public housing sites throughout their redevelopment. nullthough many studies have considered the effects of nullPnullnull on both residents and neighbor - hoods nullor enullmple, see Castells, nullnullnullPopnulln et al., nullnull, nullnullnullTurbov and Piper, nullnullnull nullelenbach and nulloith, nullnullnull few have considered the effects of redevelopment under this federal program on crime levels in the neighborhoods surrounding the sites, and fewer still have specifi- cally tied changes in crime levels to a sitenulls redevelopment timeline and physical changes. The current research aims to remedy that deficiency through an enullmination of crime displacement andnullr potential diffusion of benefits in and around five public housing developments undergoing redevelopment in nulllwaunulle, nullisconsin, and nullashington, D.C. This article reports on the results from one part of a larger research pronullct that statistically analynulld crime displacement using three different methodologiesnullthis article focuses on the results of the nulleighted Displacement nullotient nullDnull nullowers and nullhnson, nullnullnull null The nullnullis a descriptive measure based on simple arithmetic, manullng it easy to implement and very accessible, even to those without a bacnullround in statistics. One big attraction of the quotient is the ease of changing its parameters, which allows a researcher to compare results for different areas and across different time periods in short order. nullterpreting the results is straightforward. The nullnullshould be attractive to practitioners who are studying the effects of large-scale public housing redevelopment, as long as the method is used with the understanding that the results are descriptive. Policymanullrs want to nullow what wornull immediately or soon after an intervention is implemented, and this short-term thinnullng often precludes researchers from using mores statisti - cally rigorous designs. This desire for very timely results manulls the nullnullvaluable. One of this articlenulls aims is to demonstrate that limited resources or statistical snulllls should not limit the ability of practitioners to consider possible displacement or diffusion of benefits in the area around the redevelopment site. null nullnull nullusing nullt of nullnull, nullction nullnullnull Public nullw nullnullnull nullnullnullnullC. nullnullvnullas amended by nullction nullnullof the nullality nullusing and nullornullnullsponsibility nullt of nullnull. Public nullw nullnullnullnull October null, nullnull. null Contact the author for information on the full report that contains analysis using all three methodologies.

105Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites Public housing is being renovated under this federal program nationwide, with the enullectation that quality of life in the immediate and surrounding areas will be improved greatly. The programnulls ability to affect community crime rates, however, is still unclear, and the research for this article sought to address that question. Background This article first reviews the relevant literature in two main topic areas on which the current research is founded. nullsignificant amount of previous wornullhas been conducted on levels of crime in public housing and the elements of public housing that might contribute to higher crime rates. nullss wornull however, has focused on displacement following crime prevention and intervention ef - forts, and even less has focused on displacement from public housing. This wornullthus enullands the body of evidence on both the effects of crime on public housing redevelopment and the linulllihood that crime will be displaced to other areas after such efforts are implemented. nullnally, this section details the data and methodology used in this research. Crime and Public Housing Two main schools of thought inform our researchnullnullnullcrime and space approaches, which include ideas from the Crime Prevention Through nullvironmental Design school and defensible space the - ory and nullnullsocial disorganinulltion theory. These approaches have a common focusnullthe importance of informal social control, manifested, in this case, as public housing residentsnullability to control criminal behaviors. nullth of these approaches also focus at least in part on the physical nature of the neighborhoodnulln this case, a public housing sitenullnd its effects on crime and social control. These perspectives have guided the investigation of public housing and crime over the past several decades, and it is commonly recogninulld that the residents of the nullnds of severely distressed public housing sites targeted by nullPnullnull are disproportionately affected by crime nullenullances and nullith, nullnullnullnullD, nullnullnull Pyle nullnullnull suggested that areas with public housing tended to attract a substantial number of offenders from surrounding areas, while more recent research by nullgan and Davies nullnullnull found that violent crime tended to be associated with public housing units. nullnullwise, Dunworth and nulliger nullnullnull found higher rates for drug arrests and violent crime in areas with public housing compared with similar neighborhoods. This relationship between public housing and crime is supported by further evidence that the environment of many public housing complenulls attracts drug trafficnullng and violence nullosburg, Popnulln, and nullcnull, nullnullnullPopnulln et al., nullnullnull nullwmannulls nullnullnull nullnullnullfoundational wornullon defensible space suggested that the physical design of public housing was nully in preventing crime there. nullinullewise, Popnullin et al.nulls nullnullnullnullnullnull review of nullOPnull nullnull tied the often enulltremely unsafe conditions that enullisted in nullOPnull nullnull sites before redevelopment to poor physical design of the buildings themselves. nullthough the studies mentioned previously analynulld crime in and around public housing, few studies have focused on assessing the effect of large-scale changes to public housing environments nullones, nullnullnullTurbov and Piper, nullnullnull Turbov and Piper nullnullnull considered changes in crime

106 Crime and Urban Form Canullll levels surrounding nullPnullnull sitesnullwhile they simply reported crime levels before and after redevel - opment, their wornulldid find that crime levels dropped dramatically in nullPnullnull neighborhoods. Crime Displacement nullthough numerous types of displacement might occur after implementing an anticrime initiative, this article focuses solely on spatial displacement, or the movement of crime from a targeted area to nearby areas. Previous research in this area has largely focused on displacement that occurs as the result of a focused police enforcement effort or police intervention. These interventions are typically focused on small hot spots of crimenulleveral square blocnull at mostnullnd have distinct periods of implementation. Determining how to predict and test for possible spatial crime displacement following an interven- tion is one that has been the focus of environmental criminologists for at least the past three decades nullnullrantingham and nullrantingham, nullnullnullnullnull Cornish and Clarnulle, nullnullnullnullnull nullabor, nullnullnullnull, nullnullnullnullnull nullepetto, nullnullnullnullnull nullherman, nullartin, and nulluerger, nullnullnullnullnull. The literature on these efforts is limited, and those studies that have considered displacement have shown minulled results nullnullarclay et al., nullnullnullnullnull nullraga and nullond, nullnullnullnullnull nullaga et al., nullnullnullnullitsch, Caeti, and Taylor, nullnullnullnullwton, Taylor, and nullongo, nullnullnullnulleisburd et al., nullnullnull nullsentially, no clear or consistent finding enullsts in the literature regarding the linullli - hood that displacement will occur or the enullpected magnitude of any displacement that might occur. nullthough displacement does not appear to be a given after police enforcement efforts, researchers do not nullow if those conclusions can be applied to other, more long-term events, linull the rede - velopment of public housing. nullvertheless, this information on whether such displacement may occur is imperative for practitioners planning large-scale redevelopment efforts. nulle theorinull that crime can be displaced from public housing redevelopment sites in two main ways. Displacement can occur when public housing residents move to new locations and crime moves with those individuals to their new residences. Crime displaced in this manner would be enullected particularly in the case where large numbers of residents are moved en masse to a new location. nullternatively, crime itself may move out of the redevelopment site to nearby locations. null this case, the perpetrators of crime nullor enullmple, drug dealersnullmight attempt to maintain their criminal activities in the same general area, but are forced out of the redevelopment site itself nullor enullmple, by construction or increased enforcementnull Or, the criminal opportunities in the rede - velopment site might be reduced, but nearby areas may still offer criminal opportunities and thus absorb the crime that would have otherwise been occurring in the redevelopment site itself. null the outset of our research, our assumption was that crime would move via either or both modes of displacementnullwith relocated residents moving to their new neighborhoods, or to other nearby areas offering similar criminal opportunities. nullcause of limitations in the data collected for this study, however, our ability to study enulllicitly the first mode of spatial displacement was limited. null neither nulllwaunulle nor nullashington, D.C., were we able to obtain information on the locations of individual residents during and after the redevelopment process. These limitations were tempered somewhat, especially in nulllwaunulle, by one important factornullthe housing authority there made a significant effort to nullep residents within the redevelopment neighborhood and moved residents around among available units as construction toonullplace elsewhere within the site. Therefore,

107Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites although we did not have information on nulllwaunulle residentsnullnew addresses, we had good reason to believe that many, if not most, remained on site, and were therefore able to test for both types of spatial displacement simultaneously. null nullashington, D.C., we learned from interviews with housing authority staff that residents were moved to any available public housing unit or given nullction nullvouchers. Those interviewed did not feel that critical masses of residents had all moved to the same areas but rather that residents were scattered to various locations throughout the city. The lacnullof data on residents and the scattering of residents throughout the city made testing the first type of spatial displacementnullhere crime moves with residents to their new neighborhoodsnulloth impossible and, arguably, irrelevant. The second mode of displacement, however, was fully testable in both nullilwaunullee and nullashington, D.C. Data The research team conducted interviews with staff members of the nullusing nullthority of the City of nulllwaunulle nullnullnull and the District of Columbia nullusing nullthority nullCnullnullto gather information used to determine the redevelopment timelines and define pre-, during-, and post- intervention periods for use in the analyses. The research team collected address-level incident data for the city of nulllwaunulle from the nulll - waunulle Police Department for the nullnuary nullnull-to-nullbruary nullnull period. null nullcident data included all nullroup nullnulloffenses as classified under the nulltional nullcident-nullsed nullporting nullstem. nulle then geocoded addresses using a streetfile provided by the City of nulllwaunulle with a nullnullpercent match rate. nullnally, the team classified offenses into personal nulliolentnulland property offenses. Personal offenses included homicide, senullal offenses, assault offenses, and nulldnapping. Offenses were aggregated into monthly counts, giving pronullct staff nullnullmonths of data with which to conduct statistical analyses. The research team also collected address-level incident data for nullashington, D.C., from the nulltro - politan Police Department nullPDnullfor the nullnuary nullnull-to-nullptember nullnull period. nullcident data included all Part nulloffenses as classified under the nulliform Crime nullport system run by the nullderal nullreau of nullvestigation. nullD provided the research team with the geographic coordinates of all incident locations, so no geocoding was necessary. null in nulllwaunulle, researchers classified data into personal and property offenses, following the same scheme used for the nulllwaunulle data. Methods nullfore analysis began, the research team had to nullnullidentify the overall timeline of redevelopment for each site in the studynullnullnulldetermine the nullnterventionnullpointnullnullso that we could identify dis - placement periods, during which we would enullect to see displacementnullnullnulldefine the boundaries null nullte that the police department in nulllwaunulle changed data systems in early nullnull. This shift in systems created two data issuesnullone was that data for the last nullmonths of nullnull were missing. nullnthly totals for those months were imputed using data before and after the missing months. The second issue was that arrest data from before nullnull were missingnullwe thus were unable to analynull arrest data as initially planned.

108 Crime and Urban Form Canullll of the site from which crime displacement might occurnullnullnulldefine the displacement areas nullhose sites to which crime might be displacednull and nullnullidentify comparison areas. nullw-income housing redevelopment under nullPnullnull typically tanulls place over a long period of time, and the redevelop - ment sites often have poorly defined, vague, or overlapping geographic boundaries. This was the case in nulllwaunulle, especially, which made site definition very difficult. nullte definition in nullashing - ton, D.C., however, was relatively straightforward. Crime displacement is most often studied by choosing an area to which crime will most linullly be displacednullreferred to as the nulldisplacement nullonenullnulland comparing levels of crime in that area with the target area nullrea where the intervention toonullplacenull The displacement nullne most commonly surrounds the target areanullthis particular design is sometimes described as a nulluffer nullne.nullThis buffer nullone can be concentric, reaching a set distance in all directions from the target area, or it can be contiguous to the target area but enullending only in limited directions nullamilton-nullith, nullnullnull nullst research thus far has loonulld for immediate spatial displacement in areas contiguous to the target area nullraga, nullnullnull The sinull of the nullne varies in the literature as well, and nullwers and nullhnson nullnullnull suggested that there is a displacement gradient that describes displacement as de - creasing with increasing distance from the target areanullthey suggested using multiple displacement nullnes that increase in distance from the target area for comparison purposes. The displacement areas for both the nulllwaunulle and nullashington, D.C. sites were drawn as concen - tric rings nulluffer nullnesnullaround the target area. nulle tested two nullnes for displacement in each sitenull one ring that was nullnullnullfeet from each site and one that was nullnullnullfeet from each site. The two buffer areas were mutually enulllusivenullthe area contained in the nullnullnullfoot buffer did not include the nullnullnullfoot buffer. This method allowed us to determine whether displacement or diffusion of benefits occurred only within areas very close to the site, or if either had a wider reach. null the selected study sites, comparison areas were selected based on recommendations from both nullCnulland DCnull. Comparison sites were other public housing developments that were similar to the redeveloped sites before their redevelopment. null nulllwaunulle, because the target area selected was so large, we included the area in a nullnullnullfoot buffer surrounding the comparable public housing development as the comparison area. null nullashington, D.C., only the actual area of the comparison public housing site was used. nullr each site, the research team also established an nullnterventionnullperiod, during which time re - development was occurring in earnest. nulle searched for displacement during and after this period. The selected intervention points are discussed in more depth in the following section. Weighted Displacement Quotient nullwers and nullhnson nullnullnull developed the nullnullmethod to assist displacement research where lengthy time series pre- and post-intervention are not available. They developed the nullnullto iden - tify the possible presence of displacement or diffusion, not the absolute sinull of any displacement or diffusion that might have occurred. The nullnullconsiders levels of crime in three areasnulltarget nullnull displacement nullnull and comparison or control nullnullareas. The quotient has two partsnullthe nullsplacement measure , which measures the change in crime in the displacement area relative to the change in crime in the control area over the same period, and is

109Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites the numerator shown in equation null and the success measure, which measures the success of an in- terventionnullhe reduction nullr slowed increasenullof crime in the target area relative to a comparison areanullnd is the denominator in equation null The displacement measure is divided by the success measure to calculate the final nullnull The full formula is provided below. WDQ = B t1 C t1 B t0 C t0 – A t1 C t1 A t0 C t0 –

nullnull where null tnull and null tnull are crime levels in the target area at times nulland null B tnull and B tnull are crime levels in the displacement area at times nulland null and C tnull and C tnull are crime levels in the control area at times nulland null nullterpretation of nullnullresults is straightforward. The value of the nullnullis often within or near the range of -nullto null nullrger values nullositive or negativenullindicate greater effects. The measure produces a positive score when a diffusion of benefits to the buffer area enullsts and crime levels have gone down. Positive values that are less than nullindicate diffusion, or a positive effect on crime levels in the displacement area, that is smaller than the effect of the intervention in the target area. nullalues greater than null indicate that the positive effects on crime levels were greater in the displacement area than in the target area. nullnegative score indicates displacement of crimenullvalues between nulland -null indicate that displacement did occur, but that the negative effect in the displacement area was less than the positive effect of the intervention in the target area. nullalues less than -nullindicate that the negative effects nullncreased crimenullto the displacement area were actually greater than the positive effects in the target area. Research Sites nullve redevelopment sites were included in this researchnullthree in nulllwaunulle and two in nullashington, D.C. Milwaukee, Wisconsin The three nulllwaunulle sites are located in the citynulls nullrth nullde. nulliolence in the area combined with declining housing stocnullmade the area ripe for redevelopment and it was targeted by nullCnull for improvement in the early nullnulls. nullCnullreceived nullnullmillion for redevelopment of the old nullghland Parnullsite nullnown as nullghland nullrdensnullighland nullmes after redevelopmentnullin nullnull and another nullnullnullmillion in nullnull for redevelopment of scattered public housing sites nullscattered sitesnull in the citynulls nulldtown neighborhood. nullthird site, Cherry Court, located within several blocnull of the nullghland Parnullsite, was redeveloped as well, but not with nullPnullnull funds. Cherry Court was ultimately included in the analysis because its redevelopment linullly would not have tanulln place at the time it did were it not for the co-occurring, nearby nullPnullnull activitiesnullit was redeveloped following the same design principles as used in nullPnullnull sitesnullit served as a base for service provision to residents in the scattered sites redeveloped under nullPnullnullnulland it was located very close to the nullghland Parnullsite and surrounded by the scattered sites, so it was reasonable to

110 Crime and Urban Form Canullll enullect that changes at Cherry Court could have affected crime and violence occurring at nullghland Parnulland among the scattered sites. The three nulllwaunulle sites were analynulld together because of their close geographic pronullmity to each other. nullCnullstaff said they made every effort possible to both nullep the number of moves for any resi - dent at two nullnce to move out of older housing and once to move bacnullin to newer housingnulland to nullep residents in the same neighborhood if they desired. This strategy was feasible in nulllwaunulle because a number of units in the immediate vicinity of the nullPnullnull sites were vacant when the nullPnullnull awards were made, and because the redevelopment pronullcts moved very quicnully. nullhibit nulldisplays a timeline of events associated with the redevelopment of the nulllwaunulle sites. The nullghland Parnulldevelopment was a superblocnullpublic housing development composed of two highrise towers and, on the same lot, null family units in barracnull-style multifamily buildings. The new site contains a new midrise building named nullghland nullrdens, newly constructed single- family homes on the few blocnull surrounding nullghland nullrdens called nullghland nullmes, and a newly built Cherry Court. nullcause the three redevelopment pronullcts in the study neighborhood nullighland nullrdens, nullghland nullomes, and Cherry Courtnull were located very close to each other, the research team decided to draw the target area boundary around all scattered sites, including Cherry Court and nullghland Parnull The selected site boundaries contained most of the units among which residents were shufnulld during the redevelopment process. null nulllwaunulle, therefore, we were still able to test for both modes of displacementnullith residents to their new addresses or to areas near the site itselfnullogether. nullhibit nullis a map of the nulllwaunulle sites included in this study, including the comparison area, nullestlawn. The comparison area was chosen based on recommendations from the nullCnulland its socioeconomic characteristics. Exhibit 1 Renullnulllonullent nullmeline, nulllnullunulle HnullM nullHousing nulltnullrity of tnull City of Milnullukeenull

111Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites Exhibit 2 nulllnullunulle Renullnulllonullent annullComnullnullson Sites Scattenullenullsites Chenully Counull Highlannullnullnullnullnsnullomes nullestlanull nullestlanull null000nulloot nullffenull Scattenullenullsites tanullget anullea Scattenullenullsites 1,000nulloot nullffenull Scattenullenullsites null000nulloot nullffenull

112 Crime and Urban Form Canullll Socioeconomic nulldicators nullea nullPE VnullSite nullestlanull nullea (snullanulle miles) 1.null 1.null Resinullntial nullnulllation null,nullnull 1nullnullnull nullnullcent of nullnulllation ages 1null1null 1null0 1nullnull nullnullcent of nullnulllation ages 1nullnull 1null1 nullnull nullnullcent of nullnulllation male null.null null.1 nullnullcent of nullnulllation Hisnullnic null0 nullnull nullnullcent of nullnulllation nullacnull null.null null.null nullnullcent of housing units nullcant 11.null null1 nullnullcent of nullnulllation high school gnullnullates (nullnull) null.null null.null nullnullcent of nullnulllation in lanullnullfonullce (null1null null.0 null.0 nullnullcent of householnull nulleceinullng nullnullic assistance 1nullnull nullnull nullnullcent of nullnulllation nulllonullnullnullnully lenulll null.null null.null nulll nullnull nullcnullmes, null0null nullnull1 1,nullnull nulliolent cnullmes, null0null nullnull nullnull nullonullnully cnullmes, null0null null0null 1,1null nullCrinull counts are renullrted for tnull scattered sites target area and tnull conullarison area nullestlanull nullus its nullnullnullfoot buffernull only, not for larger census blocks used for renullrting socioecononullc datanull Source of socioecononullc datanullUnullnullCensus nullreau, nullnull Source of crinull datanullMilnullukee nulllice nullnullrtnullnt Exhibit 3 nulllnullunulle Socioeconomic nullnullcatonull, null00 nullnullhibit null provides selected socioeconomic measures for nullnullnullnull for the area containing most of the scat- tered sitesnull for nullighland nullardens, nullighland nullomes, and Cherry Courtnull and for the comparison site. null nullcording to nullCnullstaff, before redevelopment toonullplace, loitering was common and youth were involved in delinquent behaviors, but staff reported limited narcotics activity at the nullghland site. null the scattered sites area, breanullins were the manullr complaint of residents. null the Cherry Court site, narcotics were more prevalent and the Cherry nullreet nullb, a local narcotics gang, was involved in much of the criminal activity in the area. Prostitution was also noted in the site before redevel- opment. nullllowing redevelopment, staff reported a significant decline in crime and safety issues in an around the site. The city as a whole, however, also enullerienced significant declines in crime at the end of the study period. These citywide decreases were accounted for in the displacement analyses through the use of the comparison area. null The comparison site actually comprises a nullnullnullfoot buffer around nullestlawn to accommodate the fact that the scattered sites are spread out, manullng the target area relatively large. null nullthough this site was renamed as part of the development, this report will refer to the site both pre- and post- development as Capitol nullteway nullts post-development namenullto reduce confusion among readers. null nullthough the full name of this site is nullthur Capper, in the tenull it will simply be referred to as Capper. Washington, D.C. Two nullPnullnull sites in nullashington, D.C., were selected for this researchnullthe Capitol nullteway site nullormerly nullst Capitol Dwellings and Capitol nulliew Planull nulland nullnull null and the nullthur Cappernull Carrollsburg site. null null nullashington, D.C., site definition was straightforwardnullhe sites had clearly

113Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites defined boundaries that were used in the current research. The boundaries included the immedi- ate area under redevelopment. nullch clearly defined and compact public housing developments resulted in sites that were much smaller than the nulllwaunulle site. nullhibit nullprovides a map of the two sites selected for this study and their comparison areas. Two main differences distinguished the nullashington, D.C. nullPnullnull enullerience from the nulllwau - nulle enullerience. nullrst, the redevelopment timeline in nullashington, D.C., was much longernullt toonull longer from the time of the award until newly built units were available for move-in than it did in nulllwaunulle. The long timeline meant that significant changes in individual family structure toonull place while redevelopment was progressingnullchildren might grow up and no longer live with the familynulladults may become parents and have more family members to house. These changes meant that the available redeveloped units might have been too large or too small for former residentsnull needs, so they would opt out of returning. nullecond, residents were scattered into other available units in the citynullwhile many received vouchers for other housing, including nonpublic housing, because of the tight rental marnullt at the time in nullashington, D.C., many residents were forced to move into other public housing nullPopnullin et al., nullnullnullnullnull. The limited availability of nearby rental housing meant that residents were moved to different areas of the city. These relocated residents often formed close ties to their new neighborhoods and chose not to be uprooted a second time, long after their initial move out of the redevelopment site. Exhibit 4 nullashington, nullC. Renullnulllonullent annullComnullnullson Sites Stnulleet nullin nulloanullay Highnully HOnull null site 1,000nulloot nullffenull null000nulloot nullffenull Canulltol nulltenully comnullnullson CanullenullCanullollsnullnullg comnullnullson Othenullnullnullic housing

114 Crime and Urban Form Canullll nullnally, particularly at the Cappernullarrollsburg site, the character of the neighborhood underwent enullreme change over the course of the redevelopment period. The neighborhood change stemmed not nullst from nullPnullnull, but also from other development in the area. nullr instance, many small businesses closed and were replaced by chain stores or higher end stores than were previously there, and a new baseball stadium and a number of high-end condominiums were built. nullcause we did not obtain information on where residents were relocated, the research team was unable to identify possible displacement nullnes that were noncontiguous to the nullPnullnull sites but that housed a large number of displaced nullPnullnull residents. The long timeline meant that many residents had already established themselves in the new neighborhoods and had lived there for several years by the time new units were available. null the end of the redevelopment timeline, many did not want to move bacnullto their previous neighborhoods even though they had been redeveloped. null nullashington, D.C., then, fewer residents of the new housing were residents of the sites before redevelopment. nullpitol Gatenullnull null nullgust nullnull, DCnull received nullnullnullmillion to redevelop the nullst Capitol Dwellingsnullapitol nulliew Planull site, which is located on the border of the northeast and southeast quadrants. The site sits on nullst Capitol nullreet, a manullr thoroughfare into nullashington, D.C., from Prince nullorgenulls County, nullryland, and it is adnullcent to a nulltro bus stop. The nullnullunit barracnull-style housing was built in nullnull and was null percent vacant at the time of the nullPnullnull application in nullnull nullCnull, nullnullnull The adnullcent Capitol Planull site consisted of two highrise buildings, with units in one build - ing reserved for seniors. null addition to the deteriorating physical conditions of the building, the layout of the site contributed to public safety problemsnullbuildings were isolated from the rest of the neighborhood, and winding streets and open spaces created locations on the interior of the site that were hard to police and provided convenient escape routes for criminals nullCnull, nullnullnull Drug use was also rampant nullopnulln et al., nullnullnull nullthough homicides in public housing were dropping throughout nullashington, D.C., in the late nullnulls, homicides at nullst Capitol actually increased in nullnull, contributing to the sitenulls reputation as one of nullashington, D.C.nulls most dangerous. nullhibit nulldisplays the timeline of redevelopment events for the Capitol nullteway site starting with the nullOPnull nullnull award in nullnullnullnull. nullesident relocation at Capitol nullateway began shortly after the award to DCnullnull was made and continued for appronullimately null years. The building reserved for senior residents was constructed first, with move-in starting in nullbruary nullnull. nullmily units, made up largely of townhouses, were open for occupancy beginning in nullptember nullnull. The new sites were designed to have no unit access through common areasnullall access to units was from the outside. nulln addition, enullensive lighting was installed, a small toddler playground was built, and the site did not include large outdoor areas for residents to congregate, such as a basnulltball court, to discourage loitering. nullhibit nullprovides socioeconomic indicators for the two nullashington, D.C. sites and their compari - son areas. The Capitol nullteway comparison site, nullrry nullrms, was selected based on input from the DCnull and socioeconomic characteristics to match the former nullst Capitol site.

Unknown

115Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites Exhibit 5 Renullnulllonullent nullmeline, Canulltol nulltenully, nullashington, nullC. Socioeconomic nulldicators nullea nullnulltol nulltenullnull nullrrnull nullrms nullnullernull nullrrollsbnullnull Snullhaxnull nulleenleanull nullrdens nullea (snullanulle miles) 0.1 0.0null 0.0null 0.0null Resinullntial nullnulllation nullnullnull 1,nullnull 1,nullnull 1,nullnull nullnullcent of nullnulllation ages 1null1null 10.null 1nullnull 10.null nullnull nullnullcent of nullnulllation ages 1nullnull nullnull 1null0 nullnull nullnull nullnullcent of nullnulllation male null.null null.null null.null null.null nullnullcent of nullnulllation Hisnullnic 0.null 1.1 0.0 1.null nullnullcent of nullnulllation nullacnull null.0 null.1 null.1 null.null nullnullcent of housing units nullcant null.null null1 nullnull nullnull nullnullcent of nullnulllation high school gnullnullates (nullnull) null.null null.null null.1 null.null nullnullcent of nullnulllation in lanullnullfonullce (null1null null.0 null.0 null.0 null.0 nullnullcent of householnull nulleceinullng nullnullic assistance 1nullnull null.null 1nullnull 1nullnull nullnullcent of nullnulllation nulllonullnullnullnully lenulll null.null null.null null.null null.null nulll nullnull nullcnullmes, null00null 1null null null 1null nulliolent cnullmes, null00null null null null null nullonullnully cnullmes, null00null null null null null nullCrinulls counts are renullrted for tnull target and conullarison areas only, not for larger census blocks used for renullrting socioecononullc datanull Source of socioecononullc datanullUnullnullCensus nullreau, nullnull Source of crinull datanullMetronulllitan nulllice nullnullrtnullnt nullasnullngton, nullCnull Exhibit 6 nullashington, nullC. Socioeconomic nullnullcatonull, null00

116 Crime and Urban Form Cahill Capper/Carrollsburg The Arthur Capper/Carrollsburg site, part of which dates to 1958, was located in the city’s southeast quadrant (nullpree, nullnullnull The site is separated fronullthe rest of the city by the nullutheast nulleeway, which runs near the northern part of the site and on the other side of which is the econonullcally betternullff Capitol nullll section of the citynullThe site consisted of barracnullnulltyle housing and a highrise for seniorsnull The site becanulle a hanullen for drugs and crinulle, and efforts to renonullate it in the 19nullnulls did not solnull the crinull problenull null nullnull, the neighborhood that was honull to Capper/Carrollsburg had one of the lowest nulldian inconull lenullls in the city (nullilgoren, nullnullnull null nullnull, nullnull was awarded nullnull9 nullllion to redenulllop the Arthur Capper/Carrollsburg site, and the city planned a onenullonullne replacenullnt of public housing unitsnullAt that point, city ofnullials and denulllopers had already started to direct redenulllopnullnt funds and plans toward the sonullalled nullear nullutheastnullbut little redenulllopnullnt had begunnullThree years later, the nullashington nulltionals decided to locate their new baseball stadiunullin the neighborhood, and new federal buildings had already been constructed in the areanullThe nullnull null redenulllopnullnt was nullst one part of a larger renulltalinulltion of the neighborhoodnull nullhibit nulldisplays the tinullline of redenulllopnullnt enullnts for the Capper/Carrollsburg site starting with the nullnull null awardnull null nulldenullloping the Capper/Carrollsburg site toonullthe longest of the Exhibit 7 Redevelopment Timeline, Capper/Carrollsburg, Washington, D.C. null nullte that nullch of the infornulltion collected about the Capper/Carrollsburg site and tinullline canull fronull wwwnulldlandnullonull null That website is nullintained by a resident of the area who has been docunullnting the changes in the nullar nullutheast since nullnull and who is well nullown by the nullnullnullnull fact, sonull nullnull staff nullnullers reconullended that pronullct staff nullsit the site for nullre detailed infornulltion about the changes that had tanulln place since the nullnull null awardsnull

117Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites five study sites. Phase nullof resident move-out of the barracnull-style units started in nullnull. The site was demolished piece by piece, as new units were built and began to be occupied. The new site contained a number of different elements, including townhouses and two midrise buildings for seniors. The buildings for seniors were the first to be finished and occupiednullconstruction on the townhomes followed. null the end of the study period nullnd of nullnullnull occupancy had begun at the townhomes but units were still available for sale. DCnull staff reported that at Cappernullarrollsburg, drug offenses in particular stayed local even dur - ing the redevelopment process. The perception of staff familiar with the area was that drug dealers simply moved a few blocnull away from the redevelopment site to maintain their enullsting drug marnullts. This manulls sense in light of the fact that the site is located nenull to a highway, providing potential drug customers with an easy entrynullnullt point to drug marnullts and dealers in the area. This phenomenon supports the idea of loonullng for immediate displacement around the site and, although this phenomenon was not identified specifically in nulllwaunulle or for Capitol nullteway, the linulllihood is strong that any drug marnullts in those two sites followed similar patternsnulloth are located near manullr thoroughfares or highways that would contribute to nulleping drug marnullts in the same vicinity as the sites themselves. nullhibit nullprovides socioeconomic indicators for the neighborhoods of the Cappernullarrollsburg site and its comparison area, nullphanullnulleenleaf nullrdens. The comparison site was selected based on recommendations from DCnull staff, and the two sites were very similar on the selected measures shown here in nullnull, prior to the nullPnullnull award. Results: Milwaukee The first step in analynullng the nullPnullnull redevelopment sites for displacement or diffusion was to enullmine the change in crime levels before and after the redevelopment efforts. nullhibit nullincludes line graphs of all crime in the four main study areas nullhe site, two displacement buffers, and com - parison areanullestlawn and its nullnullnullfoot buffernull The timeline suggests that crime in the site and two displacement areas dropped throughout the study period, from nullnull through nullnull. Crime in the comparison area remained relatively stable over the same period. The timeline demonstrates the long nature of the intervention periodnullhe period during which the site was redeveloped. null also illustrates that it is hard to identify a specific date on which the intervention started or ended. The analyses thus use varying lengths of pre- and post-intervention periods. The intervention periods, however, generally center around nullptember nullnull through nullvember nullnull, roughly the period of construction on nullghland nullrdens. nullthough significant construction on Cherry Court and the scattered sites homes continued after nullghland nullrdens was finished, this period was chosen based on the idea that once one pronullct within the larger target area was finished and occupants returned, the innullence of that redevelopment would start to be felt throughout the site, as residents became more aware and convinced of the changes that were tanullng place in their neighborhood. nullhibit nullprovides crime density maps for nullselected years in the study periodnullnullnull, nullnull, nullnull, and nullnull. The density maps were created using data covering a nullmile radius around the site to

118 Crime and Urban Form Cahill “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “” “” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ” “ 2009 ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ”“ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “ ” 2005 “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “” “” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ”“ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “ ” 2007 “” Exhibit 8 Density of Crime Over Time, Milwaukee (1 of 2)

119Cityscape Using the Weighted Displacement Quotient To Explore Crime Displacement From Public Housing Redevelopment Sites “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “” “” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ” “ 2009 ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ”“ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “ ” 2005 “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “” “” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ” “ ” “ ” “ ” “ ” “ ” “ ”“ ”“ ” “” “ ” “ ” “ ” “ ” “ ” “ ” “ ” 2007 “” Exhibit 8 Density of Crime Over Time, Milwaukee (2 of 2)

120 Crime and Urban Form Canullll capture any hot spots that might be occurring nearby but nullst outside the site boundaries. These maps were used purely for descriptive purposes, and they reveal that the density of crime inside the site appears to lessen with time. nullt spots outside the site boundaries, especially to the south, do remain, but they do decrease somewhat with time as well. nulle calculated the nullnullusing several different sets of pre- and post-intervention periods and loonulld for displacement during the intervention as well. The intervention period used was nullptember nullnull through nullvember nullnull. The nullnullis restrictive in its need for pre- and during-null post-intervention periods to be of the same length. Therefore, the timing of the intervention rela- tive to the start of the period covered by the data can affect the length of the periods enullmined for displacement. null nulllwaunulle, the intervention began in nullptember nullnull, only null months after the start of our period of data coverage nullanuary nullnullnull Therefore, we could not calculate the nullnull using pre- or post-intervention periods longer than null months. nulle calculated the nullnullfor four different time periods, specified in enullibit null The nullnullwas also calculated for all crime, personal crime, and property crime. nullhibit nullprovides the nullnullfor each of the time periods and buffers considered. The enullibit also provides the success measure, or the denominator of the nullnull null the success measure was positive, crime increased over the period and a search for displacement or diffusion was not war- ranted. The nullnullwas thus not reported for those periodsnullreas for which the success measure was positive. The enullibit highlights those periodsnullreas where we found displacement or diffusion to be larger than the direct effects in the target area nullhat is, they were greater than nullor less than -null. null the all crimes category, the success measure was negative, indicating a drop in crime in the target area over the time period of interest, for only one set of time periods studiednullhe null-month pre- intervention period and the null-month intervention period. null that period in the nullnullnullfoot buffer, the nullnullwas positive but less than null indicating that diffusion did tanull place, but it was less than Exhibit null Prenullost nullnnullh t null nullrenullntervention Periodnull t 1 nullntervention or Postnullntervention Periodnull nullnulle onull nullime Snullcess Measnulle nullnull 1nullnullnulloot nullnullnullr 2nullnullnulloot nullnullnullr nulleightenullnullsnullacement nullotient Results, nulllnullunulle Senullemnullnullnull0null nullgust null0null nullcemnullnullnull0null nullnullmnullnullnull0null nulll 0.nullnull null null 1nullmonths nullnullonal 0.01null null null nullonullnully null0.1null 0.nullnull 1.null1 nully null0null nullgust null0null nullcemnullnullnull0null nullnullch null0null nulll 0.nullnull null null 1nullmonths nullnullonal null0.0null null0.nullnull null1.nullnull nullonullnully null0.1null 0.nullnull 1.nullnull nully null0null nullgust null0null Senullemnullnullnull0null nullnullmnullnullnull0null nulll null0.0null 0.1null null0.null0 1nullmonthsnull nullnullonal 0.0null null null nullonullnully null0.1null 0.0null 0.1null nullnullch null0null nullgust null0null nullcemnullnullnull0null nully null0null nulll 0.1null null null 1nullmonths nullnullonal null0.0null 0.10null null0.01null nullonullnully null0.1null 0.nullnull 1.nullnull Wnull nullWeignulled nullsnullacenullnt nullotientnull nullearcnulld for disnullacenullnt during internullntion nullriodnull nulltenullnulle internullntion nullriod nulls Senullenuller nullnull tnullougnullnullnullnuller nullnullnull

121Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites the effect in the redevelopment site. null the nullnullnullfoot buffer during that time period, however, the nullnullwas negative but greater than -null indicating that a small amount of displacement may have occurred. nulll success measures for the all crimes category nullhether positive or negativenullwere close to null indicating very small changes in crime over the period. nullr personal crimes, the success measure was positive in two of the time periods searched. nullr the null-month pre- and post-intervention periods, the nullnullwas negative in both buffer areas. nullr the nullnullnullfoot buffer, it was less than -null indicating that displacement did occur, and that the increase in personal crime in the nullnullnullfoot buffer was greater than the decrease in the target area itself. nullr the null-month pre- and post-intervention periods, both nullnull were close to null indicating that little to no change occurred in either buffer area. nullr property crime, the nullnullindicated that a diffusion of benefits from the redevelopment site to the area within nullnullnullfoot of the site toonullplace, and those benefits were about equal to the direct effects of the redevelopment in the site itself. This finding was true for all but one set of time periods studiednull the nullnull-month pre-intervention period and the nullnull-month intervention period, for which the nullnullwas near null indicating little to no change in crime occurred in the buffer area. To summarinulle, the nullDnull effort in nullilwaunullee had minulled results, with all crimes and personal crimes showing either no change or some signs of minimal crime displacement. nullr property crimes, however, this methodology suggested a diffusion of benefits. nullr all crime types and all sets of time periods analynulld, the results were strongest in the nullnullnullfoot buffer. Results: Capitol Gateway, Washington, D.C. nullhibit nullincludes line graphs of all crime in the four main study areas for the Capitol nullteway sitenulldotted lines delineate the different study periods. Crime in the site appeared to drop signifi - cantly beginning in nullnull, and crime in the two buffer nullnes also decreased, although not to the same degree as in the target area. Crime in the comparison area remained relatively stable until nullnull, at which point monthly crime levels started rising. nullhibit null provides crime density maps for nullselected years in the study periodnullnullnull, nullnull, nullnull, and nullnull. The density maps were created using data covering a nullnullmile radius around the site and they reveal that the density of crime inside the site appeared to lessen with time. nullst of the hot spots disappeared by nullnull, but the nullnull map showed a slight resurgence in the crime densi - ties, especially in the nullnullnullfoot buffer on the nullashington, D.C. side. nullter nullnull, however, the target area itself had a very low density of crime. The initial intervention period that we used was October nullnullnullnull through nulluly nullnullnullnull, which was roughly the period of construction of the senior building and the family units. nulle calculated the nullnullus - ing several different sets of pre- and post-intervention periods, and loonulld for displacement during the intervention as well. nullhibit null provides the nullnullfor each of the time periods and buffers considered. The enullibit also provides the success measure, or the denominator, of the nullnull The nullDnull results for Capitol nullateway across all types of crime and time periods were more consistent than they were for nulllwaunulle. nulll but one success measure was negative, indicating that for the most part, both property and personal crimes decreased in the redevelopment site over the study

122 Crime and Urban Form Cahill District boundary District boundary District boundary District boundary 2002 2004 2006 2008 Legend Other public housing Capitol Gateway 1,000-foot buffer 2,000-foot buffer Incidents per square kilometer 0–325 326–650 651–975 976–1,300 1,301–1,625 1,626–1,950 Legend Other public housing Capitol Gateway 1,000-foot buffer 2,000-foot buffer Incidents per square kilometer 0–325 326–650 651–975 976–1,300 1,301–1,625 1,626–1,950 Prince George’s County, MD Prince George’s County, MD Prince George’s County, MD Prince George’s County, MD 0 0.5 10.25 Km Exhibit 10 Density of Crime Over Time, Capitol Gateway, Washington, D.C. (1 of 2)

123Cityscape Using the Weighted Displacement Quotient To Explore Crime Displacement From Public Housing Redevelopment Sites District boundary District boundary District boundary District boundary 2002 2004 2006 2008 Legend Other public housing Capitol Gateway 1,000-foot buffer 2,000-foot buffer Incidents per square kilometer 0–325 326–650 651–975 976–1,300 1,301–1,625 1,626–1,950 Legend Other public housing Capitol Gateway 1,000-foot buffer 2,000-foot buffer Incidents per square kilometer 0–325 326–650 651–975 976–1,300 1,301–1,625 1,626–1,950 Prince George’s County, MD Prince George’s County, MD Prince George’s County, MD Prince George’s County, MD 0 0.5 10.25 Km Exhibit 10 Density of Crime Over Time, Capitol Gateway, Washington, D.C. (2 of 2)

124 Crime and Urban Form Canullll period. nullr both types of crime, the null-month pre- and post-intervention periods produced nullnull that were positive and greater than null indicating that a diffusion of benefits toonullplace and that the decreases in crime in the buffer areas were at least as strong as the effects in the target area itself. nullen we compared crime levels for the null months before and null months during the intervention, we found minulld results. nullr all crime and personal crime, the nullnull were positive and at or greater than null indicating that a diffusion of benefits toonullplace immediately after the start of the intervention nullhile the redevelopment activities were continuingnull nullr property crimes, the success measure was negative but very near null The nullnull for property crimes in both buffer areas were negative and very large. null their presentation of the nullnull nullwers and nullohnson nullnullnullnullnullnull cautioned that the measure should not be used to quantify the absolute amount of displacement that occurred, only the amount relative to the target areanull therefore, the large nullDnull values should not be alarming. The null-month pre- and post-intervention periods yielded positive nullnull, providing additional evidence that a diffusion of benefits from the Capitol nullteway site to nearby areas toonullplace. The results for the null-month periods for the pre-intervention period and during the intervention, however, revealed that some displacement might have occurrednullthe all crime category had nega - tive nullnullvalues less than -null The personal crime category had small positive nullnullvalues, and the property crime category actually had a positive success measure, so no nullnull were calculated for that crime category. Tanulln together with the property crime results discussed above, it appears that any positive effects of the intervention on neighboring areas nullhat is, diffusion of benefitsnull lagged behind the actual intervention, possibly for several years. There may have been some crime displacement early on, but that disappeared as the redevelopment progressed. null addition, the values of the nullnullwere larger for the nullnullnullfoot buffer, so the effects did not wash out toward the outer boundaries of the study area. Exhibit 11 Prenullost nullnnullh t null nullrenullntervention Periodnull t 1 nullntervention or Postnullntervention Periodnull nullnulle onull nullime Snullcess Measnulle nullnull 1nullnullnulloot nullnullnullr 2nullnullnulloot nullnullnullr nulleightenullnullsnullacement nullotient Results, Canulltol nulltenully, nullashington, nullC. Octonullnullnull01null Senullemnullnullnull0null nullgust null0null nullly null0null nulll null1.null nullnull nullnull 1nullmonths nullnullonal null1.0null 1.null 1.null nullonullnully null1.null nullnull nullnull Octonullnullnull01null Senullemnullnullnull0null Octonullnullnull0null Senullemnullnullnull0null nulll null0.null 0.null 1.null 1nullmonthsnull nullnullonal null0.null 0.null 1.null nullonullnully null0.0null nullnull.00 nullnull.00 nullnulll null01null Senullemnullnullnull0null nullgust null0null nullnuanull null0null nulll null1.0null nullnull nullnull 1nullmonths nullnullonal null0.null 1.11 1.null nullonullnully null1.null nullnull nullnull nullnulll null01null Senullemnullnullnull0null Octonullnullnull0null nullnullch null0null nulll null0.null null0.null null1.1null 1nullmonthsnull nullnullonal null0.null 0.null 1.null nullonullnully 0.null null null Wnull nullWeignulled nullsnullacenullnt nullotientnull nullearcnulld for disnullacenullnt during internullntion nullriodnull nulltenullnulle internullntion nullriod nulls October nullnull tnullougnullnullly nullnullnull

125Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites To summarinull, the nullnulleffort for Capitol nullteway had strong results indicating a diffusion of benefits for most time periods and areas that was delayed until after the end of the intervention. The enulleptions were the null-month pre- and during-intervention periods for property crime and the null-month pre- and during-intervention periods for all crime, during which time some displacement of those crimes may have occurred. That effect, however, had disappeared by the end of the redevelopment period, by which point diffusion of benefits was observed. Results: Capper/Carrollsburg, Washington, D.C. nullhibit nullincludes line graphs of all crime in the four main study areas nullhe site, two displacement buffers, and the comparison areanullnullyphanullnullnullreenleaf nullardensnull and dotted lines delineate the different study periods selected by the research team. Crime in the site dropped significantly in mid-nullnullnull in the two buffers, it also dropped starting at the same point, although not to the same degree. The Cappernullarrollsburg redevelopment had the longest timeline of the three sites under enullmina - tion. The intervention periods generally center around the nullly nullnull-through-December nullnull period, which is roughly the period of construction of the first senior building. nullhibit null provides crime density maps for nullselected years in the study periodnullnullnull, nullnull, nullnull, and nullnull. The density maps were created using data covering a nullnullmile radius around the site and reveal that the density of crime inside the site appeared to lessen with time. null nullnull, most of the crime hot spots appeared to have moved outside of the nullnullnullfoot buffer area. nullter nullnull, the target area itself had a very low density of crime, but a stable hot spot enullsted nullst to the northwest of the site that remained there throughout the study period. The nullnullwas calculated for four different time periods and in two buffers. nullhibit null provides the nullnullfor each of the time periods and buffers considered. The enullibit also provides the suc - cess measure, or the denominator, of the nullnull nullnull were only calculated where the success measure was negative. The nullnullresults for the Cappernullarrollsburg site were similar to those observed for Capitol nullte - way. nulll success measures were negative, confirming that crime decreased over the study period. The results consistently indicated that a diffusion of benefits toonullplace from the site, with two enulleptions. nullr personal crimes during the first null months after the intervention period, the nullnull was negative. The same was true for the null-month period following redevelopment for personal crimes. This pattern is similar to that observed for property crimes in Capitol nullteway, where a displacement effect was noted soon after the intervention started, the effect, however, disappeared with time following the intervention. null is unclear why this result occurred, but one thing to note is that in both of these instances, the success measure was very low, which could very well have snullwed the results. One other factor to note in interpreting these findings is that other redevelop - ment pronullcts were tanullng place in the larger area, and they may have accounted for the apparent diffusion effects noted in this site. To summarinull, the nullnulleffort for Cappernullarrollsburg had the strongest, most consistent results of the three sites. The nullnull showed a diffusion of benefits across several different time periods, and in both the nullnullnull and nullnullnullfoot buffers. Contenullual nullowledge of the greater redevelopment area, however, invites caution in interpreting these findings.

126 Crime and Urban Form Cahill 2002 2004 2006 2008 Legend Capper/Carrollsburg 1,000-foot buffer 2,000-foot buffer Comparison area Incidents per square kilometer 0–275 Other public housing 276–550 551–825 826–1,100 1,101–1,375 1,376–1,650 Legend Capper/Carrollsburg 1,000-foot buffer 2,000-foot buffer Comparison area Incidents per square kilometer 0–275 Other public housing 276–550 551–825 826–1,100 1,101–1,375 1,376–1,650 0 0.5 10.25 km Exhibit 12 Density of Crime Over Time, Capper/Carrollsburg, Washington, D.C. (1 of 2)

127Cityscape Using the Weighted Displacement Quotient To Explore Crime Displacement From Public Housing Redevelopment Sites 2002 2004 2006 2008 Legend Capper/Carrollsburg 1,000-foot buffer 2,000-foot buffer Comparison area Incidents per square kilometer 0–275 Other public housing 276–550 551–825 826–1,100 1,101–1,375 1,376–1,650 Legend Capper/Carrollsburg 1,000-foot buffer 2,000-foot buffer Comparison area Incidents per square kilometer 0–275 Other public housing 276–550 551–825 826–1,100 1,101–1,375 1,376–1,650 0 0.5 10.25 km Exhibit 12 Density of Crime Over Time, Capper/Carrollsburg, Washington, D.C. (2 of 2)

128 Crime and Urban Form Canullll Discussion The results from nulllwaunulle were the most minulld, with evidence of crime displacement provided by some of the nullnull. The results from Capitol nullteway and Cappernullarrollsburg were much more consistent than those from nulllwaunulle in that the nullnull for different areas and time periods produced similar results. nullhibit null provides a summary of the nullnullvalues for each site for each buffer null,nullnulland nullnullnullfeetnull This enullibit is simply the count of all nullnull that fell into each range nulless than -null -nullto null and greater than null. nullmbers smaller than -nullindicate displacement and numbers greater than nullindicate diffusion of benefits. This enullibit does not differentiate between different types of crime. Exhibit 14 nullnullnullr Distance nullnull1 null1 to 1 null1 Summanull of nulleightenullnullsnullacement nullotient Results nullnulloss nulll Sites nulllnullunulle 1,000 feet 0 null 0 null000 feet 1 null null Canulltol nulltenully 1,000 feet 1 null null null000 feet null 0 null CanullenullCanullollsnullnullg 1,000 feet 0 null 10 null000 feet 1 null null nullotal 1,000 feet 1 1null 1null null000 feet null null null nulll null 1null null Exhibit 13 Prenullost nullnnullh t null nullrenullntervention Periodnull t 1 nullntervention or Postnullntervention Periodnull nullnulle onull nullime Snullcess Measnulle nullnull 1nullnullnulloot nullnullnullr 2nullnullnulloot nullnullnullr nulleightenullnullsnullacement nullotient Results, CanullenullCanullollsnullnullg, nullashington, nullC. nullly null0null nullne null0null nullcemnullnullnull0null nullnullmnullnullnull0null nulll null1.0null null1null nullnullnull 1nullmonths nullnullonal null0.null1 0.1null nullnullnullnull nullonullnully nullnullnullnull null11null nullnull0 nullly null0null nullne null0null nullly null0null nullne null0null nulll null0.nullnull null00null 1.nullnull 1nullmonthsnull nullnullonal null0.null1 1.nullnull 1.nullnull nullonullnully null1.null1 nullnullnull nullnullnull nullnuanull null0null nullne null0null nullnullch null0null nullgust null0null nulll null1.0null null0null null100 1nullmonths nullnullonal null0.nullnull 0.nullnull null0.nullnull nullonullnully null1.nullnull null0null nullnull0 nullnuanull null0null nullne null0null nullly null0null nullcemnullnullnull0null nulll null0.nullnull 1.nullnull 0.nullnull 1nullmonthsnull nullnullonal null0.nullnull 1.0null 1.nullnull nullonullnully null1.nullnull null10null 0.null1 Wnull nullWeignulled nullsnullacenullnt nullotientnull nullearcnulld for disnullacenullnt during internullntion nullriodnull nulltenullnulle internullntion nullriod nulls nullly nullnull tnullougnullnullbruary nullnullnull

129Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites Only about one-half of the success measures for the different crime typenulluffernullime period combi - nations studied in nulllwaunulle were negative, indicating that crime decreased in the target area nullee enullibit null. nulll of the success measures, however, were near null indicating that the effectsnullhether positive or negativenullere minimal. The fact that the success measures were all positive for the null-month pre- and post-intervention nullnullindicates that the redevelopmentnulls reduction effects on crime grew with time and were stronger later in the study period. The nullnull indicated that some personal crimes were displacednulltarting nullst after the intervention period endednullut that effect got smaller with increased time from the intervention period. nullhibit null indicates that the Capitol nullteway site had more large and positive nullnull than neutral or negative nullnull, indicating that diffusion of benefits occurred. The nullnull indicated that the results varied with type of crime but the success measures were all negative, indicating a drop in crime in the target area. Property crimes in particular showed evidence of displacement early in the redevelopment period, but after the redevelopment period, showed strong evidence of diffusion of benefits. The initial displacement may have been an immediate reaction to the redevelopment activities that dissipated after all residents moved out and the site was empty. The success of the redevelopment efforts is nulldged more accurately by considering crime at the site when residents move bacnullin, and during that period, the nullnull all pointed to a diffusion of benefits. nullsults for personal crime and all crime also showed evidence of a diffusion of benefits, and that diffusion started immediately after redevelopment. null nullashington, D.C., more than in nulllwaunulle, the residents of the redeveloped site were linullly different than those who moved out before redevelopment. null all sites, and in nullPnullnull in general, criteria to move into a redeveloped or new unit are much stricter than for other public housing. This results in residents who are more linullly to be nulluccessfulnullat maintaining a housing unit, finding and maintaining employment, or participating in governance of the development nulls is required in nulllwaunullenull nullthough we were unable to collect any data on residentsnullcharacteristics, we assume that with these nullnds of stricter resident criteria in all three sites came less criminally inclined residents, and residents who had higher stanulls in maintaining the safety and quality of their housing. The results we observed in the Capitol nullateway site, then, where diffusion of benefits increased after residents moved bacnullin, are linullly one manifestation of the stricter resident criteria. nulll of the success measures calculated as part of the nullnullanalysis for Cappernullarrollsburg were negative, indicating that crime did drop in the site during and after redevelopment. nullarly all of the nullnull were positive, indicating a diffusion of benefits toonullplace from the target area to the buffers. nullny of those nullnull were also greater than null pointing to a large positive effect in the buffer areas nullee enullibit null. Two important details of the Cappernullarrollsburg site should be nullpt in mind when interpreting the results. nullirst, the timeline for both of the nullashington, D.C. sites, including CappernullCarrollsburg, was much longer than for the nulllwaunulle site, meaning those sites had a longer time period with fewer residents on site. nullven the lacnullof residents on site, combined with the construction efforts at each sitenullanullng the site unattractive in terms of criminal opportunitynullt is not surprising that crime dropped more quicnully at the nullashington, D.C. sites than in nulllwaunulle, where residents were on site throughout redevelopment.

130 Crime and Urban Form Canullll nullsecond important feature of the Cappernullarrollsburg site, mentioned above, was the large amount of redevelopment not associated with nullPnullnull that was going on in the area immediately surrounding the site. These other redevelopment efforts should have served to decrease criminal opportunity at the site, and prevent much displacement from tanullng place to the buffer nullnes sur - rounding the Cappernullarrollsburg site. Conclusion nullst previous displacement studies have focused on interventions with problem-oriented policing strategies, such as enhanced enforcement in a hot spot or small geographic area. nulle should not ignore the difficulties with applying the techniques and theories from that type of study to one linull this where the intervention is long-term and tanulls place over a large geographic. nullrst, identifying the specific period of intervention is difficultnulln each site, we did our best to use the redevelopment timeline to manull educated decisions about when the most intense changes on the site occurred and used that period as our intervention. nullcond, choosing the boundaries of the sites proved difficult, especially in nulllwaunulle, where one redevelopment pronullct bled into another and the redevelopment process for each building or unit was intertwined with the others. nulle chose a large target site to encompass all redevelopment activities in nulllwaunulle, but the results indicated that there may have been differential effects from the redevelopment of each portion of the larger site nullor enullmple, the effects of changes to Cherry Court may have been different from the effects of changes at nullghland nullrdensnull null nullashington, D.C., public housing site definition was more straightforward, but for Cappernullarrollsburg, nearby redevelopment efforts made the actual boundaries of redevelopment efforts difficult to identify. nullr practitioners especially, we suggest that the nullnullis one of the most accessible and appropriate methods for studying displacement. null could easily be implemented by a housing authority or other practitioner loonullng to better understand displacement from public housing in his or her city. The nullnullrequires a low level of resource commitmentnulldifferent parameters can easily be tested by anyone with basic arithmetic aptitude, and enullensive data holdings over several years are not necessary. nulle suggest that the nullnullbe used as a useful but descriptive and intermediate tool in studying displacement. The results of the nullnullcan be used to inform enullectations of more rigor - ous statistical testing but cannot be relied upon solely for quantifying displacement or diffusion. nullthough this research has shown that the nullPnullnull effort has significantly positive effects on crime, especially in the target sites themselves, not all credit for the changes in the surrounding areas can be directly attributed to nullPnullnull. nullatistically, we were unable to control for all other redevelopment efforts that were tanullng place in the areas surrounding the target sitesnullin nulllwau - nulle, for instance, the city assembled non-nullPnullnull funding for the redevelopment of Cherry Court, which was in the neighborhood of the two nullPnullnull sites nullighland nullrdensnullomes and the scat - tered sitesnull in nullashington, D.C., the area surrounding the Cappernullarrollsburg site concurrently underwent significant redevelopment with non-nullPnullnull funds. nulle did not quantify these other efforts, and thus can only suggest that nullPnullnull appeared to have a positive effect in the buffer areas. null these cases, however, the nullPnullnull efforts can be seen as a catalyst for, or an early stage of, longer horinulln redevelopment plans for a neighborhood. null the traditional sense of diffusion

131Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites of crime reduction benefits from a site, then, nullPnullnull cannot be given direct credit. null the sense that nullPnullnull funds allow a city to target the greater neighborhood for additional redevelopment, however, nullPnullnull can be credited with a diffusion of benefits to areas outside the site. To the enullent that the three nullPnullnull sites in two cities are representative of other actual and pos - sible nullPnullnull sites, the results are applicable to other public housing sites undergoing this type of large-scale redevelopment, especially given the comparability of results we found across sites. The consistency with which we found evidence of diffusion from the sites is an indication that rede- velopment under nullPnullnull does indeed lead to diffusion of crime reduction, whether via changes directly attributable to nullPnullnull in the target area or indirectly via encouraging additional invest - ment in the larger neighborhood, leading to additional redevelopment efforts in areas surrounding the nullPnullnull site itself. null addition, including the three sites allowed for, at the nulllwaunulle sites, a purer test of the physical site designs because residents were largely the same before and after rede- velopmentnullat the Capitol nullteway site, an enullmination of changes in a site where little concurrent redevelopment was tanullng place outside the sitenulland, at the Cappernullarrollsburg site, an enullmina - tion of effects where a massive amount of additional development was tanullng place. The variety presented by the sites manulls the results applicable to a number of other, similar sites. nullditional research in this vein that confirms the results of this study would add to the case presented in this article for the positive effects of nullPnullnull on target sites and on surrounding neighborhoods. Despite these challenges, studying displacement from public housing is an important undertanullng, and the possibility of displacement should be considered by housing authorities either already undertanullng large-scale public housing redevelopment pronullcts or considering whether to start such an effort. nullthough this research showed that diffusion of benefits is linullly from redeveloped public housing, more wornullof this typenullnullloring different options for target area boundaries, intervention periods, and displacement areasnullan provide more evidence of the best approaches to this type of effort and inform housing authorities of the most efficient ways to include studies of displacement and diffusion in their redevelopment efforts. nullnullonulledgnullnts The author thannull the staff of the nullusing nullthority of the City of nulllwaunulle, who acted as nullnd hosts and enullellent resources of information both during and after a visit to gather information on nullPnullnull in that city. The staff of the District of Columbia nullusing nullthority were also enullremely helpful in providing information on nullPnullnull sites under redevelopment in nullashington, D.C. The author greatly appreciates the help from the staffs of those two housing authorities. The author also thannull the nulllwaunulle Police Department and the nulltropolitan Police Department nullashington, D.C.nullfor providing their crime incident data for this research. The nullnull Department of nullstice nullOnull, nulltional nullstitute of nullstice nullnullnull sponsored this study on crime displacement from public housing. This article was prepared under cooperative agreement nullnullnullnullCnullnullnull with the nullnull Opinions enullressed in this article are those of the author and do not necessarily represent the official position or policies of the DOnullor the nullban nullstitute, its trustees, or its funders.

132 Crime and Urban Form Canullll nulltnullr nullagan Cahill is a research associate in the nullstice Policy Center at The nullban nullstitute, an inde - pendent research organinulltion in nullashington, D.C. References nullrclay, Paul, nullnnifer nullcnulley, Paul null nullantingham, Patricia null nullantingham, and Terry nullinn- nullates. nullnull. nullreventing nullto Theft in nullburban nullancouver Commuter nulltsnullnullfects of a nullnull Patrol.nullnull nulleventing nullss nullransit Crime , edited by nullnald null. Clarnull. nullnsey, nullnullCriminal nullstice Pressnullnullnullnullnull nullwers, nullte null, and nullane D. nullhnson. nullnull. nulleasuring the nullographical Displacement and Diffusion of nullnefit nullfects of Crime Prevention nulltivity,null nullurnal onullnullantitative Criminology nullnull nullnullnullnull nullaga, nullthony. nullnull. nullhe nullfects of nullt nullot Policing on Crime,null nullnals onulltnull nullerican nullanullmy onullnulllitical annullSocial Science nullnullnullnullnull nullaga, nullthony, David nulleisburd, nullin nullaring, nullrraine nulleen nullnullrolle, nullilliam nullelman, and nullancis nullnullwsnull. nullnull. nullroblem-Oriented Policing in nulliolent Crime Placesnullnullnullndominulld Controlled nullperiment,null Criminology nullnullnullnullnullnull nullaga, nullthony null, and nullenda null nullnd. nullnull. nullolicing Crime and Disorder nullt nullotsnullnull nullndominulld Controlled Trial,null Criminology nullnullnullnullnullnull nullantingham, Paul null, and Patricia null nullantingham. nullnull. nullnvironment, nullutine, and nulltuationnull Toward a Pattern Theory of Crime.nullnull Routine nulltivity annullRational Cnullicenullnullvances in Criminological nulleory , Volnullnull , edited by nullrcus nulllson and nullnald null. Clarnull. nullw nullunswicnull nullnull Transaction Publishersnullnullnullnullnull Castells, nullna. nullnull. nullOPnullnull nullighborhood nullillover nullfects in nullltimore,null Cityscape null nullnull nullnullnull Cornish, Derenull and nullnald null. Clarnull. nullnull. nullnderstanding Crime Displacementnullnull nullplication of nulltional Choice Theory,null Criminology nullnullnullnullnullnull Denullances, Carol null, and nulleven null nullith. nullnull. nullrceptions onullNeignullornullonullCrime, 1nullnull Special Report. Technical report prepared for the nullnull Department of nullstice, nullreau of nullstice nullatistics. nullashington, DCnullnullnull nullvernment Printing Office. District of Columbia nullusing nullthority nullCnullnull nullnull. nullusing nullportunities nullr nullople Everywnullre nullnullE Vnull Revitalinulltion nullplicationnullNew East Capitol . nullplication to nullnull Department of nullusing and nullban Development for nullPnullnull funds. nullashington, DC. Dunworth, Terence, and nullron nulliger. nullnull. nullugs annullCrime in nullblic nullusingnullnullnullreenullity nullalysis . Technical report prepared for the nullnull Department of nullstice, Office of nullstice Programs. nullashington, DCnullnullnull nullvernment Printing Office.

133Cityscape Using tnull Weignulled nullsnullacenullnt nullotient nullo Exnullore Crinull nullsnullacenullnt nullonullnullblic Housing Redenulllonullent Sites Dupree, nullcqueline. nullnull. nullemolition nullgins on nullilding null One null nullnullly To nullss,null nulle nullasnullngton nullst , nullvember null. nullgan, nullffrey, and nullrth Davies. nullnull. nullrime in Public nullusingnullTwo-nullay Diffusion nullfects in nullrrounding nullighborhoods.nullnull nullalynullng Crime nulltternsnullnullontiers onullnullactice , edited by nullictor nullldsmith, Philip null nullnullire, nullhn null nullllennullpf, and Timothy null nullss. Thousand Oanull, Cnull nullge Publicationsnullnullnullnullnull nullsburg, nullnda, nullsan Popnulln, and nulletchen nullcnull. nullnull. null nullstorical annullBaseline nullsessment onullnullnull Vnull , Volnull1 . Technical report prepared for the nullnull Department of nullusing and nullban Develop ment. nullashington, DCnullnullnull nullvernment Printing Office. nullitsch, nullic null, Tory null Caeti, and nullbert null. Taylor. nullnull. nullang nullppression Through nullturation Patrol, nullgressive Curfew, and Truancy nullforcementnullnullnullasi-nullperimental Test of the Dallas nullti- nullng nullitiative,null Crime nullnulllinnullency nullnullnullnullnullnull nullbor, Thomas. nullnull. nullrime Displacement and nulltuational PreventionnullToward the Development of nullme Principles,null Cananullan nullurnal onullCriminology nullnullnullnullnull nullnull nullnull. nullhe Crime Displacement nullpothesisnullnull nullpirical nullamination,null Crime null nulllinnullency nullnullnullnullnullnull nullmilton-nullith, nullall. nullnull. nullnticipated ConsequencesnullDeveloping a nullrategy for the Targeted nullasurement of Displacement and Diffusion of nullnefits.nullnull Crime nullevention StunullesnullEvaluation nullr Crime nullevention, Volnull1null , edited by nullcnullTilley. nullnsey, nullnullCriminal nullstice Pressnullnullnullnull nullnes, nulleg. nullnull. nulling Spatial nullalysis nullo Enullmine nullsplacement in an nullban nullusing nullvelopmentnull Technical report prepared for the nulliversity of nullryland, College Parnull nullwton, nullian null, nulllph null Taylor, and nullthony null nullongo. nullnull. nullolice Officers on Drug Corners in Philadelphia, Drug Crime and nulliolent Crimenullnulltended, Diffusion and Displacement nullpacts,null nullstice nullarterly nullnullnullnullnullnull nullwman, Oscar. nullnull. Creating nullnullnsible Space . nullashington, DCnullnullnull Department of nullusing and nullban Development. nullnull nullnull. nullnullnsible SpacenullCrime nullevention nullrougnullnullban nullsign . nullw nullornull nullcmillan. Popnulln, nullsan null, nulluce nulltnull nullry null Cunningham, nullren D. nullown, nullremy nullstafson, and nullrgery null Turner. nullnull. nullnullcanull onullnullnull Vnull Researcnullnullnnullngs annullnulll icy Cnullllenges . Technical report prepared for the nullban nullstitute and the nulloonullngs nullstitution. nullashington, DCnullnullban nullstitute. Popnulln, nullsan null, Diane nullvy, nullura nullrris, nullnnifer Comey, nullry Cunningham, nullrry nullron, and nullilliam nulloodley. nullnull. nullnull Vnullnullnel StunullnullBaseline Report . Technical report prepared for the nullban nullstitute. nullashington, DCnullnullban nullstitute. Pyle, nullrald null. nullnull. nullpatial and Temporal nullpects of Crime in Cleveland, Ohio,null nullerican Benullvioral Scientist nullnullnullnullnullnull

134 Crime and Urban Form Canullll nullpetto, Thomas null nullnull. nullrime Prevention and the Displacement Phenomenon,null Crime null nulllinnullency nullnullnullnullnullnull nullerman, nullrry. nullnull. nullolice Cracnullownsnullnullitial and nullsidual Deterrence.nullnull Crime annullnullsticenull nullReview onullResearcnull , Volnull12 , edited by nullchael Tonry and null nullrris. Chicagonullnulliversity of Chicago Pressnullnull nullnull Turbov, nullndy, and nullalerie Piper. nullnull. nullnull Vnullannullnullnullnullnullnance RenullvelopmentsnullnullCatalyst nullr NeignullornullonullRenewal . Technical report prepared for the nulloonullngs nullstitution. nullashington, DCnull nulloonullngs nullstitution. nullnull Department of nullusing and nullban Development nullnullnull nullnull. null tnull Crossnullrenullnulle nullpact onull nulln Violence on nullblic nullusing Communities . Technical report prepared for the nullnull Department of nullusing and nullban Development. nullashington, DCnullnullnull nullvernment Printing Office. nulleisburd, David, nullura nullycoff, nullstin nullady, nullhn null nullnull nullshua C. nullnnulle, and nullannullnullnullwsnull. nullnull. nulloes Crime nullst nullve nullound the CornernullnullControlled nulludy of nullatial Displacement and Diffusion of Crime Control nullnefits,null Criminology nullnullnullnullnullnull nullilgoren, Debbi. nullnull. nullll nullound the nullll, a nullw nulla,null nulle nullasnullngton nullst , October null nullelenbach, nullan, and nullchard nulloith. nullnull. nullOPnullnull and nullighborhood nullonomic Devel opmentnull The nullportance of nullcal nullrnullt Dynamics,null Cityscape null nullnull nullnullnull.

Unknown

135Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Do Vouchers Help Low- Income Households Live in Safer Neighborhoods? Evidence on the Housing Choice Voucher Program Micnullel null nullns Ingrid Gould Ellen nulltnullrine O’Regan nullagner nullhool and nullrman Center, nullw nullornullnulliversity Introduction nullny scholars have argued that crime rates shape residential decisions and can thereby modify urban form. nullr enullmple, several researchers have pointed to high rates of urban crime as a contributor to suburban nullght nullullen and nullvitt, nullnullnullnullesnullowsnull and nulllls, nullnullnull nullthough some debate enullsts about how dramatically changes in crime per se can alter the decision about nullstract nullis article enullmines an important potential nullstinullation nullr tnull nullusing Cnullice Voucnullr nullogram, namely, wnulltnullr participants are able to access sanullr neignullornullonullnull nulling neignullornullonullcrime annullsubsinullnullnullnullusing nullta nullr null large cities, we enullminenull wnulltnullr voucnullr nulllnullrs are able to reacnullcommunities witnulllower levels onullcrimenullnulle nullunnulltnullt, in 2000, voucnullr nullusenulllnull occupienullneignullornullonull tnullt were about as sanull as tnullse nullusing tnull average poor renter nullusenulllnullannullwere signinullantly sanullr tnulln tnullse in wnullcnullnullusenulllnull assistenulltnullougnullplacenullasenullprograms livenull Notably, Blacnull voucnullr nulllnullrs livenullin signinullantly lower crime neignullornullonull tnulln poor nullusenulllnull onulltnull same race, but nullspanic annullnullite voucnullr nulllnullrs nullnullnotnullnull a separate analy null sis onullseven cities, we nullunnulltnullt voucnullr nulllnullrs livenullin consinullrably sanullr neignull null bornullonull in 200nulltnulln tnully nullnullin 1nullnull largely because crime rates nullll more in tnull neignullornullonull wnullre voucnullr nulllnullrs live tnulln in otnullr neignullornullonullnull

136 Crime and Urban Form nullns, Ellen, and O’Regan whether to live in a city versus the suburbs nullllen and Onullegan, nullnullnull researchers generally agree that crime is a significant concern for households and can innullence householdsnullneighborhood choices. nullrtually all of this research has focused on the residential decisions of middle-income households. null nullwer income households, however, care a great deal about neighborhood crime, too, although they may not have the same means to avoid it. null surveys of both nullutreaunulland nullving to Opportunity nullTOnullparticipants, respondents consistently cited crime as a primary motivation for wanting to enroll in those programs and move out of original high-crime neighbor- hoods nulloering, nullins, and nullchardson, nullnullnullnullnratty, nullnullnahan, and Pettit, nullnullnullnullbinowitnull and nullsenbaum, nullnullnull One nully nullstification for the nullusing Choice nullucher Program nullouchersnullis to provide assisted tenants with a greater range of neighborhood choices and, hopefully, enable them to reach betternull and safernulleighborhoods. null nullthough previous research has enullmined the enullent to which voucher holders reach lower poverty neighborhoods, virtually no wornullhas enullmined the safety levels of the neighborhoods in which voucher holders live. This article aims to fill that gap. The public safety risnull of living in high-crime environments are substantial. People living in high- crime neighborhoods are more linullly to be victims of crime, suffering physical, financial, and psy - chological harm. nulltruba and nulling nullnullnull estimate that moving to safer neighborhoods saved up to null lives for nullnullnullparticipants in the nullO program, with null of those lives saved from averted homicides. nullreover, being a witness to violent crime or living in fear of victiminulltion can lead to stress and even diminished performance in school nullarbarino et al., nullnullnullnullarnully, nullnullnullnullafford, Chandola, and nullrmot nullnullnull nullnally, evidence suggests that youth who grow up in high-crime neighborhoods are disproportionately more linullly to begin criminal careers and engage in risnull behaviors, such as drug and alcohol use nullase and nulltnull nullnullnullnulllen and Turner, nullnullnull nulldeed, some researchers have argued that the disparate results across nullO sites may be partly enulllained by variation in crime among these different locations nullurdicnullnullll et al., nullnullnull Clearly, crime is a vital component of neighborhood quality and thus a nully outcome of interest in evaluating the efficacy of subsidinulld housing policies that seenullto move program participants to better neighborhoods. This article aims to shed light on this critical dimension of the nullusing Choice nullucher Program in cities. nullecifically, we address the following questionsnullnullnullnullw does the safety of the neighborhoods where voucher households live compare with the safety of the neighborhoods where they might have otherwise livednullnullnullnullw does enullosure to neighborhood crime vary across different types of voucher householdsnullnullnullnullw has crime enullosure changed over time for voucher householdsnullTo what enullent can these changes be attributed to shifts in the geographic distribution of these households versus improvements in neighborhoods where these households are concentratednull nulling data on annual census tract-level crime rates in null large cities that were averaged for the years nullnull to nullnull, we address our first question by enullmining the enullosure of voucher holders null nulleenwood and nullocnullnullnullnull is an enulleptionnullthey found that residential decisions of low-income households are also affected by crime. null This increased choice itself may affect urban form, but we do not enullmine that in this article.

137Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull to crime. null null enulleriment with a variety of counterfactuals to assess whether vouchers are enabling households to reach safer neighborhoods than those in which they would have linullly lived absent their vouchers. nulle shed light on our second question by enullploring differences in enullposure to crime across subgroups of voucher holders. nullnally, using tract- and neighborhood-level crime data from nullcities in nullnull and nullnull, we describe changes over time in the degree to which voucher holders in those cities were enullosed to crime in their neighborhoods. nullfore conducting these empirical analyses, we provide a summary of the relevant literature. nullnull, we describe the data and our methods. null then present the empirical results of our analyses and conclude with a discussion of the nully findings and the policy implications. null a preview, we find that, in the large cities we studied, voucher households live in lower crime neighborhoods than other subsidinulled households. The findings regarding subgroups are nuanced. nulllacnull voucher holders face higher neighborhood crime rates than nullite and nullspanic voucher households, but nullacnull voucher holders live in safer neighborhoods than other renters of the same race, whereas nullite and nullispanic voucher holders do not. nulls for changes over time, voucher holders in our seven sample cities lived in substantially safer neighborhoods in nullnull than they did in nullnull. The movement of voucher households, however, contributed little to those improvements. nullther, the nully change was that the crime rates in the neighborhoods where voucher holders typically live fell marnulldly. Previous Literature Despite being discussed in policy circles as far bacnullas the nullnulls, vouchers did not become a feature of federal housing policy until nullnull nullrlebenull, nullnullnullnullhwartnull nullnullnull but the program grew quicnully. null nullnull, more than nullnullnullnullhouseholds held vouchersnullin nullnull, that number bal - looned to more than nullnullmillion. nullucher households comprised null percent of nullnull Department of nullusing and nullban Development nullnullnullassisted households in nullnull. nullnully potential benefit of vouchers is that they provide households much more nullnullbility over location choice than pronullct-based assistance does. nullhanced location choice, proponents argue, will linullly reduce urban poverty concentration and allow voucher households access to higher opportunity neighborhoods. nullereas public housing and low-income housing tanullcredit nullnullTCnull residents are typically quite limited in their choice of developments and units, voucher households should be able to select from a much wider array of neighborhoods and, therefore, have the oppor- tunity to choose lower crime areas. null addition, voucher tenants are less visible to neighbors than public housing and nullnullC developments and, thus, may have an easier time reaching neighbor - hoods with lower crime and poverty rates. nullreover, recent policy changes have enullanded the portability of vouchers. null nullnull, Congress amended the nullction nullstatute to permit voucher holders to use their subsidies anywhere within a given metropolitan area and, in nullnull, further amended the statute to allow for voucher use anywhere within the nullited nullates. null addition, the nullPnullnull Program and the nullality nullusing null nullte that we do not have census tract-level crime data in the suburbs of these large cities nulllthough some of the cities themselves are considered suburbannull so our analysis is limited to central cities.

138 Crime and Urban Form nullns, Ellen, and O’Regan and nullrnullnullsponsibility nullt of nullnull have provided nullD with additional tools to help local public housing authorities deconcentrate assisted households, frequently with the use of housing vouchers. nullill, voucher location choice is surely constrained. nullnullmum rents paid are capped by nullir nullrnullt nullnts, potentially limiting neighborhood options nulllthough Devine et al. nullnullnull show that almost all communities have rental housing units that would be affordable to voucher holdersnull null addition, landlords may resist accepting tenants with vouchers, especially in lower crime and lower poverty environments, and voucher holders may have limited information about alternative neighborhoods when manullng their choices. Poverty Exposure for Subsidized Households nullst of the literature describing the neighborhoods in which assisted households lived focuses on poverty rates. nullr traditional public housing residents, the evidence clearly illustrates that they live in comparatively poor neighborhoods. nulloering, nullamely, and nullichardson nullnullnullnullnullnull found that, in nullnullnullnull, slightly less than one-half of all public housing tenants lived in high-poverty census tracts nulltracts with poverty rates of nullnull percent or highernull. nullimilarly, nullewman and nullchnare nullnullnullnullnullnull reported that more than nullnull percent of tenants in family public housing lived in high-poverty census tracts in nullnullnullnull. nullidence suggests that voucher households also live in neighborhoods with higher than average poverty. Pendall nullnullnullnullnullnull, enullamining census tract-level data from nullnullD on nullnullnullnull voucher households, found that neighborhoods with voucher holders had a nullnull poverty rate of null percent on average, compared with the nationwide average of null percent. null addition, tenants receiving all forms of assistance were more linullly than renters as a whole to live in neighborhoods scoring high on a neighborhood distress indenull constructed from poverty ratesnullpublic assistance receiptnulland the proportion of female-headed households, high school dropouts, and labor force participants. nullnetheless, studies typically find that voucher holders live in somewhat less distressed census neighborhoods than other assisted households. nullr enullmple, in their comprehensive enullmination of the location patterns and neighborhood characteristics of the entire voucher population, Devine et al. nullnullnull found that almost null percent of census tracts in the null largest metropolitan statistical areas nullnullsnullincluded at least one voucher household. null contrast, only nullpercent of all census tracts in these nullnullnulls included public housing units. The relatively high dispersion of voucher house - holds suggests that at least some voucher households must live in high-quality neighborhoods. nulldeed, in the same study, the authors found that, in nullnull, almost null percent of voucher house - holds lived in census tracts with fewer than null percent of the residents living in poverty. nullother nullnull percent lived in tracts with nullnull- to nullnull-percent poverty rates. nullppronullimately nullnull percent of voucher families lived in neighborhoods with poverty rates of null percent or more. nullrtung and nullnig nullnullnull provided evidence that the voucher program in the nullshington, D.C. metropolitan area has been more effective in providing access to lower poverty neighborhoods than other forms of housing assistance. They found that, although null percent of the public hous - ing and null percent of the other nullD-assisted developments are in tracts with median incomes below nullnullnullnull only null percent of voucher households live in such tracts. Thus, although about null percent of nullshington, D.C.null voucher households lived in tracts with median incomes below nullnullnull,nullnullnull, they were less concentrated in poor tracts than their counterparts living in public housing and other place-based, subsidinulld housing.

139Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull nullst recently, nullClure nullnullnull compared locational outcomes for the voucher and nullnullC pro - grams. nulling nullnull administrative data on voucher households and nullnullC units placed in service through that year, he found that about null percent of nullnullC households and null percent of voucher households lived in low-poverty census tracts and, on average, voucher households lived in very slightly lower poverty neighborhoods than nullnullC households. null nullgnificantly, the proportions of nullnullC and voucher households in high-poverty tracts were slightly lower than the percentages of poor households that lived in high-poverty tracts, although they were higher than the share of all renters who lived in such tracts. The households assisted through both of these programs, in other words, were reaching neighborhoods with somewhat lower poverty rates than poor households, but they were still living in neighborhoods that had significantly higher poverty rates than other renters nullt least in nullnullnull nullnumber of authors have documented the tendency for the voucher population to cluster geo - graphically nullang and nullrady, nullnullnullnullng, nullrady, and nullng, nullnullnull This clustering does not appear to be simply the result of the clustering of units with rents below nullir nullrnullt nullnts. nullcial segregation, imperfect information, and the refusal of landlords to rent to voucher households all linullly contribute to clustering as well nullurner, Popnulln, and Cunningham, nullnullnull nullen these clustered households, however, live in significantly lower poverty neighborhoods compared with public housing households. Neighborhood Crime Rates nullw studies enullmine the neighborhood crime rates enullerienced by voucher holders, largely because of a lacnullof suitable data. nullwever, studies of three manullr mobility programsnullautreaunull nullO, and nullPnullnullnullrovide some evidence on the neighborhood safety of both public housing residents nullre-movenulland voucher households nullost-movenull nullcause participants were chosen pre - cisely because they lived in distressed neighborhoods, the reported numbers are not generalinullble to all subsidinulld households. nullnetheless, the figures are illuminating. null brief, these groups were located in very high-crime areas when living in their original public housing developments and chose to move to lower crime nullet still relatively unsafenullareas after receiving their vouchers. The nullutreaunullprogram was created in Chicago in nullnull as a result of a series of lawsuits against the Chicago nullusing nullthority nullnullnulland nullD. nullutreaunulloffered nullacnullfamilies in Cnull housing the opportunity to move to desegregated areas around the Chicago area, including the suburbs. The program moved more than null,nullnullnull families between nullnullnullnull and nullnullnullnull nullnulleels et al., nullnullnullnullnull. nullccording to nullbinowitnulland nullsenbaum nullnullnull, nearly one-half of nullutreaunullparticipants reported that vio - lent incidents occurred regularly in their neighborhoods. Criminal victiminulltion rates were twice as high among Chicago public housing tenants compared with the city as a whole. nullels et al. nullnullnull estimated that violent crime rates in nullutreaunullparticipantsnulloriginal neighborhoods were three times as high as those in the average Chicago neighborhood. null null is worth noting that nullnullC units were relatively more concentrated in low-poverty neighborhoods than voucher units were in the suburbs, but that they were slightly less concentrated in central cities. Our analysis focused on large cities.

140 Crime and Urban Form nullns, Ellen, and O’Regan null the short-term, those households that moved to new neighborhoods through the program con - tinued to face higher crime rates than others in their surrounding areas. nullburban movers landed in neighborhoods with a violent crime rate about five times as high as the crime rate in the Chicago suburbs at that time, and those that moved within the city faced violent crime rates about one and one-half times as high as the average neighborhood in the city. null the longer term, however, the nullutreaunullhouseholds tracnulld by nullels et al. nullnullnull lived in neighborhoods with violent and property crime rates that were comparable to the rates for the county as a whole. nullD launched nullO in nullnull as a five-city enullerimental demonstration to move subsidinulld house - holds living in high-poverty public housing developments into low-poverty neighborhoods. nulloering, nulleins, and nullichardson nullnullnullnullnullnull reported that more than one-half of nullTO participants identified crime, gangs, and drugs as the principal motivation for wanting to move out of their neighborhoods. nullanratty, nullnullnahan, and Pettit nullnullnull reported that almost null percent of the nulls nullgeles participants cited getting away from drugs or gangs as the primary reason for wanting to move. null shocnulling proportion of these respondents reported that one or more of their household members were the victim of a crime in the past nullmonths. nullministrative data supported these reports of victiminulltion. nullolent crime rates for the baseline nullO census tracts in nullston, Chicago, and nulls nullgeles were three times higher than in the metropolitan areas as a whole nullingsley and Pettit, nullnullnull null for the post-move neighborhoods, some evidence indicates that nullO participants occupied lower crime neighborhoods after participating in the program. nullngsley and Pettit nullnullnull found that violent crime rates in nullston, Chicago, and nulls nullgeles were almost twice as high in the nullO origin neighborhoods than in the nullction nullmoversnullinitial post-move neighborhood. nullins and nullroder nullnullnull reported results of pre- and post-move surveys for the nullO treatment, comparison, and control groups nullhe comparison group include households that received vouchers but were not restricted to use them in low-poverty neighborhoodsnull nullrvey respondents in the two groups that received vouchers reported significantly greater improvements in neighborhood safety than the control group for every question asnulld. null Thus, nullO participants were successful in using vouchers to move to safer neighborhoods. nullmilarly, some evidence suggests that participants moving out of nullPnullnull sites are moving to safer and more afnullent neighborhoods. nullron et al. nullnullnull provided a snapshot of post- revitalinulltion neighborhood conditions in eight cities and found that post-revitalinulltion households still occupy relatively unsafe neighborhoods. Overall, about null percent of the respondents reported nullig problemsnullwith drug trafficnullng and gang activity in their current neighborhood, and fewer than null percent reported big problems with violent crime. nulluseholds in the sample that were no longer receiving a housing subsidy were the least linullly to report big problems with drug trafficnullng and gang activity, while returning nullPnullnull residents were the most linullly to report big problems. The evidence that nullPnullnull revitalinulltion pronullcts are moving households to safer neighborhoods is preliminary, however, because the revitalinulltion pronullcts and voucher mobility nullPnullnull spawned are still in progress. null nullestions address perceived safety during the day, safety during the night, drug activity in view in the neighborhood, and whether a household member was a crime victim in the past nullmonths.

141Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull Considered as a whole, previous wornullon the neighborhood conditions faced by subsidinulld house - holds suggests that voucher households, on average, live in neighborhoods with higher poverty rates than the average renter, but they live in lower poverty areas than public housing tenants and in areas with slightly lower poverty rates than other poor households. Thus, tenants assisted through these programs have had some success in reaching low-poverty neighborhoods, but the success has been relatively modest. null nullow little about the enullosure of voucher households to crime. nullat we do nullow comes from the enullerience of a very particular subset of voucher holders that moved out of distressed public housing developments through the nullutreaunull nullO, or nullPnullnull programs. This body of research offers some suggestive evidence that these selected voucher recipients have been successful in moving to safer neighborhoods. null remains to be seen if the broader voucher populationnullhose not necessarily eligible for and selected into specialinulld programs, and representing a wider array of citiesnullas been able to reach safer neighborhoods. Data and Methods Our analysis relies on two sets of crime data, all restricted to large cities nullather than nullnullnull nullrst, we use data from the National NeignullornullonullCrime Stunull nulleterson and nullivo, nullnullnull a nationally representative sample of crime data for nullnullnullcensus tracts in null nullnull cities, collected by nullth Peterson and nulluren nullivo of Ohio nullate nulliversity. Crime counts from nullnull to nullnull were provided to Peterson and nullivo by local police departments. The data set includes an average of the Part nullcrime categories over the entire nullyears for each census tract. nullch nullyear averages allow for abnormal spinulls to be smoothed out over the sample period, and are frequently used in crime research when available nullampson, nulludenbush, and nullrls, nullnullnull The sample of null cities was randomly chosen from all cities with at least nullnullnullnullpeople as of the nullnull Census, and then was stratified by region. null the event that a citynull police department was not able to provide crime data, the city was replaced with a city that was similar in terms of geography and demographics. nulllist of the null cities, in addition to basic descriptive statistics on crimes and subsidinulld housing units for each city, is shown in appendinullnullnull The second data set includes annual census tract-level crime data for seven null.null. citiesnullnullustin, Chicago, Cleveland, Denver, nulldianapolis, Philadelphia, and nullattlenullrom nullnull to nullnull. nullpendinullnullnull displays the crime data availability for those years and describes the sources of the data. null nulle merged census tract-level counts of four types of households to the crime datanullvoucher house- holds, renter households below the poverty line, public housing tenants, and nullnullC households. null obtained voucher and public housing data from nullDnull Picture of nullbsidinulld nulluseholds. null the present time, voucher data are available through this data set for nullnull, nullnull, nullnull, and nullnull. null addition, we obtained access to household-level voucher data from nullD for nullnull to estimate null nulle do not have nullnull data for three of the cities. nullr these cities, we use nullnull crime data to estimate nullnull voucher crime enullosure rates. nullthough this is not ideal, there is not much reason to enullect substantial changes in the neighborhood distribution in crime from nullnull to nullnull in these cities, and this is preferable to limiting the nullnull analysis to four cities. null Cleveland, which has missing crime data for nullnull, we use nullnull and nullnull crime data to estimate nullnull crime rates using a linear interpolation.

142 Crime and Urban Form nullns, Ellen, and O’Regan crime enullosure rates for voucher households with different demographic characteristics. Public housing data are available for nullnull to nullnull, nullnull, nullnull, and nullnull. nullnullC data are available from nullDnull nullnullC database from nullnull to nullnull. nullnull Census data on poor renter counts are available for nullnull. nullnally, we merge these data with nullnull Census counts of total housing units and tract- level demographic statistics from the nullban nullstitutenull nullighborhood Change Database. null with all administrative data sets, gaps in coverage and variation in quality enullst. nullD collects voucher and public housing data from local housing authorities, and in the early years of our sample, reporting rates were sometimes well below nullnullpercent. nullhe data set provides complete informa - tion for null percent of voucher recipients in nullnull, for enullmple.nullnull nullnull, reporting rates rose to null percent. nullD publishes the percentage of vouchers and public housing units that are reported by each city, so we can identify which cities were most affected by these data gaps. nullpendinullnullnull displays the percent reported by each citynull housing authority for the longitudinal sample nullustin, Chicago, Cleveland, Denver, nulldianapolis, Philadelphia, and nullattlenullin nullnull and nullnull. null nullnull nullnd presumably nullnullnull the reporting rate for vouchers was consistently very high. nullr all cities aside from nulldianapolis nullnullpercentnull the reporting rate was null percent. Public housing reporting rates were considerably lower, at least in nullnull. null that year, reporting rates for public housing were nullst null percent in Chicago and null percent in Philadelphia. null have little reason to believe, however, that reporting rates would vary with the crime rates of the development. null they did vary, we enullect that they would be lower in higher crime developments, suggesting that our results could potentially understate the crime rates enullerienced by public housing residents. nullhibit nulldisplays descriptive statistics as of nullnull for the null-city cross-sectional sample and the nullcity longitudinal sample. nullcause our samples are restricted to large cities nullhat is, not nullnullnullwe also provide descriptive statistics for all tracts in nullnull cities with populations greater than null,nullnullas a comparison. Exhibit 1 nullnullitnull nullossnullectional Samnulle nullnullnullnullnull 7nullitnull nullnnulltnullinal Samnulle nullnullnullnullnull nulll nullracts in nullSnullnullties null5nullnullnull Ponulllation nullnull5nullnullnull a nullnullnullge City annullCensus nullnullct Chanullctenullstics in null00 Cnullmes nullnull1,000 nullonulle null.0 null.null null.null nullveranull nullract nullaracteristics nullouchenullholnullnull nullnulltnullct null.null null.null null.null Lnullnull units nullnulltnullct null.null null.0 1nullnull nullnullic housing nullnulltnullct null.null null.null null.null nullonullnullentenull nullnulltnullct 1null.null 1null.null 1null.null nullnulllation nullnulltnullct null11null nullnullnull null111 nullnullnully nullte (nullightenullanullnullge) 1nullnull 1nullnull 1nullnull nullnullcent nonnullisnullnic nullite (nullightenullanullnullge) null.null null.1null null.null nullnullcent nonnullisnullnic nullacnull(nullightenullanullnullge) null.null null.null 1nullnull nullnullcent Hisnullnic (nullightenullanullnullge) null.null 1nullnull null.null nullotal nullnulllation in tnullcts null,nullnullnullnull nullnullnullnull0 10nullnullnullnullnull nullHnull nulllonullinconull nullusing tax creditnull a nulll core cities of nulltronulllitan areas, nullI UnifornullCrinull Renullrts, nullnullnull

143Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull Comparing the three samples in enullibit null we see that the tracts in the longitudinal sample differ slightly demographically from the full set of urban tracts, but the tracts included in the null-city sample are quite similar to the full set. nullecifically, the tracts in the seven-city sample have larger percentages of poor and nullacnullresidents than the full city sample. The tracts in the null-city sample contain similar proportions of people in poverty and in different racial groups as those in the full city sample, although the null-city sample is slightly more non-nullite. The largest difference between the two samples is the city crime ratesnullthe average crime rate is considerably lower for the null cities than for the full set of nullnull cities. null for subsidinulld housing, average voucher concentrations within census tracts are fairly uniform throughout the two samples and all nullnull cities, ranging from null to null voucher holders per tract, or appronullmately nullpercent of all housing units. nullnullC concentrations also vary little across the samples, ranging from null to null per tract nulless than nullpercent of a tractnull housing units on averagenull Public housing concentration is much more varied across the samples. null the longitudinal sample, Chicagonull presence among the seven cities leads to a larger average number of public housing units per census tract than the other samples. nullppendinull null-null displays how crime and housing variables change over time in the cities in the longitudi- nal sample. null shown, crime rates decreased over time, as they did across the nullited nullates, while the number of voucher households and nullnullC units increased, but the number of public housing units declined. nullgregate crime rates nullnullressed as crimes per nullnullnullpeoplenull null declined null percent from nullnull to nullnull. nullom nullnull to nullnull, the number of voucher and nullnullC households doubled. Public housing units did the oppositenulleclining over the decade from null,nullnullto null,nullnullunits. nullnally, we have access to data from more than nullnullnullnullindividual voucher households that, in nullnull, lived in one of the null cities for which we have crime data. This represents null percent of all voucher holders nationwide in nullnull. nullhese cities also contain null percent of nullnullC tenants and null percent of all public housing tenants.nullnullom these data we can determine whether the house - hold used a voucher to move into that census tract in that year, the race of the household head, whether the household contains children under null, and the total household income. Estinullting Groupnullpecinull nullinull Exposure To estimate the crime rates faced by the typical household in each group, we estimate crime enull- posure rates, which weight a neighborhoodnull crime rate by the proportion of the samplenull relevant household type nulloucher, nullnullC, etc.nullwithin that neighborhood. These enullosure rates, in other words, essentially show the neighborhood crime rate enullerienced by the average member of the given group. nullecifically, the crime enullosure measure is enullressed for voucher households as summation_old n V υ i [ [Crime] i * (

)] i = 1

nullnull null Crime rates are enullressed both as crimes per nullnullnullpeople and as crimes per nullnullnullhousing units. null the results section, however, we present crime enullosure rates as crimes per nullnullnullpeople, with crimes per nullnullnullhousing units and crimes per square mile as robustness checnull, included in the appendinull

144 Crime and Urban Form nullns, Ellen, and O’Regan where Crime i is the crime rate nullither total or violentnullin census tract i, v i is the number of voucher households nullr public housing, nullnullC, or poor renter householdsnullin census tract i, and V is the number of voucher households nullr public housing or nullnullC units, or poor renter householdsnull in the sample. The resulting value is essentially a weighted average neighborhood crime rate, or the crime rate faced by the typical household in that group. null conduct statistical tests to learn if the differences in means across groups are statistically significant. null addition, we estimate the percentage of each housing subgroup population that resides in a high-crime neighborhood, which is defined as a neighborhood with a crime rate at least one standard deviation above the mean. null robustness checnull, we also use the number of crimes per square mile of land area and the number of crimes per nullnullnullhousing units. Comparing voucher household crime enullosure with that of the general population is informative, but it does not provide a very good counterfactual for where voucher households would have lived if they had not had the benefit of a housing voucher. To provide a sense of the other options that voucher households might have in the absence of a voucher program, we consider the average crime rates in the neighborhoods where nullnullC units, public housing units, and all units occupied by poor renters are located. These housing units represent locations where voucher households might live in the absence of the program. Comparing the neighborhoods of voucher holders with those of tenants in place-based programs sheds light on whether the increased choice provided by vouchers helps subsidinulld households reach better neighborhoods. nullcause much of the growth in the voucher program is a result of the demolition of public housing, public housing locations serve as a viable counterfactual for where voucher holders could be living if such demolitions had not occurred. The nullnullC, as the largest place-based housing subsidy in the country, is another relevant place-based counterfactual. Identifnullng nullanges in nulloucnullr nullinull Exposure Onullr nullinull To estimate the relative changes in crime enullosure among the voucher households from nullnull through nullnull, we first limit the sample to a balanced panel, which includes only neighborhoods for which we have crime and housing data in nullnull and nullnull. nullte that we have two mechanisms through which crime enullosure could change over time for voucher holders. nullrst, the distribution of voucher households across neighborhoods could shift to neighborhoods with higher or lower crime rates. nullcond, the distribution of voucher holders could remain constant, but crime rates could increase or decrease in the neighborhoods in which voucher holders are located. This is an important distinction for policy. null the gains in public safety for voucher holders were largely a result of safety gains within their enullsting neighborhoods, the continued mobility allowed by the voucher program may not deserve a lot of the credit for these gains. null, however, the vouchers enabled tenants to move to safer neighborhoods, then continued mobility deserves the credit. To test whether changes in crime enullosure for voucher households were due to spatial shifts in crime patterns, we decompose the crime changes and compute a hypothetical crime enullosure rate, showing what the crime enullposure rate would have been for voucher holders in nullnullnullnull if the distribu- tion of voucher holders had remained unchanged between nullnull and nullnull. This rate uses the nullnull voucher neighborhood distribution with nullnull crime rates, defined notationally nullor vouchersnullas summation_old n V 1998 υ i,1998 [ [Crime] i, 2008 *

] i = 1 . nullnull

145Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull null the actual crime enullosure rate in nullnull nullsing nullnull crime and voucher distributionsnullis roughly the same as this hypothetical rate, then we can infer that changing conditions in the neighborhoods where voucher holders tend to live largely drove any changes. null contrast, if we find that the actual crime enullosure rate is significantly lower than the hypothetical crime enullosure rate, then we infer that changes in the distribution of voucher holders linullly enulllained a large part of any reduction in enullosure. Cross-Section Results nullhibit nulldisplays crime enullosure rates for the null cities in the cross-sectional sample covering the year nullnull. null include in the enullibit crime enullosure rates for all households, voucher households, nullnullC tenants, public housing tenants, and poor renters living in these cities. The enullibit shows that voucher holders, on average, lived in neighborhoods that had significantly higher crime rates than those lived in by all households but slightly lower crime than those lived in by poor renters. nulls for comparisons to residents living in other types of subsidinulled housing, voucher holders lived in neighborhoods that were significantly more safe than those lived in by tenants in place-based subsidinulld housing programs. null nullnull, public housing tenants and, perhaps surprisingly, nullnullC tenants, lived in significantly higher crime neighborhoods than voucher holders. nullll of these results are robust to modifications in the crime rate denominator.null nullhat about enullposure to violent crimes in particularnull Total crime rates are largely driven by property crimes, particularly larceny and other thefts. nulln this sample, only null percent of the crimes are violent crimes.nullnullposure to violence, however, may be a particular concern. nullnullr nullnullnull found that lower youth cognitive test scores can be enulllained in part by association with violent peers and enullosure to neighborhood violent crime. nullarnully nullnullnull found that children living in census blocnullgroups where a homicide occurs nullweenullbefore a standardinulld test perform worse than other comparable children. Exhibit 2 nullnulle nullimes nullr 1nullnull Peonulle StatisticallnullDinullnullrent nullom Vonullher nullime Exnullsnulle Ratenull Violent nullimes nullr 1nullnull Peonulle StatisticallnullDinullnullrent nullom Vonullher Violent nullime Exnullsnulle Ratenull Cnullme Enullosunulle Rates in null00 (samnulle: null cities) nulll householnull null.0 Signinullantly lonullnull(1null nullnull Signinullantly lonullnull(1null nullouchenull householnull null.null null 1nullnull null Lnullnull tenants 100.null Signinullantly highenull(1null 1nullnull Signinullantly highenull(1null nullnullic housing tenants 10nullnull Signinullantly highenull(1null null.null Signinullantly highenull(1null nullonullnullentenull null.null Signinullantly highenull(1null 1nullnull null nullHnull nulllonullinconull nullusing tax creditnullnull nullnot anulllicablenull

146 Crime and Urban Form nullns, Ellen, and O’Regan The patterns for violent crime enullosure are fairly similar to those for total crime enullosure. nullong assisted households, public housing residents lived in the most violent neighborhoods on average, while voucher holders lived in the least violent. On average, voucher holders lived in neighborhoods with safety levels very close to those of the neighborhoods lived in by the average poor renter. nullother way to measure differences in crime enullosure is to compare the proportion of each popu - lation that lived in a high-crime neighborhood, defined as neighborhoods with crime rates more than one standard deviation above the mean. nullhibit nulldisplays these proportions, together with results from statistical tests of differences in proportions between tenants in each housing program. The share of voucher holders who lived in high-crime neighborhoods was significantly lower than the proportion for either nullnullnullTC or public housing tenants. The only difference between these results and those in enullhibit null is that the proportion of nullnullnullTC households living in high-crime neighborhoods was slightly higher than the share of public housing residents, although the difference is not statistically significant. The violent crime results are also very similar. These promising findings on voucher crime enullosure appear to contradict previous wornullthat finds that voucher households have had limited success in gaining access to higher quality neighborhoods nullcClure, nullnullnullPendall, nullnullnullnullng, nullrady, nullnullnullnullng, nullrady, and nullng, nullnullnull These differences could be because of idiosyncrasies in our sample or they could renullct differences in patterns of enullosure to crime as compared with enullosure to poverty and racial minorities. To test this using the null-city sample, enullibit nullenullands on the analysis presented in enullibit null displaying poverty and minority enullosure rates and average crime rates for the housing subgroups in nullnull. Exhibit 3 nullnulle onull nullnullinnull Percent in nullnullnullrime Neinullborhoods Statisticallnull Dinullnullrent nullom Vonullher Pronullrtionnull Percent in nullnullnulliolent nullime Neinullborhoods Statisticallnull Dinullnullrent nullom Vonullher Pronullrtionnull nullenullcent in HighnullCnullime nulleighnullonullhoonulls nully nullynulle of Housing in null000 (samnullle: null1 cities) nullotal null1 Signinullantly lonullnull(1null null1 Signinullantly lonullnull(1null nullouchenull householnull nullnull null 11.0 null Lnullnull tenants 11.null Signinullantly highenull(1null 1nullnull Signinullantly highenull(1null nullnullic housing tenants 10.null Signinullantly highenull(1null null.null Signinullantly highenull(1null nullonullnullentenull null0 Signinullantly highenull(1null 11.null Signinullantly highenull(1null nullHnull nulllonullinconull nullusing tax creditnullnull nullnot anulllicablenull Exhibit 4 nullimes nullr 1nullnull Peonulle nullveranull PovertnullRate nullnull nullveranull Percent Minoritnull nullnull nullighnullnulloonullCnullme, nullnullnully, annullnullnonullty Enullosunulle Rates in null00 (samnulle: null cities) nullouchenullhouseholnull null.null null.null null.null Lnullnull tenants 100.null null.null null.1 nullnullic housing tenants 10nullnull null.null null.null nullonullnullentenull null.null null.null null.null nullHnull nulllonullinconull nullusing tax creditnull

147Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull The enullibit clearly illustrates that crime enullosure patterns are indeed different. nullnullC and voucher households, on average, live in communities with virtually identical poverty rates and minority population shares, but they live in communities that are quite different in terms of crime. nullusehold preferences might help to enulllain the differences and similarities between crime, poverty, and minority enullosure among these different types of households. The households with greater residential choicenullouchers and poor rentersnullive in neighborhoods with dramatically lower crime rates but with fairly similar poverty rates and racial compositions. This suggests that voucher holders and other poor households, when choosing neighborhoods, may prioritinull the avoidance of high-crime areas, not neighborhoods with high minority andnullr poor populations. This prioritinullng also suggests that if our nully interest is facilitating access to safe neighborhoods that offer a rich set of opportunities, then poverty rates and minority concentration may not serve as ideal pronulles. nullfferences nullross Subgroups of nulloucnullr Holders nullthough the previous enullibits provide useful information about the location and neighborhood choices of the average voucher holder, this section enulllores whether notable differences across subgroups enullst. null nullow that housing marnullt opportunities and outcomes differ noticeably by income, race, and family structurenullthe opportunities and outcomes of housing voucher holders may differ as well. nullhibit nulldisplays crime enullosure rates for voucher households, disaggregated by the race of the household head, household income strata, and the presence of children. The largest differences are across income groups, where we see a monotonic decline in voucher enullosure to neighborhood crime as household income increases. This seems surprising given that vouchers should technically neutralinull income differences by allowing households to pay only null percent of their income for rent. null for racial differences, nullspanic voucher holders lived in neighborhoods with the lowest crime, and nullacnullvoucher holders lived in the highest crime Exhibit 5 Snullnullonull onullVonullher nulllders nullimes nullr 1nullnull Peonulle a nullouchenullCnullme Enullosunulle in null00 null nullmognullnullic annullnullnulllity Chanullctenullstics (samnulle: null cities) nulll nulluchenullholnullnull null.null nullite nulluchenullholnullnull null.null null nullacnullnulluchenullholnullnull null.null Hisnullnic nulluchenullholnullnull null.null null Householnullincome nullnull0,000 null.null Householnullincome null0,000 to nullnullnullnull null.1 null Householnullincome null0,000 to nullnullnullnull null.null null Householnullincome nullnull0,000 null.null null Householnull nullth chilnullen (null) null.0 Householnull nullthout chilnullen null.null null nullnullt statistically different fronullrelenullnt reference categorynull a nullese rates are slignullly nullgnullr tnulln tnull nullucnullr crinull exnullsure rates renullrted in exnullibit nullbecause of differences betnullen tnull nullusenullld data and tnullse renullrted in HUnulls nullcture of Subsidinulld Housenullldsnull nulligninullantly different fronullnullack nullucnullr nulllders at tnull nullnullrcent lenulllnull nullSigninullantly different fronullnullucnullr nulllders nulltnullinconulls belonullnullnullnullnullat tnull nullnullrcent lenulllnull

148 Crime and Urban Form nullns, Ellen, and O’Regan neighborhoods. Crime enullposure rates for voucher households with children were only very slightly lower than those for households without children, and the difference was not statistically significant. nullthough enullibit nullsuggests that nullspanic voucher holders are the least enullosed to crime, and that nullacnullvoucher holders are the most enullosed, it fails to tanull into account the safety level of the neighborhoods that households of different races tend to live in absent housing assistance. nulllthough it is impossible to nullow enullctly where households would have lived absent their voucher, enullibit null appronullmates such a counterfactual by comparing enullosure to neighborhood crime for voucher households of different races with enullosure of poor households and renter households of the same race. The implicit assumption, in other words, is that absent a voucher, households would have lived in the neighborhoods lived in by unassisted, poor, and renter households of the same race. This comparison does not suggest that voucher holders should be constrained or guided in their residential choices by their racenullit simply assumes that they are as linullly to operate under such constraints, nullst linull unassisted poor and renter households of the same race. The results are surprising. nullhite and nullispanic voucher holders tend to live in nullignuller crime neighbor- hoods than their counterparts who do not receive vouchers nullpoor households and renter householdsnull. null contrast, nullacnullvoucher households live in census tracts with slightly lower crime rates than nullacnullpoor and nullacnullrenter households. The voucher program is helping to close the nullacnullnullite and nullacnullnullspanic racial gaps in enullosure to crime. Of course, our comparison groups may differ from voucher holders in unobserved ways, and these differences may be more pronounced for particular racial groups. nullr instance, nullite and nullspanic voucher holders may have quite different location preferences or face very different constraints compared with the full set of nullite and nullspanic poor households, but nullacnullvoucher holders may be more similar to other nullacnullpoor households. Thus, we should be cautious in drawing conclusions from these findings. Exhibit 6 Ponulllation nullimes nullr 1nullnull Peonulle nullouchenull, nullonull, annullRentenullHouseholnullCnullme Enullosunulle Rates null Race (samnulle: null cities) nullite nullouchenullhouseholnull null.null nullonullhouseholnull null.1 Rentenullhouseholnull null.null nullacnull nullouchenullhouseholnull null.null nullonullhouseholnull null.null Rentenullhouseholnull null.null nullsnullnic nullouchenullhouseholnull null.null nullonullhouseholnull null.0 Rentenullhouseholnull null.null

Unknown

149Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull Longitudinal Results The cross-section analyses suggest that, at least in cities, voucher households lived in neighbor- hoods that were about as safe as those where poor renters lived, and they lived in lower crime neighborhoods than where other subsidinulld households lived. null now enullmine how voucher crime enullosure changed over time. nullhibit nulldisplays nullnull and nullnull crime enullosure rates for all households and for voucher households in nullstin, Chicago, Cleveland, Denver, nulldianapolis, Philadelphia, and nullattle. nullnullhibit null illustrates that total crime dropped considerably in the entire sample nullfrom nullnull to nullnull crimes per nullnullnullpeoplenull but the reduction was not statistically significant and not every city ennulloyed these reductions. null nullustin, Denver, and nullndianapolis actually enullperienced slight increases in overall crime rates. nulltably, the reduction in the crime rates in the neighborhoods where voucher holders lived was even larger than that for all tracts and was statistically significant, unlinull the re - duction for all tracts. The typical voucher household enullerienced a reduction in crime in every city enullept for nullstin nullhere average crime rates rose slightly from null to null crimes per nullnullnullpeoplenull and even in that city, the crime increase for voucher holders was smaller than that enullerienced by the average household in the city. The only city where the overall crime decrease was greater than the crime decrease for voucher holders was Chicago. nulln Cleveland, Denver, nullndianapolis, and nulleattle, voucher holders enullerienced greater decreases in neighborhood crime than the overall population. nullthough it is impossible to nullow enullctly what drove these reductions in enullosure to neighbor - hood crime, a simple decomposition can shed some light on the causes. null Two possible enulllana - tions emerge. nullther the spatial distribution of voucher households changed and they moved disproportionately to lower crime neighborhoods, or it remained the same but the neighborhoods voucher holders lived in enullerienced disproportionate declines in crime. To tease out the correct Exhibit 7 1nullnull 2nullnull nulll nullnulleholds Vonullher nullnulleholds nulll nullnulleholds Vonullher nullnulleholds Changes in Cnullme Enullosunulle, 1nullnullnull0null(samnulle: nullcity longitunullnal) nulll null.null 101.null null.null null.0 nullstin null.null null.null null.0 null.null Chicago null.null 10null1 null.null null.null Clenulllannull null.null null.null null.0 null.null nullnnullnull null.null 10null1 null.null null.1 nullnullananulllis 11nullnull 1null.null 1null.null 1null.null nullilanulllnullia null.0 null.1 null.null null.null Seattle null.null 1null.null null.null 1null.0 null nullte that the value of the crime rate denominator does not change from nullnull to nullnull. Thus, we are underestimating crime rate decreases in neighborhoods and cities with population growth. null The analysis does not tanull into account the possibility that the spatial location of these households could affect neighborhood crime rates but still allows us insight into whether crime decreases were more related to household movements or within-tract improvements.

150 Crime and Urban Form nullns, Ellen, and O’Regan enulllanation, we estimate how average neighborhood crime rates for these subgroups would have changed over time if the geographic distribution of each subgroup had remained constant. nullecifi - cally, enullibit nullpresents what crime enullosure rates would have been in nullnull had the distribution of voucher holders across neighborhoods remained identical between nullnull and nullnull. nulln other words, we calculate the enullosure of nullnull voucher holders to nullnull neighborhood crime rates.nullnull compare these hypothetical neighborhood crime rates with the actual nullnull crime enullosure rates. Exhibit null 1nullnullVonullher nullcationnull2nullnullnullime 2nullnullVonullher nullcationnull2nullnullnullime nullcomnullsition of Cnullme Rate Changes (samnulle: nullcity longitunullnal) nulll null.null null.0 nullstin null.0 null.null Chicago null.null null.null Clenulllannull null.null null.null nullnnullnull 10nullnull null.1 nullnullananulllis 1null.1 1null.null nullilanulllnullia null.null null.null Seattle 11nullnull 1null.0 null shown in enullibit null the hypothetical crime enullosure rates in the nullnull voucher location column the left are generally only slightly larger than the actual nullnull crime enullosure rates on the right, suggesting that improvements in crime enullosure were mostly driven by improvements in the neighborhoods where the various housing groups lived at baseline, rather than by the movement of voucher recipients to lower crime neighborhoods. nullill, the hypothetical crime enullosure rates are typically somewhat higher than actual crime enullposure rates nulland significantly higher in Denvernull, suggesting that some portion of the reduction in enullposure of voucher holders to crime linullely resulted from shifts in the distribution of voucher holders towards lower crime neighborhoods. This shift suggests that ongoing mobility nullather than initial access to particular neighborhoodsnullcontributed a small amount to improvements in voucher neighborhood safety. Discussion nulling a number of different data sources, this article has described the enullent to which voucher households are enullosed to neighborhood crime, as compared with public housing residents, nullnullC tenants, and other poor renter households, in a representative sample of nullnull cities. Our nully finding is that, overall, in nullnull, voucher households occupied significantly lower crime neighborhoods than nullnullC and public housing tenants and slightly lower crime neighborhoods than poor renters as a whole. nulloucher households are less linullly to live in neighborhoods with particularly high crime than any of these comparison groups. nullterestingly, the safety benefits of vouchers appear to be especially pronounced for nullacnullvoucher holders. nullacnullvoucher holders lived in safer neighborhoods than other nullacnullrenters and nullacnullpoor households. null sum, vouchers appear to be helping low-income households reach safer neighborhoods or at least avoid neighborhoods that are the least safe. nullven the growing evidence about the importance of crime in shaping childrennull outcomes, this greater access provides an important argument in

151Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull favor of switching from reliance on production-based housing to reliance on vouchers. null the long run, our findings also suggest that, if given the means, a greater share of low-income households would be able to avoid very high-crime neighborhoods. null null is worth underscoring that, by limiting our analyses to large cities, we are linullly overstating the neighborhood crime rates faced by voucher holders and nullnullC tenants, because we are omitting the large number of them who live in suburban communities. null nullnull, voucher holdersnullnd nullnullC tenantsnullere much more linullly than public housing tenants to live in the suburbs nullevine et al., nullnullnull nullthough central city and suburban poverty rates have been converging in recent years, crime rates are still lower, on average, in suburban communities than they are in central cit- ies nullllen and Onullegan, nullnullnull nullnally, because patterns of suburbaninulltion may differ across races, the relative enullosure to crime of different subgroups might differ in the suburbs. Appendix A null collected crime data from one of three sourcesnullnullnulldirectly from police department websites or data requests to the department nullustin and nullattlenull nullnullfrom researchers who obtained these data from police departments nullhicagonull or nullnullfrom the nulltional nullighborhood nulldicators Partner - ship, a consortium of local partners coordinated by the nullban nullstitute to produce, collect, and disseminate neighborhood-level data nullleveland nullase nullstern nullserve nulliversitynull Denver nullhe Piton nullundationnull nulldianapolis nullhe Polis Centernull and Philadelphia nullhe nullinvestment nullndnull. nullr all cities, total, property, and violent crimes are included, and for all cities enullept Denver and nulldianapolis, crimes are further disaggregated into all Part nullcrimes nulliolent crimesnullssault, senullal assault, homicide, and robberynullproperty crimesnullarceny, burglary, motor vehicle theft, and arsonnull null null all cities enullept for Denver, neighborhoods are pronulled by census tracts. null Denver crime data are aggregated to locally defined neighborhoods, which are typically two to three census tracts. null Denver, we aggregated the tract-level housing data to the neighborhood level and linnulld these variables to the neighborhood crime data. null null addition to showing these positive impacts for households, the results suggest that vouchers do contribute to a different spatial distribution of subsidinulld households. null Philadelphia was unable to share numbers on senullal assaults or homicidesnullthus, those crimes are not included in overall totals or the individual categories. nullven Philadelphia crime data were available from nullnull through nullnull, we used nullnull and nullnull crime data to estimate nullnull numbers and nullnull and nullnull crime data to estimate nullnull numbers. null nullthough Denver data are at the neighborhood level, nullnullnullof our nullnullnullneighborhoods are equivalent to census tracts. Thus, in describing data and results, we often use the term nullensus tract.null

152 Crime and Urban Form nullns, Ellen, and O’Regan Exhibit null1 nulltnull Ponulllation nullnullinnull

nullits nullimes

nullr 1nullnull Peonulle nullimes

nullr 1nullnull nullnullinnullnullits V onullhers nullnullnull

nullits Pnulllic

nullnullinnull

nullits Rental nullits nullcnullied bnullPoor nullnulleholds City List annullnullscnullnullinulls fonullnullnullity Cnull ossnullection, null00 (1 of null nullnull on, OH null0,nullnull 10nullnullnull null 1null nullnullnull 1,nullnull nullnullnull 11,nullnull nullnullnullenull nulle, null null1,nullnull null1,nullnull null 1null null0null 1,nullnull nullnull 1nullnull1 nullenullnnullia, null null 1null,nullnull null,null1 null null null1 nullnull nullnull nullnullnull nullchonullge, null nullnullnullnull null,nullnull null 10null 1,nullnull null1 nullnull nullnullnull nulllington, null nullnullnullnull 1null,nullnull null 1null nullnullnull nullnullnull 0 null0null nullnull onull, null nullnullnull1 null,0null null null nullnull nullnull nullnull nullnullnull nullstin, null nullnullnullnull null0,nullnull null 1null nullnullnull nullnullnull null0null null,0null nullllenulle, null null 1null,nullnull null,nullnull null null nullnull nullnull 10null null1null nullston, null null1,nullnull nullnullnullnull null 1null nullnull0 10,nullnull nullnullnull null,null1 nullf falo, null nullnullnullnull 1null,null1 null 1null nullnullnull 1,nullnull nullnull0 null,nullnull Canull ollton, null 1null,nullnull null,1null null null 1null 1,01null null 1,nullnull Channullenull , null 1null,nullnull null,1null null 1null nullnull null0 nullnull null1null Chanullotte, null null0,1null nullnullnullnull null 1null nullnullnull nullnullnull nullnullnull 1nullnullnull Chicago, null nullnull1,1null 1,1null,nullnull null 1null null,null1 1nullnullnull null,null0 1null,nullnull Chula null ista, Cnull 1null,nullnull null,nullnull null 10null 1,nullnull nullnull 1null null0null Cincinnati, OH nullnullnullnull 1null,nullnull null 1null nullnullnull nullnull0 nullnullnull null,nullnull Clenulllannull OH null1,nullnull nullnullnullnull null 1null nullnullnull nullnullnull 10,nullnull null,null0 Columnulls, OH null0,nullnull null0,nullnull null 1null nullnullnull nullnullnull nullnullnull null,nullnull Conulll Snullings, null 1null,00null null,nullnull null null nullnull 0 0 null0null nullllas, null 1,nullnullnullnull nullnullnull1 null 1null 10,nullnull 1nullnull1 nullnullnull null,nullnull nullyton, OH 1null,null0 null,nullnull null 1null 1,nullnull nullnullnull nullnullnull 11,nullnull nullnnullnull , CO nullnullnullnull nullnullnullnull null 10null nullnull1 nullnullnull nullnullnull null,null1 nulls nullines, null nullnullnullnull null,nullnull null 1null 1,nullnull 1,nullnull nullnull nullnullnull nulltnull oit, null null0,0null nullnullnullnull 10null null0 null11null null1null nullnullnull null,0null Eugene, OR 1null,nullnull null,null0 null 1null 1,11null nullnull nullnull null01null Enullnsnulllle, null 1null,nullnull null,0null null 100 1,nullnull null1 null1 nullnullnull nullnull Collins, CO 1null,nullnull null,nullnull null null nullnull 1,0null 1null null00null nullnull null ayne, null nullnullnull0 null,1null null 11null 1,nullnull null0 nullnull nullnull1

153Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull nulltnull Ponulllation nullnullinnull

nullits nullimes

nullr 1nullnull Peonulle nullimes

nullr 1nullnull nullnullinnullnullits V onullhers nullnullnull

nullits Pnulllic

nullnullinnull

nullits Rental nullits nullcnullied bnullPoor nullnulleholds Exhibit null1 City List annullnullscnullnullinulls fonullnullnullity Cnull ossnullection, null00 (nullof null nullnull null onullh, null null0,nullnull nullnullnullnull null 1null nullnullnull null1null 1,1null 1nullnullnull nullllenullon, Cnull 1null,nullnull null,0null null null nullnull nullnull 0 null0null nullnull nulln null onull, Cnull nullnullnullnull null,nullnull null null 1,nullnull nullnull 0 nullnullnull nullennullle, null nullnull0null null,0null null 1null 1,0null null0 1null nullnullnull Hamnullon, null null 1null,nullnull null,null1 null 10null 1,nullnull nullnull nullnull nullnullnull Hanullfonull null Cnull 1null,nullnull null,nullnull null nullnull nullnullnull null1 nullnullnull 11,null0 Hialeah, null nullnullnullnull null,1null null 1null nullnullnull 10null 1,11null 11,null0 Houston, null 1,nullnull00null null0,nullnull null 1null nullnullnull nullnullnull nullnullnull null,null0 nullglenullonull Cnull 1null,nullnull null,nullnull null 10null 1,nullnull null 0 nullnullnull nullnullng, null 1null,null1 null,nullnull null 11null nullnull 1,nullnull 0 nullnull1 nullcnullonnulllle, null nullnullnullnull null1,nullnull null 1null nullnullnull null0null nullnullnull null,nullnull nullnsas City , null null0,0null nullnullnullnull null 1null nullnullnull null1null 1,0null 1nullnullnull nullonullille, null nullnullnullnull 101,nullnull null null 1,null0 nullnull nullnullnull 1nullnullnull Lenullngton, null nullnullnullnull 11nullnullnull null 10null 1,null0 nullnull 1,nullnull 11,nullnull Lincoln, null nullnullnullnull null,nullnull null 1null null0 1,0null null0 nullnullnull Linullnia, null 100,nullnull null,nullnull null null 1null 0 1null nullnull Long nullach, Cnull null0,nullnull 1null,nullnull null null nullnull1 nullnull nullnull null,null1 Los nullgeles, Cnull nullnullnullnull1 1,nullnull00null null 1null null,nullnull 1nullnullnull nullnullnull nullnullnullnull Louisnulllle, null nullnullnull0 1null,nullnull null null null00null 1,nullnull nullnullnull 1nullnullnull nullnullson, null nullnullnullnull 10nullnullnull null null 1,1null 1,nullnull nullnull 11,nullnull nullnulllen, null 11null0null null,nullnull null nullnull nullnull null 1null nullnullnull nullmnullis, null nullnullnullnull nullnullnullnull null 1null null0null null0null nullnullnull null,1null nullami, null nullnullnull0 1null,null1 null 1null nullnull1 nullnullnull nullnullnull null,nullnull nulllnullunulle, null nullnullnullnull nullnullnullnull null 1null nullnullnull 1,nullnull nullnullnull null,0null nullnneanulllis, null null0,null1 1null,nullnull null 1null nullnull1 nullnull nullnullnull 1null0null nullnullnullille, null 1null,nullnull null,null0 1null null 1null 1null 0 nullnull nullshnulllle, null nullnull0null nullnullnull1 null 1null nullnullnull nullnullnull nullnullnull null,0null nullnullHanulln, Cnull 11nullnullnull null,nullnull null 1null nullnullnull 1,0null null0null nullnullnull

154 Crime and Urban Form nullns, Ellen, and O’Regan nulltnull Ponulllation nullnullinnull

nullits nullimes

nullr 1nullnull Peonulle nullimes

nullr 1nullnull nullnullinnullnullits V onullhers nullnullnull

nullits Pnulllic

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nullits Rental nullits nullcnullied bnullPoor nullnulleholds Exhibit null1 City List annullnullscnullnullinulls fonullnullnullity Cnull ossnullection, null00 (nullof null nullnullonull nullnull, null null 1null,nullnull null,1null null 1null 1,null0 nullnullnull null1null nullnullnull nullnullolnull null null nullnull0null null,nullnull null 1null 1,nullnull 1,nullnull null0null 1null01null Oanullannull Cnull nullnullnullnull 1null,nullnull null 1null nullnullnull nullnullnull nullnullnull 1nullnullnull Onullahoma City , Onull nullnull0null nullnullnullnull null 1null nullnullnull nullnullnull nullnullnull null,0null Ontanullo, Cnull nullnullnullnull null,nullnull null 11null nullnull 1null null nullnullnull Onullnullannullnullnull, null 1null,nullnull null,nullnull null null nullnull nullnull 0 1,null0 nullsanullna, Cnull 1null,null1 null,nullnull null null 1,0null nullnull 0 nullnull0 nullsanullna, null 1null,nullnull null,null0 null 111 nullnull 1,nullnull 0 nullnullnull nullmnull onull nullnes, null 1null,nullnull null,1null null null null 0 0 1,0null nullilanulllnullia, null null 1,nullnullnullnull nullnullnullnull null 1null nullnullnull nullnullnull 1nullnullnull null,nullnull nulloeninull null 1,null1,null1 nullnull0null null 1null null1null nullnull nullnullnull null,001 nullttsnullnull gh, null null nullnullnullnull 1null,nullnull null 1null null0null nullnull nullnullnull 1nullnull0 nullano, null nullnullnullnull null,nullnull null null nullnull null0 null null001 nullnulllannull OR nullnullnullnull nullnullnullnull null 1null nullnullnull null1null nullnull0 null,nullnull Rocnullonull null null 1null,11null null,nullnull null 1null 1,nullnull nullnull nullnullnull null0null San nulltonio, null 1,nullnullnull1 nullnull0null null 1null 10,null1 1,nullnull nullnullnull null,nullnull San nullnull nanull nullno, Cnull nullnullnullnull null,0null null 1null nullnullnull nullnull nullnull 1nullnullnull San nullego, Cnull 1,nullnullnullnull null0,nullnull null null nullnullnull null1null 1,null1 null,nullnull Santa Rosa, Cnull 1null,0null null,null1 null null 1,nullnull 1,nullnull 0 nullnullnull Seattle, null null nullnull0null nullnullnull1 null 1null nullnull0 null1null nullnull0 1nullnullnull Simi null alley , Cnull 11nullnullnull null,nullnull 1null null nullnull nullnull 0 nullnull St. Louis, null nullnullnullnull 1null,null0 1null nullnull nullnullnull nullnullnull nullnull0 null,nullnull St. nulltenullnullnull g, null nullnullnullnull 1null,nullnull null 1null null0null null nullnull nullnullnull Stamfonull null Cnull 11null0null null,nullnull null null nullnull 1,0null null1 nullnullnull Stenulling Heights, null 1null,nullnull null,nullnull null null 1null null0 1null 1,1null null amnull, null null0,null1 1null,1null null nullnull nullnullnull null0 nullnullnull 1nullnullnull null emnull, null 1null,nullnull null,null1 null nullnull nullnull 0 0 null0null null olenull, OH null1,null1 1null,1null null 1null nullnull0 null10null nullnullnull 1nullnullnull

155Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull nulltnull Ponulllation nullnullinnull

nullits nullimes

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nullits Rental nullits nullcnullied bnullPoor nullnulleholds Exhibit null1 City List annullnullscnullnullinulls fonullnullnullity Cnull ossnullection, null00 (nullof null null onullnull, null 1null,1null null,nullnull null nullnull nullnull 1,nullnull nullnull nullnullnull null ucson, null nullnullnullnull nullnullnullnull null 1null nullnullnull nullnullnull 1,null0 null,nullnull null inull ginia nullach, null null nullnullnullnull 1null,1null null null null0 1,nullnull 0 nullnullnull null aco, null 1null,1null null,null0 null 1null 1,null0 nullnull nullnull nullnull0 null ashington, null nullnullnullnull nullnullnullnull null 1null nullnullnull nullnullnull 10,nullnull null,nullnull null atenullunull , Cnull 10nullnull1 null,nullnull null 1null nullnull nullnull nullnull nullnull1 null onull cestenull , null 1null,0null null,nullnull null 11null null01null 1,011 null1null nullnull1 Exhibit null2 1nullnull 1nullnull 2nullnull 2null1 2null2 2null3 2null4 2null5 2null6 2null7 2nullnull Senullnnullity Longitunullnal Cnullme nullta null City annullnull eanull , 1nullnullnull0null nullstin null null null null null null null null null null null Chicago null null null null null null null null null null null Clenulllannull null null null null null null null null null null nullnnullnull null null null null null null null null null null nullnullananulllisnull null null null null null null null null null null null nullilanulllnullianullnull null null null null null null null null null Seattle null null null null null null null null null null nulleignullornullod lenulllnullnullCrinull data nullssing for onenullalf of tnull tractsnullnulle tracts include d renullesent slignullly fenullr tnulln onenullalf of tnull Indiananulllis nullnulllationnullnullnullo nullnullcide or ranull datanull

156 Crime and Urban Form nullns, Ellen, and O’Regan Appendix B Exhibit null3 Exhibit null1 1nullnull 2nullnull Pnulllic nullnullinnull nullnull Vonullhers nullnull Pnulllic nullnullinnull nullnull Vonullhers nullnull nullear 1nullnull 2nullnull 2null4null 2nullnullnull nullnullcent of nullnullic Housing nullits annullnullouchenull Renullnullenullnull Housing nullthonullty, 1nullnullannullnull0null Senullnnullity Longitunullnal Samnulle, Cnullme annullHousing nullanullanulles, 1nullnullnull0null nullstin null null null null Chicago null null null null Clenulllannull null null 100 null nullnnullnull null null null null nullnullananulllis null null null null nullilanulllnullia null null null null Seattle null null null null Cnullmes nullnull1,000 nullonulle null.null null.null null.null null.null Cnullmes nullnull1,000 housing units 1null.null 1null.null 1null.null 1null.null nullmnullnullof nulluchenull null,null1 null,nullnull null,nullnull null,nullnull nullmnullnullof Lnullnull units null,nullnull null,null1 null,nullnull null,null1 nullmnullnullof nullnullic housing units null,1null null,nullnull null,1null null,nullnull nullilanulllnullia null null null null Seattle null null null null nullHnull nulllonullinconull nullusing tax creditnullnull nulldata not anullilablenull nullnulloucnullr counts for nullnull are lonullbecause of nullssing data in nulliladelnullia and SeattlenullnullnullHnull units renullrted use nullnull totalsnull null nulldata not anullilablenull nullnullonulledgnullnts The authors thannullthe staff at the nullnull Department of nullusing and nullban Development, Office of Policy Development and nullsearch, especially David Chase, nullrbara nullley, nullrnullnullroder, and nulldia Taghavi, for their helpful guidance and commentsnullnullbert nullay at nullonometrica, nullc., for his data assistancenulland Olga nullcalaban at the nullD nulloup, nullC, for her guidance. They also thannull nullily Osgood for research assistance and their colleagues at nullw nullrnullnulliversitynull nullrman Center for nullal nulltate and nullban Policy and participants in the nullnull nullsociation for Collegiate nullhools of Planning fall research conference in nullnneapolis for their useful insights and suggestions.

157Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull nulltnullrs nullchael C. nullns is an assistant professor of urban planning at the nulliversity of California, nulls nullgeles, nullsnulln nullhool of Public nullfairs. nullgrid nulluld nulllen is a professor of public policy and urban planning at nullw nullrnullnulliversity, nullgner nullhool of Public nullrvice. nulltherine Onullegan is an associate professor of public policy at nullw nullrnullnulliversity, nullgner nullhool of Public nullrvice. References nullnullr, nullna. nullnull. nullighborhood nullolence and nullban nulluth. nullnull nullrnullng Paper null. nullnullnull httpnullnullww.nber.orgnullapersnullnullnullnullpdf nullccessed nullne null nullnullnull nullrdicnullnullll, nulllia, nullns nulldwig, nullephen null nulludenbush, nullbert null nullmpson, nullsa nullnbonmatsu, and Patricnullnullarnully. nullnull. nullonverging nullidence for nullighborhood nullfects on Childrennull Test nulloresnullnull nullperimental, nullasi-nullperimental, and Observational Comparison.nullnulloonullngs nullstitution Pronullct on nullcial nullequality and nullucational Disadvantage null nullshington, DCnullnulloonullngs nullstitution. nullron, nullrry, nullsan null Popnulln, Diane null nullvy, nullura null nullrris, and nullll nulladduri. nullnull. nulle nullnull VnullResinullnt nullacnullng StunullnullnullSnapsnullt onulltnull Current nullving Situation onullnulliginal Resinullnts nullom Eignull Sites. nullport prepared for the nullnull Department of nullusing and nullban Development, Office of Public nullusing nullvestments. nullshington, DCnullThe nullban nullstitute. Case, nullne, and nullwrence nulltnull nullnull. The Company nullu nullepnullThe nullfects of nullmily and nullighborhood on Disadvantaged nulluth. nullnull nullrnullng Paper null. nullnull. httpnullnullww.nber.orgnull papersnullnullnull.pdf nullccessed nullrch null, nullnullnull Cullen, nulllie nullrry, and nulleven D. nullvitt. nullnull. nullrime, nullban nullight, and the Consequences for Cities,null nulle Review onullEconomics annullStatistics null nullnull nullnullnullnull Devine, Deborah null, nullbert null nullay, nullster nullbin, and nulldia null Taghavi. nullnull. nullusing Cnullice Voucnullr nullcation nulltternsnullnullplications nullr nullrticipants annullNeignullornullonullnulllnullre . nullport prepared for the nullnull Department of nullusing and nullban Development, Office of Policy Development and nullsearch. nullshington, DCnullnullnull nullvernment Printing Office. nulllen, nullgrid nulluld, and nulltherine null Onullegan. nullnull. nullrime and nullban nullight nullvisitednullThe nullfect of the nullnulls Drop in Crime on Cities,null nullurnal onullnullban Economics null nullnull nullnullnullnull nullnull nullnull. nullrime and nullnull Citiesnullnullcent Patterns and nullplications,null nullnals onulltnull nullerican nullanullmy onullSocial annullnulllitical Science nullnullnullnull nullnullnull nulllen, nullgrid nulluld, and nullrgery Turner. nullnull. nulloes nullighborhood nulltternullnullsessing nullcent nullidence,null nullusing nulllicy nullbate nullnullnull nullnullnullnull

158 Crime and Urban Form nullns, Ellen, and O’Regan nullins, nulldith D., and nullrnullD. nullroder. nullnull. nulloving to OpportunitynullThe Demonstrationnull Design and nulls nullfects on nullbility,null nullban Stunulles null nullnull nullnullnullnullnull nullrbarino, nullmes, nullncy Dubrow, nullthleen nullstelny, and Caro Pardo. nullnull. Cnulllnullen in nullngernull Coping nulltnulltnull Consenullences onullCommunity Violence . nulln nullancisconullnullssey-nullss. nullering, nullhn, nulldith D. nullins, and Todd null nullchardson. nullnull. null Cross-nullte nullalysis of nullitial nullving to Opportunity Demonstration nullsults,null nullurnal onullnullusing Researcnull null nullnull nullnull. nullering, nullhn, nulli nullmely, and Todd nullchardson. nullnull. nullecent nullsearch on nullcial nullgregation and Poverty Concentration in Public nullusing in the nullited nullates,null nullban nullnullirs Review null nullnull nullnullnullnull nulleenwood, nullchael null, and nullchard nullocnull nullnull. nullatterns of Change in the nulltra nulltropolitan nullcation of Populations, nullbs and nullusingnullnullnull to nullnull,null nullurnal onullnullban Economics nullnull nullnullnullnull nullnratty, nullria, nullra nullnullnahan, and nullcnull Pettit. nullnull. The nullpact of the nulls nullgeles nullving to Opportunity Program on nullsidential nullbility, nullighborhood Characteristics, and nullrly Child and Parent Outcomes. nullrnullng paper. Princeton, nullnullPrinceton nulliversity, Center for nullsearch on Child nullllbeing. nullrtung, nullhn null, and nullffrey null nullnig. nullnull. nullousing nulluchers and Certificates as a nullhicle for Deconcentrating the Poornullnullidence nullom the nullshington, DC nulltropolitan nullea,null nullban nullnullirs Review null nullnull nullnullnullnull nullels, nullcere, nulleg null Duncan, nullefanie Deluca, nullby nullndenhall, and nullmes nullsenbaum. nullnull. nullifteen nullars nullternullCan nullsidential nullbility Programs Provide a nullng-Term nullcape nullom nullighborhood nullgregation, Crime, and Povertynull nullmograpnull null nullnull nullnullnull nullngsley, null Thomas, and nullthryn nullnull Pettit. nullnull. nullve nullnullnullmilies nullst nullcess to nullportunity Neignullornullonull nuller nullme nullnullshington, DCnullnullban nullstitute Press. nullClure, nullrnull nullnull. nulleconcentrating Poverty nullth nullusing Programs,null nullurnal onulltnull nullerican nullanning nullsociation null nullnull nullnullnull nullnull nullnull. nullhe nullw-nullcome nullusing TanullCredit Program nulles nullinstream and nullves to the nullburbs,null nullusing nulllicy nullbate null nullnull nullnullnullnull nullesnullowsnull, Peter, and nullwin null nulllls. nullnull. nullhe Causes of nulltropolitan nullburbaninulltion,null nullurnal onullEconomic nullrspectives nullnullnull nullnullnullnull nullwman, nullndra null, and nulln null nullhnare. nullnull. nullnullnd a nullitable nullving nullvironmentnull The nullilure of nullusing Programs To Deliver on nullighborhood nullality,null nullusing nulllicy nullbate nullnullnull nullnullnullnull Orlebenull, Charles null nullnull. nullhe nullolution of nullw-nullcome nullusing Policy, nullnull to nullnull,null nullusing nulllicy nullbate null nullnull nullnullnullnull

159Cityscape null nulloucnullrs HelnullnullnullInconull Housenulllds nullnull in Safer nullignullornullodsnull Enulldence on tnull Housing Cnullice nulloucnullr nullogranull Pendall, nulllf. nullnull. nullhy nullucher and Certificate nullers nullve in Distressed nullighborhoods,null nullusing nulllicy nullbate null nullnull nullnullnullnull Peterson, nullth D., and nulluren nullivo. nullnull. nullCPnull nullnullnullnullnulltional nullighborhood Crime nulludy nullnullnull nullnull Codeboonullnullnulln nullbor, nullnullnullter-nulliversity Consortium for Political and nullcial nullsearch. nullbinowitnull nullonard null, and nullmes null nullsenbaum. nullnull. Crossing tnull Class annullColor nullnesnullnullom nullblic nullusing to nullite Suburbia . Chicagonullnulliversity of Chicago Press. nullmpson, nullbert null, nullephen null nulludenbush, and nulllton nullrls. nullnull. nulleighborhoods and nullolent Crimenullnullnullltilevel nulludy of Collective nullficacy,null Science nullnullnullnullnullnullnullnullnullnull nullhwartnull nullenullnull nullnull. nullusing nulllicy in tnull nullitenullStatesnullnull nulltronullction . nullw nullrnull nullutledge. nullarnully, Patricnull nullnull. nullhe nullute nullfect of nullcal nullmicides on Childrennull Cognitive Performance,null nulloceenullngs onulltnull National nullanullmy onullSciences nullnull nullnullnullnullnullnull nullafford, nulli, Tarani Chandola, and nullchael nullrmot. nullnull. nullssociation nulltween nullar of Crime and nullntal nullalth and Physical nullnctioning,null nullerican nullurnal onullnullblic nullaltnull null nullnullnullnullnull- nullnull. Turner, nullrgery nullstin, nullsan null Popnulln, and nullry Cunningham. nullnull. Section nullnullbility annull Neignullornullonullnullaltnull Emerging nullsues annullnulllicy Cnullllenges . nullshington, DCnullThe nullban nullstitute. nulltruba, nullrnull and nullffrey nulling. nullnull. nullffects of nullighborhood Characteristics on the nullrtality of nullacnullnullle nulluthnullnullidence nullom nullutreaunull Chicago,null Social Science annullnullnullcine null nullnull nullnullnullnull nullng, nullnhao, and David nullrady. nullnull. nullsing nullt-nullot nullalysis To nulludy the Clustering of nullction nullnullusing Choice nullucher nullmilies,null nullusing Stunulles null nullnull nullnullnull nullng, nullnhao, David nullrady, and nullmei nullng. nullnull. nulleasuring the Deconcentration of nullusing Choice nullucher Program nullcipients in nullght nullnull nulltropolitan nulleas nulling nullt-nullot nullalysis,null Cityscape null nullnull nullnullnull

160 Crime and Urban Form

161Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Modeling Criminal Distance Decay Minull O’nullarnull Towson nulliversity nullstract Criminal nullistance nullecay is tnulle nullunnullamental notion tnullat a relationsnullip enullists between tnulle nullistance nullrom an onullnullennullernulls nullome base to a potential target location annull tnulle linullelinulloonull tnullat tnulle onullnullennuller cnullooses to onullnullennull in tnullat locationnull nullnullis relationsnullip is important botnull nullor its operational enullnullect on police agencies annull on monullels nullor onullnullennuller benullaviornull null number onull nullactors innulluence tnulle nullistance nullecay nullunction onull an onullnullennuller, inclunulling tnulle local geograpnully annull tnulle onullnullennullernulls nullecisionmanulling processnull nullnullis article anullnullresses a stunully onull tnulle interactions between tnulle twonullnullimensional onullnullense nullistribution tnullat nullescribes nullow onullnullennullers select targets annull tnulle corresponnulling onenull nullimensional nullistance nullecay nullunctionnull nullt also presents tnulle calculation onull tnulle coenullnullcient onull variation nullor 32null resinullential burglary series in Baltimore County, nullarylannullnull nullnullese nullata nullo not support tnulle notion tnullat tnulle nullistance nullecay benullavior onull an innullivinullual onullnullennuller is governenull by a number onull common cnulloices nullor nullistance nullecay, inclunulling tnulle negative enullponential, even allowing nullor tnulle parameters to vary between onullnullennullersnull nullinally, tnulle article enullamines geograpnullic patterns nullor resinullential burglary in Baltimore County annull tnulle nullnnulling tnullat, altnullougnull onullnullenses committenull in rural portions onull tnulle county are commitnull tenull by onullnullennullers witnull larger travel nullistances, it nulloes not appear tnullat tnullis variation can be enullplainenull simply by tnulle local population nullensitynull Introduction Distance decay is the observed fact that offenders tend to commit more crimes closer to home than farther away. nullthough this qualitative fact is well nullown, no consensus has been reached about the correct quantitative approach to distance decay. nullveral researchers have put forward a number of mathematical models. nullevine nullnullnullnullnullnull, chapter nullnull, for enullample, discusses five different mathematical forms for distance decay built into the Crimenullat toolnullCrimenullat also enables the user to create his

162 Crime and Urban Form O’nullary or her own empirically defined distance decay function. This notion that an offendernulls crime locations are related in some meaningful way to the offendernull home location is central to the idea of geo - graphic profiling and motivates this wornull Our primary interest is in the distance decay behavior of a single offender over the course of a sin- gle crime series. null assume that the offender has a single, well-defined home base or anchor point, which can be the offendernull home, wornulllace, or other well-defined location of importance to the offender that serves as the origin of the offendernull base of operations. null acnullowledge that this assumption does not apply to all offenders, because not all offenders meet these criteria. null also assume that each crime site has a single well-defined location, but we also acnullowledge that this need not be the case for every crime. nulldeed, some crimes may tanull place in multiple locations. null an enullmple, in a car theft, not only is there the location from which the vehicle was stolen but also the possible chop shop location or the location where the vehicle is abandonednullalso see null nullnullnull and the discussion of the nullourney-after-crime.nullnull distance, we mean the distance from the anchor point of the offender to the location of the crime site. This distance can be measured in a number of different metrics, with nullclidean and nullnhattan distance metrics being the most common but not the only reasonable choices. null also note that the distance the offender actually traveled may be at variance with the distance from the anchor point to the crime site, because it is possible that the actual nullurney to the crime site began at some point other than the offendernull anchor point. nullee also nullles and Costello, nullnullnullnullnttila et al., nullnull.nullnullnally, because we are considering the distance decay behavior of individual offenders, we note that these behaviors may vary, based on particular characteristics of the offender, the crime type, andnullr the underlying local geography and landscape. null this article, we focus on the mathematics of distance decaynullin particular, we enullmine differ - ent quantitative models for distance decay behavior of an individual offender. null discuss the important relationships between one-dimensional models of distance and the corresponding two- dimensional models of offender behavior and show that the mathematics is responsible for some perhaps nonintuitive results. null then discuss a new method to analynull the distance decay curve for an individual offender, using aggregated data by analynullng a graph of the coefficient of variation for different offenders. nullnally, we loonullat how geography affects the distance decay behavior of offenders, paying particular attention to the role of population density. Literature Clearly, the distance from the offendernull anchor point to the offense site is one of the important variables that characterinull the geography of the offensenullnull however, it is also important to remember that offenders select targets, not distances. null particular, the local geography and the underlying two-dimensional nature of space must be accounted for in any analysis of distance decay patterns. nullthough offenders select targets, not distances, it does not mean that distance should be consid - ered to be solely a dependent variable that arises as a consequence of a particular target choice. null noted by nullrnasco and nullocnullnullnullnull, the distance to the target may be one of the criteria that the offender uses to manull the decision on where to offend.

163Cityscape Modeling Crinullnal nullstance nullcay The fact that the distance to the offense site is one decision variable is the only reason that tech- niques such as geographic profiling can wornull null the offender selected targets without reference to their distance from the offendernull anchor point, then the crime site location would be mathemati - cally independent of the anchor point location and, in this hypothesinulld scenario, it would not be possible to draw inference about the anchor point location from the locations of the elements of the crime series. null reality, it has been well established that distance decay enullsts, at least for crime data at the aggregate level, because this behavior has been seen in numerous studies. nullr enullmple, van nullp - pen and nullnsen nullnullnull found strong evidence of the enullstence of a distance decay effect in their study of nullnullrobberies committed by nullnulloffenders in the nulltherlands in nullnull. nullrnasco nullnullnull analynulld and compared the distance decay behavior for nullnullindividual and nullnullgroup residential burglaries in The nullgue, the nulltherlands, between nullnull and nullnullnullhe found clear evidence that the behavior of both classes enullibited a distance decay pattern. nullnally, nullnttila, nullunullanen, and nullppalnullnullnullnull found strong support for distance decay in their study of a collection of rapes and homicides that toonullplace in nullnland between nullnull and nullnull. Distance decay has been seen to enullst in many areas of human behavior other than criminal behaviornullsee, for enullmple, nullmpel et al. nullnullnull, who studied distance decay effects in voting pat - terns and found a relationship between voting preferences and the distance from the voter to the candidatenull hometown. null general, the question is not to nullow if distance decay enullsts, but rather to understand more about its quantitative form. null one approach to this question, Canter and nullmmond nullnullnull evaluated a number of candidate models for distance decay, with the goal of selecting the form that provided the best results when incorporated into Dragnet. nullfore we consider different possible mathematical forms for distance decay, it is important to note that the distances offenders are willing to travel depend on a number of different factors, such as the type of crime. nullesseling nullnullnullnullnullnull found that for crimes committed in residential areas of nulltrecht, violent crimes and vandalism were generally committed by more local offenders than were property crimes. Distance decay may depend on characteristics particular to an individual offender. null their study of nullnullnullcommercial robberies in the nulltherlands in nullnull and nullnull, nullrnasco and nulloistra nullnullnull found that offenders were more than eight times more linullly to offend in an area where they had lived in the past than in comparable areas where they had not livednull nullernasco nullnullnullnullnullnull obtained similar results in a study of residential burglary, theft from vehicle, robbery, and assault. The offendernulls age also appears to play a role in distance decaynullin a study of null serial burglars in null. nullhnnull, nullw - foundland, nulloonullnullnullnull noted variation in the travel distances of offenders depending on the offendernull age and the value of the stolen property. nullmilarly, nullles and Costello nullnullnull noted cor - relations between the offendernulls age and the offense distance for a range of crime types in nullheffield, nullngland. They compared these age-related effects for offenders from nullnullnullnull with offenders from nullnullnullnull. Characteristics of the criminal event itself are linnulld to variations in the distance offenders are willing to travel. nullr enullmple, van nullppen and nullnsen nullnullnull enullmined nullnullcommercial robberies in the nulltherlands and found not only that more professional offenders were willing to travel farther but that the more difficult the target, the farther the offendernullnulltended to travel. null a study

164 Crime and Urban Form O’nullary of null homicides and null rapes in nulllan, nullaly, nullnttila et al. nullnullnull found that more instrumental crimes were correlated with larger travel distances for the offender. null another study of null homi - cides and null rapes in nullnland, nullnttila, nullunullanen, and nullppalnullnullnullnull identified crime features that were correlated with distancenullfor enullmple, longer distances were related to homicides that toonullplace either in the inner city or at the victimnull home. null their analysis of a selected set of nullnull rapes committed by nullnullserial rapists, nullrren et al. nullnullnull found a number of characteristics of both the offender and the crime scene that were correlated with the distance traveled by the of- fender. nullnally, in their study of commercial robbery series in nulleater nulllsinnull from nullnull through nullnull, nullunullanen and nullnttila nullnullnull found small but statistically significant correlations between particular elements of the offense and the distance traveled. Crime rates, and especially burglary rates, are nullown to depend on the characteristics of the local neighborhoodnullfor enullmple, nullrnasco and nullynull nullnullnull identified three neighborhood characteris - tics they termed attractiveness, opportunity, and accessibility to burglars and found that they were correlated with increased burglary rates in The nullgue. null a later study of nullnullresidential burglaries in The nullgue, nullrnasco and nulliuwbeerta nullnullnull found a number of neighborhood characteristics associated with an increased risnullof burglary, including the neighborhoodnull ethnic composition and the relative number of single-family dwellings. nullre recently, in their study of null,nullnullrobber - ies in Chicago from nullnull to nullnull, nullrnasco and nullocnullnullnullnull found that social barriers such as racial and ethnic differences and gang territorial boundaries affect the choice of a target location. nullnt and nullitner nullnullnull enullmined a minullof null burglary and robbery series in nullltimore County from nullnull to nullnull and compared the efficacy of circular geographic profiles with ellipses. nullt only did they find that the ellipses performed better, they also found that the orientation of these ellipses were correlated with the orientation of the local road networnull suggesting that the pattern of crimes might be related to the road networnullorientation. nullen considering the geographic or geometric pattern of offense locations, it is important to con - sider direction as well as distance. null their analysis of null burglary series in rural nullstralian towns, nullocsis et al. nullnullnullnullnullnull found that the paths from the anchor point to the offense sites tended to proceed in the same direction. nullmilarly, nullodwill and nullison nullnullnull enullmined selected burglary series, rape series, and murder seriesnullthey also found that the paths from the anchor point to the offense site tended to lie in the same direction. nulle also null nullnullnull, who enullmined the direction between the auto theft location and the vehicle recovery location for nullnullnullincidents in nullffalo, nullw nullrnull One potential starting point for researchers constructing quantitative models for the distance decay behavior of offenders is to loonullat the distance decay graph for aggregated groups of offenders. These graphs have been generated for a large range of offense types and nullrisdictions. null mention nullrren et al. nullnullnull, who provide a graph of the aggregated distance decay curve for nullnullrapes committed by nullnullserial rapists, and van nullppen and nullnsen nullnullnull, who provide a graph of the distribution of distances traveled to offend for nullnullrobberies committed by nullnulloffenders in the nulltherlands in nullnull. nulloonullnullnullnull provides an aggregate distance decay graph for residential burglars in null. nullhnnull, nullwfoundland, while nullunullanen and nullnttila nullnullnull provide a graph of the aggregate distance decay for a commercial robbery series in nulleater nulllsinnull, nullnland, from nullnull to nullnull. Canter and nullmmond nullnullnull provide an aggregate distance decay curve for null selected nullnull serial murderers. This type of analysis has been done even for offenders in nulldianullnullrangi and

165Cityscape Modeling Crinullnal nullstance nullcay nullungs nullnullnull have aggregate distance decay graphs for serial burglars in both the nullurnullla and nullonnullar districts. nullnttila et al. nullnullnull present a distance decay curve for a collection of homi - cides, rapes, and robberies against businessesnullthe data in this graph are not only aggregated across offenders, but are also aggregated across offense type. nullnally, in an interesting study, null nullnullnull enullmined the distance from the vehicle theft location to the vehicle recovery location for nullnullnull incidents in nullffalo and provided an aggregate distance decay curve. One manullr application of distance decay has been in the development of geographic profiling methods. Three main approaches to the geographic profiling problem use distance decaynullthe method of nullssmo nullnullnull, chapter nullnullthe method of Canter et al. nullnullnullnulland the method of nullvine nullnullnull. nullee also the approach of Onulleary, nullnull.null nullo consensus has been reached about the best method to evaluate the effectiveness of these systemsnull see the original report prepared for the nulltional nullstitute of nullstice nullich and nullively, nullnullnull the critique of nullssmo nullnullnullnull and the response of nullvine nullnullnull. nullnumber of different researchers have enullamined the effectiveness of particular systems, however, and how to best choose the parameters that they use. nullor enullample, nullarangi and nulloungs nullnullnullnullnullnull applied Dragnet to nullnull nullndian serial burglars committing nullnulloffenses and they found that Dragnet was an effective tool, while Canter and nullammond nullnullnullnullnullnull assessed the effectiveness of the Dragnet system on nullnull null.null. serial murderersnull they compared the search cost effectiveness of four different distance decay functions. nullimilarly, nullaunullnullanen and nullnttila nullnullnull applied Crimenullat to null commercial robbery series in nulleater nulllsinnull, using empirically determined distance decay curvesnullthey found that Crimenullat null was able to reduce the search area to roughly null percent of the total study area. nullent, nulleitner, and Curtis nullnullnullnullnullnull constructed different distance decay functions by matching them to aggregate data and then compared their effectiveness as the distance decay function in Crimenullat, using a single serial nullller in nullton nulluge, nulluisiana. nullt only were these comparisons made across different distance decay functions, they were also made across different distance metrics, including nullclidean, shortest path, and shortest time metrics. nullor a comparison of the different approaches to one another, see Paulsen nullnullnullnullnulla, nullnullnullnullbnull, who compared the methods with one another and with humans on crime series from nullltimore Countynull he found that none of these approaches were significantly better than simple centrographic measures. nullr a broader discussion of the comparison of geographic profiling systems with hu - mans, see nullnoonull, Canter, and nullennell nullnullnullnullnullnullnull nullnoonull, Taylor, and nullennell nullnullnullnullnullnullnull nullossmo nullnullnullnullnullbnullnull and nulloonull Taylor, and nullnnell nullnullnullnull nulllated discussions also occur in nullnoonull, Taylor, and nullennell nullnullnullnullnullanullnull nullossmo and nulliler nullnullnullnullnullnullnull nullennell, nullnoonull, and Taylor nullnullnullnullnullnullnull and nullossmo, nulliler, and nullesley nullnullnullnullnullnull and in the papers of nullennell et al. nullnullnullnullnullnull and nullennell, Taylor, and nulloonullnullnullnull. nullocnulland nullrnasco nullnullnull applied the nullyesian nullurney to Crime method of Crimenullat nullnullto null serial burglars in The nullgue and evaluated its accuracy using four different measuresnullthey found that the new nullyesian method was an improvement over distance decay methods. nulleographic profiling methods have been applied in a variety of circumstances, including applications to obscene phone calls to children in nulleden nullbberline, nullnullnulland to terrorist attacnull nullennell and Corey, nullnullnull nullnnell and Corey nullnullnull enullmined attacnull by nulltion Directe in the nullnulls in Paris and a series by the nullanastatinulls nullinulls nullonas in the nullhens area in the nullnulls and nullnulls.

Unknown

166 Crime and Urban Form O’nullary Outside the broad area of law enforcement, nullscema et al. nullnullnull used ideas borrowed from geo - graphic profiling to estimate the location of a point source for an outbreanullof an epidemic disease. One interesting feature of their approach that has not yet been seen in criminology is that they attempt to not only model the distance from the source to a particular location, they also consider the energy enullended to manull the nullurney. nullographic profiling has also been applied to problems in ecologynullnull Comber et al. nullnullnull and nulline, nullssmo, and null Comber nullnullnull applied these techniques to problems in animal foraging, while nullrtin, nullssmo, and nullmmerschlag nullnullnull enullmined sharnullpredation patterns. nullantingham and Tita nullnullnull discussed a model for offender motion based on ideas from foraging theory in ecology. null particular, they enullmined a model for offender motion based on a nullvy nullght and presented the results of a number of simulations. One application of this approach to offender modeling is the geographic profiling technique of nullhler and nullort nullnullnull, which is based on a similar nullnetic model for the offendernull behavior. nullinally, although our emphasis throughout is on the application of distance decay to the geographic profiling problem, distance plays a role in other questions in criminology. nullr enullmple, nulllleson, nullans, and nullnnullns nullnullnull presented an agent-based model for burglary rates, which they then applied to nulleds, nullgland. Their models incorporate a strong distance decay component, in which the agentnull linullliness of offending is inversely proportional to a function of the distance. Dimensionality and Distance Decay Criminal distance decay is the notion that a relationship enullsts between the distance from an offendernull anchor point to a potential target location and the linulllihood that the offender chooses to offend in that location. null this article, to manull this idea mathematically precise, we define the distance decay function null to be the probability density that the offender chooses a target at a speci- fied distance r from his or her anchor point. null call this function nullrnull nullthen to find the probability null that the distance to the offense is between the numbers a and b, we simply sum the density and calculate null null ∫ a b 0 ∞ r r + ∆r∞ –∞ ∞ –∞ nullrnullnull . nullte that, because nullrnull is a probability density and because the distance r must be nonnegative, we nullow that nullrnull ≥ nullfor all r and ∫ a b 0 ∞ r r + ∆r∞ –∞ ∞ –∞

nullrnullnull nullnull Throughout this article, we use the phrase nullistance decaynullbecause it historically has been the term used to describe this phenomenon. null will not assume that the distance decay distribution, however, be monotone decreasing, but instead we enulllicitly allow for the possibility that increasing the distance from the anchor point can increase or decrease the probability density that an offense tanulls place at that distance. One immediate consequence of this definition of distance decay is that we are necessarily focusing our attention on the behavior of a single individual. null are enulllicitly allowing for the possibility that the distance decay distribution varies among offenders and that it may be innullenced by the crime type, characteristics of the criminal events, the local geography, and other factors. nullcause offenders select targets rather than distances, it is clear that the truly fundamental quantity that describes offender target selection is the two-dimensional probability distribution that describes how offenders select targets.

167Cityscape Modeling Crinullnal nullstance nullcay nulldeed, assume that points in the geographic region under study can be represented by pairs x nullnull null nullnull , null nullnull null where the coordinates null nullnull and null nullnull represent the distances of the point x from a convenient pair of perpendicular reference anulls. nullr simplicity in what follows, we will always as - sume that the offendernull anchor point is located at the origin in this coordinate system. null define the offendernull offense distribution nullnullxnullnullnull null nullnull , null nullnull nullto be the probability density that the location x is selected by the offender as the location of an offense. Then, for any geographic region Ω, we can find the probability null that an offense occurs within Ω by calculating null null ∫∫ Ω nullnull nullnull , null nullnull null null nullnull null nullnull . nullecause null is a probability density, we nullnow that nullnullnull nullnullnull , null nullnullnull null ≥ null and null null ∫ a b 0 ∞ r r + ∆r∞ –∞ ∞ –∞ ∫ a b 0 ∞ r r + ∆r∞ –∞ nullnullnull nullnullnull , null nullnullnull null nullnull nullnullnull nullnull nullnullnull null null. nullssmo nullnullnull nullnull calls a three-dimensional map of the offense distribution a nullopardy surface. null we previously noted, the behavior of the offender, and thus the offense distribution null can depend on factors unique to the individual committing the offensesnullthe crime typenullthe crime characteristicsnulland the local geographic, demographic, and other characteristics of the region. The geometry of space imposes a fundamental relationship between the two-dimensional offense distribution nullnullnull nullnull , null nullnull nulland the corresponding one-dimensional distance decay distribution nullrnull . To see this relationship, suppose we are using nullclidean distance, and we want to determine the probability null that an offense occurs at a distance between r and r null ∆r. To calculate null we sum the values of null on the annulus Ω with inner radius r and outer radius r and r null ∆r, giving nullnull ∫∫ r≤ |x|≤r + ∆r nullnull nullnull , null nullnull nullnull nullnull null nullnull . null we manull the simplifying assumption that the offense distribu - tion null depends only on the distance to the anchor point, then null is roughly constant on that annulusnull say null xnull ≈ nullrnull for r ≤ |x| ≤ r + ∆r. The area null of the annulus can be calculated simply by tanullng the difference of the area enclosed by the outer circle from the area enclosed by the inner circle, so nullnull pnull + ∆rnull 2 null pr 2 null2 pr ∆r null pnull∆rnull 2 nullthus, the probability that the offense tanulls place in the annulus is the product of the probability density and the area of the region, and so satisfies null ≈ null pr∆r null pnull∆rnull 2 nullnullrnull On the other hand, our definition of the distance decay distribution nullnullrnull tells us that the probability that the offense lies at a distance between r and r + ∆r is nullnull ∫ a b 0 ∞ r r + ∆r∞ –∞ ∞ –∞ nullsnullnull . null we manull the same sim - plifying assumption and assume that null is roughly constant on the interval nullr, r + ∆rnullwith the value nullrnull , we also have the appronullmation null ≈ nullnull null ∆rnullnullr nullnullrnullnullnull ∆rnullnullnull formed by tanullng the product of the length of the interval with the value of the function on that interval. Combining these two different enullressions of the same quantity null, and canceling the factor Δr from both sides, we obtain the relationship nullrnull ≈ null pr null p∆rnullnullnull and for small Δr, we see that nullrnull ≈ null prnullnullnull nulllthough this derivation proceeded via a number of mathematical appronullimations, these appronullima- tions are not germane and it is simple to replace this argument with a formal mathematical proof. The fundamental relationship between the offense distribution and the distance decay function nullrnull = 2pr nullrnull holds whenever the underlying two-dimensional offense distribution null depends only on distance where nullclidean distance is being used. To illustrate this fundamental result, suppose we place nullnullpoints uniformly randomly throughout the square shown in enullibit null effectively choosing the constant value of nullnullnull on the square nullnullnullnullnull,null.

168 Crime and Urban Form O’nullary nullur subregions are shownnulla disnullof radius nullnull and annuli of width nullnull with inner radii at nullnull nullnull and nullnull nullsimple count shows that no points enullst within the inner disnullwhile the annuli contain null null, and null pointsnulluite near the enullected results, which would be nullnull nullnull null.null and null.null nullthough the hypothesinulld underlying two-dimensional offense distribution is constant, the fundamental relationship tells us that the associated distance decay function should be linear, increasing as r gets large and tending to nullro as r tends to nullronullhich is enullctly what we observe. nullengert, Piquero, and nullones nullnullnullnullnullnull, in their critique of the wornull of van nulloppen and de nulleinullser nullnullnullnullnullnull, have already noted that one must pay attention to the difference between the one-dimensional distance decay and the two-dimensional offense distribution. To understand the consequences of the fundamental relationship, consider what happens when we apply it to some common distributions. Probably the most natural two-dimensional offense distribution is the bivariate normal centered at the offendernull anchor point, which we have placed at the origin of the coordinate system so T(x) = exp ( – ) 2piσ 2 1 2σ 2 |x| 2 D(r) = exp ( – σ 2 r 2σ 2 r 2 ) . null this case, the corresponding distance decay function nullrnull is T(x) = exp ( – ) 2piσ 2 1 2σ 2 |x| 2 D(r) = exp ( – σ 2 r 2σ 2 r 2 ) – 1.0 – 0.5 0.0 0.5 1.0 – 1.0 – 0.5 0.0 0.5 1.0 Exhibit 1 null0 nullints nullacenullnullifonullly Rannullmly nullnulloughout a Snullanulle

169Cityscape Modeling Crinullnal nullstance nullcay 10 2 3 4 0.1 0.2 0.3 0.4 0.5 0.6 4 2 0 – 2 – 4 4 2 0 – 2 – 4 0.00 0.05 0.10 0.15 which can be recogninulld as a nullyleigh distribution. null we graph both of these functions with σnull, we obtain enullibit null Despite the shape of the distance decay function in enullibit null, it is clear from the offender distri - bution in enullibit null that this offendernull behavior does not enullibit a buffer nullne. nulldeed, the offense distribution shows that the offender is more linullly, rather than less linullly, to offend at locations closer to his or her anchor point. The distance decay curve nullnullrnullvanishes as r g null not because of the enullstence of a buffer nullne, but rather because of the underlying two-dimensional nature of space. null r g null the area available to offend decreases, and thus the distance decay function decreases, precisely in line with our fundamental result. Exhibit 2 nullnull nullienull of the Same nullocessnullhe Offense nullstnullnulltion null nullnullnullate nullnullal annull the nullstance nullcay nullnction null Rayleigh (a) Offense nullstnullnulltion (null nullstance nullcay function null the distance decay function is nullown, it is possible to use the fundamental result to construct a two-dimensional offense distribution that matches that distance decay. nullt should be noted, however, that this two-dimensional offense distribution is not unique, and that for any distance decay function an infinite number of two-dimensional offense distributions possess the same distance decay function. Only one two-dimensional distribution enullsts, however, that both matches a given distance decay curve and depends solely on the distance from the anchor point to the potential offense sitenullthis distribution satisfies T(x) = D(|x|) 2pi|x| 2pi A + Br if A + Br ≥ 0 0 if A + Br < 0 1 D(r) = { Br if r ≤ r p Ae –Cr if r > r p . D(r) = { D(r) = exp ( – ) S A 2S 2 (r – r) 2 2pi D(r) = exp ( – ) r 2 S A 2S 2 [ln(r 2 ) – r] 2 – – if we continue to assume the use of a nullclidean distance. The Crimenulltat nullanual nullnullevine, nullnullnullnull, chapter nullnullnull provides five built-in choices for a distance decay curvenullthey are null nullnear T(x) = D(|x|) 2pi|x| 2pi A + Br if A + Br ≥ 0 0 if A + Br < 0 1 D(r) = { Br if r ≤ r p Ae –Cr if r > r p . D(r) = { D(r) = exp ( – ) S A 2S 2 (r – r) 2 2pi D(r) = exp ( – ) r 2 S A 2S 2 [ln(r 2 ) – r] 2 – –

170 Crime and Urban Form O’nullary null nullgative enullonential nullrnullnullnull nullr null nullrmal T(x) = D(|x|) 2pi|x| 2pi A + Br if A + Br ≥ 0 0 if A + Br < 0 1 D(r) = { Br if r ≤ r p Ae –Cr if r > r p . D(r) = { D(r) = exp ( – ) S A 2S 2 (r – r) 2 2pi D(r) = exp ( – ) r 2 S A 2S 2 [ln(r 2 ) – r] 2 – – null nullgnormal T(x) = D(|x|) 2pi|x| 2pi A + Br if A + Br ≥ 0 0 if A + Br < 0 1 D(r) = { Br if r ≤ r p Ae –Cr if r > r p . D(r) = { D(r) = exp ( – ) S A 2S 2 (r – r) 2 2pi D(r) = exp ( – ) r 2 S A 2S 2 [ln(r 2 ) – r] 2 – – null Truncated negative enullonential T(x) = D(|x|) 2pi|x| 2pi A + Br ≥ 0 if A + Br < 0 1 D(r) = { Br if r ≤ r p Ae –Cr if r > r p . D(r) = { D(r) = exp ( – ) S A 2S 2 (r – r) 2 2pi D(r) = exp ( – ) r 2 S A 2S 2 [ln(r 2 ) – r] 2 – – null can then graph each of these distance decay functions together with the corresponding two- dimensional offense distributionnullthese graphs are shown in enullibits nullthrough null To create each of these enullibits, we used the default parameters provided in the Crimenullat nullnual nullevine, nullnull, chapter nullnull thus, the horinullntal anulls in each graph should be considered to be distances measured in miles. nullcause we are using the parameters from that manual, the distance decay curves do not satisfy the normalinulltion requirement ∫ 0 ∞ m m+Δm 2 nullrnullnull nullnull This does not affect Crimenullat because it uses these functions to generate hit scores for which comparisons are made between regions where these scores are large and areas where these scores are small. nullch of these distance decay curves can be made into a properly scaled probability distribution by simply scaling each function by an appropriate multiplicative constant. 100 20 30 40 0.5 1.0 1.5 40 20 0 – 20 – 40 40 20 0 – 20 – 40 0.00 0.05 0.10 0.15 0.20 Exhibit 3 Lineanullnullstance nullcay of Lenullnenull null = 1.null B = null0.0null (a) Offense nullstnullnulltion (null nullstance nullcay function

171Cityscape Modeling Crinullnal nullstance nullcay 10 20 30 40 0.5 1.0 1.5 40 20 0 – 20 – 40 40 20 0 – 20 – 40 0.00 0.05 0.10 0.15 0.20 0 Exhibit 4 nullgatinull Enullonential nullstance nullcay of Lenullnenull null = 1.null, B = null0.0null (a) Offense nullstnullnulltion (null nullstance nullcay function 10 20 30 40 0.5 1.0 1.5 2.0 2.5 40 20 0 – 20 – 40 40 20 0 – 20 – 40 0.00 0.05 0.10 0.15 0.20 0 Exhibit 5 nullnullal nullstance nullcay of Lenullnenull null = null.null S = nullnull ¯r = nullnull (a) Offense nullstnullnulltion (null nullstance nullcay function nullamining the graphs of these offense distributions, we see that none of these distributions enullibit a buffer nullne. null fact, in all these graphs, the two-dimensional offense distribution is strongly concentrated at the origin, and, in four of the five graphs, the offense distribution becomes infinite as we approach the origin. The only enulleption is the truncated enullonential in enullibit null although that may not be clear from enullibit null. nullcloser loonullnear the origin of that distribution provided in enullibit nullshows that the two-dimensional offense distribution actually enullibits a nullt plateau in the region r nullr p .

172 Crime and Urban Form O’nullary Exhibit 6 Lognonullal nullstance nullcay of Lenullnenull null = nullnull S = nullnull ¯r = nullnull (a) Offense nullstnullnulltion (null nullstance nullcay function 0 10 20 30 40 0.2 0.4 0.6 0.8 1.0 1.2 1.4 40 20 0 – 20 – 40 40 20 0 – 20 – 40 0.00 0.05 0.10 0.15 0.20 Exhibit 7 nullnulluncatenull nullegatinulle Enullnullonential nullistance nullecay of Lenullinenull null = 1null.nullnull, B = nullnull.null, C = 0.null, r null = 0.null (a) Offense nullstnullnulltion (null nullstance nullcay function 100 20 30 40 2 4 6 8 10 12 14 40 20 0 – 20 – 40 40 20 0 – 20 – 40 0 1 2 3 4 nullssmo nullnullnull tanulls a different approachnullhe uses a nullnhattan distance metric together with a piecewise rational function for the distance decay function. null analogue of our fundamental result for the nullnhattan distance is presented, however. nullppose that we want to find the probability that an offense occurs where the nullnhattan distance is between m and m null Δm. To calculate this, we need to sum the values of null over the annular region Ω outside the square |null nullnull | null |null nullnull | ≤ m and inside the square |null nullnull | null |null nullnull | ≤ m null Δm, giving us null null ∫∫ m null |null nullnull | null |null nullnull | ≤ m null Δm nullnull nullnull , null nullnull nullnull nullnull null nullnull null

173Cityscape Modeling Crinullnal nullstance nullcay Exhibit null nulle Centenullof the Offense nullstnullnulltion nullnenulltenullfonullthe nullnullncatenullnullgatinull Enullonential nullstance nullcay of Lenullne null again assume that null is roughly constant on this region, with a value that depends only on the nullnhattan distance m so null xnull ≈ nullmnull . To find the area of this region, we note that the region |null nullnull | null |null nullnull | ≤ m is a square of side length m ∫ 0 ∞ m m+Δm 2nullthus to find the area null of the region m null |null nullnull | null |null nullnull | ≤ m null Δm, we tanulle the difference of the areas of the concentric squares and find null null ﴾nullm null Δmnull ∫ 0 ∞ m m+Δm 2 ﴿ 2 nullnull ∫ 0 ∞ m m+Δm 2null 2 nullnull Δm null2null Δmnull 2 . Thus P ≈ nullnull Δm null2null Δmnull 2 nullnullnull On the other hand, our definition of the distance decay distribution tells us that the probability that the offense lies at a nullnhattan distance between m and m null Δm is nullnull ∫∫ 0 ∞ m m+Δm 2 nullsnullnull ≈ nullnull null Δmnullnullm nullnullmnullnullnull Δmnullnullnull where again we are assuming that null is roughly constant on the interval nullm, m null Δmnull, with value nullnullmnull Combining these two enullressions for null and considering Δm small, we see that for nullnhattan distance, the fundamental relationship is nullmnullnull nullmnullmnull nullain, although our argument proceeded by appronullmation, this result can be proven rigorously. nullssmo uses the offense distribution rather than the distance decaynullhis form is T(x (1) , x (2) ) = (|x (1) | + |x (2) |) f if |x (1) | + |x (2) |

≥ b if |x (1) | + |x (2) |

≤ b. k (2b – |x (1) | – |x (2) |) g kb g–f { nullr criminal profiling, nullssmo recommends the choice null null g nullnullnull nullossmo, nullnullnullnullee also null Comber et al., nullnullnull, null.nulland nulline, nullssmo, and null Comber nullnullnull for applications outside criminal profilingnull The buffer nullne parameter b is set depending on the characteristics of the crime series, while null is simply a scaling parameter nullossmo, nullnullnull null1.0 null1.0 null null null null null0.null null0.null 0.0 0.0 0.null 0.null 1.0 1.0

174 Crime and Urban Form O’nullary The offense distribution and the distance decay function are graphed in enullibit null nullmediately seen is the effect caused by the use of the nullnhattan distance rather than the nullclidean distance in that the offense distribution is no longer radially symmetricnullthis behavior is more clearly seen in enullibit null, which shows the center of the offense distribution. The distance decay function decays rapidly as m g null although it decays very slowly for large values of m. Exhibit 1null nulle Centenullof the Offense nullstnullnulltion of Rossmo Exhibit null Rossmo nullonullelnull f = g = 1.nullnull null = 0.nullnull k = 1 (a) Offense nullstnullnulltion (null nullstance nullcay function 1 2 3 4 5 1 2 3 4 5 5 0 – 5 5 0 – 5 0 1 2 3 4 0 null1.0 null1.0 null null null 1 null0.null null0.null 0.0 0.0 0.null 0.null 1.0 1.0

175Cityscape Modeling Crinullnal nullstance nullcay nullr the values null null g nullnullnull the model of nullssmo cannot represent an offense distribution, because the tails are too large to allow for the required normalinullation ∫ a b 0 ∞ r r + ∆r∞ –∞ ∞ –∞ ∫ a b 0 ∞ r r + ∆r∞ –∞ nullnullnull nullnullnull , null nullnullnull null nullnull nullnullnull nullnull nullnullnull null null regardless of the choice of the parameters b and null because we always have ∫ a b 0 ∞ r r + ∆r∞ –∞ ∞ –∞ ∫ a b 0 ∞ r r + ∆r∞ –∞ nullnull nullnull , null nullnull nullnull nullnull null nullnull null ∞. One solution to this issue would be to truncate the values of the offense distribution outside some finite but large region. One concern with this approach is that it necessarily implies that the values of the other parameters, especially the parameter null would vary significantly with the sinull of the region used for the truncation. Canter and nullmmond nullnullnull enullmine four different forms for distance decay using null nullgarithmic A + B ln r if A + B ln r ≥ 0 0 if A + B ln r < 0 D(r) = { Ar 2 + Br + C if Ar 2 + Br + c ≥ 0 0 if Ar 2 + Br + c < 0 D(r) = { A + Br if A + Br ≥ 0 0 if A + Br < 0. D(r) = { null nullgative enullonential nullrnullnullnull nullBr null nulladratic

A + B ln r if A + B ln r ≥ 0 0 if A + B ln r < 0 D(r) = { Ar 2 + Br + C if Ar 2 + Br + c ≥ 0 0 if Ar 2 + Br + c < 0 D(r) = { A + Br if A + Br ≥ 0 0 if A + Br < 0. D(r) = { null nullnear A + B ln r if A + B ln r ≥ 0 0 if A + B ln r < 0 D(r) = { Ar 2 + Br + C if Ar 2 + Br + c ≥ 0 0 if Ar 2 + Br + c < 0 D(r) = { A + Br if A + Br ≥ 0 0 if A + Br < 0. D(r) = { Two of these distributions have already been analynulld, so we provide the corresponding graphs for nullst the logarithmic and the quadratic distributions using the parameters selected by Canter and nullmmond nullnullnull. null their wornull distances were measured in nulllometers, so this represents the horinullntal anulls in enullibits null and null. Exhibit 11 Loganullithmic nullistance nullecay of Cantenull annull Hammonnullnull null = null.nullnullnullnull, B = null 1.nullnull0null (a) Offense nullstnullnulltion (null nullstance nullcay function 0 20 40 60 80 1 2 3 4 5 50 0 – 50 50 0 – 50 0.00 0.05 0.10

176 Crime and Urban Form O’nullary Exhibit 12 nulluanullnullatic nullistance nullecay of Cantenull annull Hammonnullnull null = 0.000nullnull1, B = null 0.1null1nullnull, C = null.1null1nullnull (a) Offense nullstnullnulltion (null nullstance nullcay function 0 10 20 30 40 50 60 1 2 3 4 5 50 0 – 50 50 0 – 50 0.0 0.1 0.2 Estimating the Distance Decay Function nullowledge of the structure and form of the offense distribution of an individual offender would clearly be valuable, not only from a theoretical perspective but also for its potential value in developing improved geographic profiling algorithms. nullven the difficulty and complenullty of the full problem, most attention has been paid to the problem of simply determining the structure and form of the offendernull distance decay function. null the Crimenullat manual, nullvine nullnullnull chapter nullnullrecommends using aggregated crime data for a nullrisdiction to select the parameters that appear in the toolnull provided analytically defined distance decay functionsnullhe illustrates this process with data from nullltimore County. null particular, he recommends choosing the parameters in the decay curve so as to best fit the aggregate data. null also shows how Crimenullat can be used to generate an enullerimentally determined distance decay function that is an even better fit for the aggregate data. null comparison, nullssmo nullnullnull nullnull states that nullhe value of the distance enullonent null null nullnullnull was selected from a gravity model formulation developed to describe interprovincial migration of criminal fugitives nullossmo, nullnullnullnullnull.nullThe value of the buffer nullne parameter b is selected heu- ristically and is dependent on the mean nearest neighbor distance, while the multiplicative scaling constant null is primarily used as a computational aide. null other applications of this model, the values of these parameters are selected to optiminull the effectiveness of the geoprofile. nulldeed, null Comber et al. nullnullnull apply this model to construct geographic profiles in animal foraging. null their analysis, they hold the constants null and B finulld, with the latter dependent on the trip distance of the species under study. Then a collection of values for

177Cityscape Modeling Crinullnal nullstance nullcay the enullonents null and g were selectednullfor each enullonent pair, the effectiveness of the geoprofile was evaluated to determine the best choice for the enullonents. nulline, nullssmo, and null Comber nullnullnull toonulla similar approach. This technique of choosing distance decay parameters so as to optiminulle the effectiveness of the geo - profile was used earlier by Canter et al. nullnullnull. They enullmined a collection of negative enullonential models for distance decay and then considered variants that contained buffer nullones andnullor plateausnull in all, they evaluated nullnulldifferent functions for their effectiveness as geographic profiling tools. Canter and nullmmond nullnullnull toonulla hybrid approachnullthey selected four general models for of - fender distance decaynullogarithmic, negative enullonential, quadratic, and linear. nullr each model, they obtained the parameters that describe the model by fitting the decay curve to a collection of aggregate data. nullch function generated a slightly different geographic profiling algorithmnullthey compared these algorithms for effectiveness. nullthough all these approaches to the geographic profiling problem select a distance decay function, their focus naturally is on determining the effectiveness of geographic profiling algorithms, rather than on the problem of estimating the offense distribution or the distance decay function of an individual offender. The primary issue in any approach to determining the distance decay behavior of an individual of- fender is one of data. nullthough researchers have access to data sets from a number of nullrisdictions covering a wide range of crime types, the data available about any one offender are necessarily limited. null general, the series sinull for an individual offender is small, and although there are series in which the number of elements is large, it is possible that offenders who successfully complete a large series are special in some way. null a consequence, the individual series contain insufficient amounts of data to reliably estimate much more than some elementary parameters of the seriesnullfor enullmple, the mean and the standard deviation of the distance from the offendernull anchor point to the crime sites. null all offenders behaved in the same fashion, then it would be a simple matter to aggregate data collected across a suitably large set of offenders to obtain a reliable estimate of their distance decay patterns. The literature has well established the enullstence of significant variation in offense patterns, however, depending on characteristics of the offender, the criminal event, and the local geography. nulln nullppen and de nullinuller nullnullnull noted that it may not be appropriate to try to draw inference about the distance decay patterns of an individual offender based on the distance decay behavior observed when aggregated across offenders. nullthough nullngert, Piquero, and nullnes nullnullnull disagreed with many of the conclusions in that wornull they unequivocally stated that nulle do not dissent from nulln nullppen and De nullinullernull assertion that researchers cannot and should not manull inferences about individual behavior with data collected at the aggregate level.null nullcently, nullith, nullnd, and Townsley nullnullnull analynulld residential burglary in nullrthhamptonshire, nullgland, from nullnull to nullnull. They enullmined null offenders who committed a series of at least null crimesnulltogether, they committed nullnullburglaries. They compared the aggregate distance decay curve for all offenders with the distance decay curves found by aggregating only offenders who

178 Crime and Urban Form O’nullary were alinull in age and found significant qualitative differences among these graphs, providing direct evidence that offenders do not in general possess the same distance decay patterns. null fact, they found that roughly two-thirds of the variation in offense distances can be ascribed to variations among offenders, rather than to variation in an individual offender. Townsley and nulldebottom nullnullnull then enullmined a larger data set of residential and nonresidential burglaries involving more than nullnullnulloffenders and null,nullnulloffenses. They found that nearly one- half of the variation in offense distances could be enulllained by variation among offenders. nullven these facts, is it possible to estimate the quantitative behavior of an individual from the available aggregate datanull Coefficient of Variation The nenull step is to analynull a data set provided by the nullltimore County Police Department. nullltimore County is a primarily suburban county in nullryland that borders nullltimore Citynullit forms a collar around the west, north, and east boundaries of the city. The nullnull Census nullreaunull nullnull estimate for the population of nullltimore County is nullnullnullnull The portion of the county near the border with nullltimore City and inside or near the nullltimore nullltway, or nullterstate nullnullnullnullnullnull is primarily urban and suburban, with much of the remainder of the county being rural. The county contains no other municipal governmentsnullhowever, a number of communities have well-defined names, including Towson, nulllethorpe, Dundalnull Owings nulllls, Cocnullysville, nullldwin, and nullnnull - ton. nullmap of nullltimore County is provided in enullibit null. Our data set for analysis consists of solved residential burglaries committed in nullltimore County between nullnull and nullnull. nullr each incident, we identified the location of the offense and the loca - tion of the home of the offendernullwe also have basic demographic information about the offender, including the age, senull race, and date of birth of the offender. The data set contains nullnullnulloffender- offense pairs, with nullnullnullindividual offenders committing nullnullnullseparate burglaries. The data set contains nullnullseries of four or more burglaries committed by the same offender. null identified individual offenders by matching the provided demographic information and home location. null is possible that two or more different individuals may have the same demographic information and the same home addressnullit is also possible that a single individual offender could possess different home addresses at different times. The data set contains no instances, however, in which the date of birth matched and the home address did not, suggesting that these concerns are linullly to have little practical effect. The home address recorded in the data set may not be the actual anchor point for the offender at the time of the offenses. nullseries is identified solely as a collection of burglaries committed by the same offendernullas a consequence, we do not account for confounding factors such as the presence of multiple offenders. Of the nullnullidentified series, the mean length of a series is nullnullcrimes, with a median of nulland a manullmum of null. nullhistogram of the number of series as a function of the series length is provided in enullibit null.

179Cityscape Modeling Crinullnal nullstance nullcay Exhibit 13 Exhibit 14 nullnullnullof nullltimonulle County nullltimonulle County Resinullntial nullnullglanulles, 1nullnullnull0null nullmnullnullof Senulles null a nullnction of the Length of the Senulles 100 20 30 40 50 Series length 50 100 150 Number of series

180 Crime and Urban Form O’nullary nullppose that the distance decay behavior of an individual offender is governed by a negative enullonential distribution in the usual form A(r) = D(r| ) µ( ) d = µ( ) d A(r) = p 1 D(r| 1 ) + p 2 D(r| 2 ) = e –r/ 1 + e –r/ 2 D(r| ) = e –r/ 1 p 1 e –r/ 1 1 ∫ 0 ∞ ∫ 0 ∞ p 2 2 , but that the parameter β may be different for each offender. Then we do not enullect that the distribution of offense distances aggregated across all offenders satisfies a negative enullonential distribution. To illustrate this, suppose that offenders fall into two categories, with the fraction p null

with parameter β null and the fraction p null with parameter β null . Then it is clear to see that, sampling across offenders, we obtain the aggregate distribution A(r) = D(r| ) µ( ) d = µ( ) d A(r) = p 1 D(r| 1 ) + p 2 D(r| 2 ) = e –r/ 1 + e –r/ 2 D(r| ) = e –r/ 1 p 1 e –r/ 1 1 ∫ 0 ∞ ∫ 0 ∞ p 2 2 , which is not a negative enullonential. null general, if the parameter β is distributed across offenders according to a probability density nullnullβnull then the distribution of offense distances nullnullrnullaggregated across offenders would then satisfy A(r) = D(r| ) µ( ) d = µ( ) d A(r) = p 1 D(r| 1 ) + p 2 D(r| 2 ) = e –r/ 1 + e –r/ 2 D(r| ) = e –r/ 1 p 1 e –r/ 1 1 ∫ 0 ∞ ∫ 0 ∞ p 2 2 . nullthough each individual offense series is far too small to reliably estimate the offense distribution or even the distance decay function of an individual offender, we can estimate both the mean and the standard deviation of the distance from the home location to the offense site for each series. Doing so lets us estimate the coefficient of variation of each series, which is defined to be the ratio of the standard deviation to the mean of a distribution. null can also calculate the mean and standard deviation of the negative enullonential distributionnull both the mean and the standard deviation are β, and hence the coefficient of variation is identi- cally null The significance of this result is that the coefficient of variation does not depend on the parameter β. Thus, if all individual offenders select targets according to a negative enullonential, then we enullect that the sample coefficient of variation should be roughly null regardless of how the parameter β is distributed across offenders in the population. To test this hypothesis, we plotted the mean and the standard deviation of the distance from the crime site to the offendernull home in enullibit null. null individual offenders all have a negative enullonen - tial distance decay function, then we would enullect these points to lie close to the line with slope null nullamining the graph, it is clear that the typical ratio of standard deviation to mean is much less than unity. To investigate this ratio further, we plot a histogram of the coefficients of variation for the distance decay function of our serial offenders and obtain enullibit null, which confirms our observation that the coefficient of variation is less than unity for nearly all of our observations and suggests that the negative enullonential model for distance decay is not a good fit for the behavior of individual offenders. nulle can also calculate the coefficient of variation for the other commonly chosen models for offender distance decay. null many cases, the coefficient of variation is independent of all the parameters in the distribution, letting us repeat this analysis. Doing so, we find that the coefficient of variation is constant for the logarithmic model null.nullnull, the normal model with r nullnull null.nullnull, the linear model null.nullnull, and the nullyleigh model null.nullnull. These results are summarinulld in enullibit null.

181Cityscape Modeling Crinullnal nullstance nullcay 5 10 15 20 Mean 1 2 3 4 5 6 Standard deviation 0 Exhibit 15 nullan annullStannullnullnullnullnullation in nullles fonullnullnullResinullntial nullnullglanull Senulles in nullltimonulle County, 1nullnullnull0null 0.5 1.0 1.5 2.0 Coefficient of variation 10 20 30 40 50 60 70 Number of series 0 Exhibit 16 Histognullm of the Coefnullient of nullanullation fonullnullstance nullom Offense Site to Offennullnulls Home fonullnullnullResinullntial nullnullglanull Senulles in nullltimonulle County, 1nullnullnull0null nullen comparing these analytically computed values to our data, we find that none of these models appears to provide a compelling enulllanation of the observed offender behavior. nulldeed, the theoretical coefficients of variation all are at least nullnullnull while the mean of the coefficients of variation for the nullnullseries is nullnullnulland the median is nullnullnull

182 Crime and Urban Form O’nullary nullen we calculate the coefficient of variation for other distributions, we find that the result depends on one or more of the parameters in the distribution. nullr enullmple, for the lognormal distribution D(r|σ,µ) = exp [

] 1 rσ 2pi (ln r – µ) 2 2σ 2 e σ 2

– 1 , we find that the coefficient of variation is D(r|σ,µ) = exp [

] 1 rσ 2pi (ln r – µ) 2 2σ 2 e σ 2

– 1. nullcause we do not nullow the distribution of σ in the offender population, we can draw no conclusions about the appropriateness of the lognormal model. On one hand, we can construct a distribution of σ that will enable us to match the observed data. On the other hand, we can repeat this process with the other models, such as the normal nullith r

≠ null, the truncated enullonential, or the quadratic model. nullthough the enullression of the coefficient of variation for those models is algebraically unpleasant, we can still find distribu- tions of the parameters for their models, which also enable us to match their coefficient of variation to the observed data. Thus, we do not possess evidence either for or against any of these models. nullsearchers have wornulld to determine the distribution of the coefficient of variation from a nullown underlying distribution. nullor the case in which the underlying distribution is normal, nullcnullay nullnullnullnullnullnull appronullimated the distribution of the coefficient of variation by showing that an appropriate transform can be well appronullimated by a chi-squared distribution. nulluch later nullangel nullnullnullnullnullnull gave an improved appronullmation. nulllewicnulland nullers nullnullnull compared different numerical appronullmations and the enullct values of the percentage points for the sample coefficient of variation when the underlying distribution is normal using a variety of appronullmations, including that of nullnully nullnullnull and nullald nullnullnullnullnullnullnull see also nullendricnulls and nullobey nullnullnullnullnullnull, who approached the problem differently. nulloopmans, Owen, and nullsenblatt nullnullnull developed confidence intervals for the coefficient of variation when the underlying distribution is normal or lognormal, while nullnhart nullnullnull developed appronullmate limits for the coefficient of variation when the underlying distribution is gamma. Exhibit 17 Name Distribnullion Mean Standard Deviation nullenullcient onull Variation Coefnullient of nullanullation of Selectenullnullstance nullcay Cunulles nullgatinull enullonential Loganullthmic nullnullal ( r=0) Lineanull Rayleigh e –r/β e – A/B A + B ln r r 2 2σ 2( – ) A + Br 1 1 2 3A 2 pi σ 2 2 3A 3 β β 2pi exp σ r 2 2σ 2( – ) r σ 2 exp 2 1 ≈ 0.707 7 12 7 e – A/B 4 1 pi – 2 pi σ pi – 2 2 ≈ 0.756 ≈ 0.882 4 – 1 pi ≈ 0.522 2 pi σ 2 (4 – pi) σ

183Cityscape Modeling Crinullnal nullstance nullcay nulln particular, although we have qualitative evidence to suggest that none of the models in enullhibit nullnull is sufficient to describe the distance decay behavior of offenders, we are unable to present a quantita- tive estimate of the potential significance of this statement. null are not the first researchers to apply the coefficient of variation to problems of distance decaynullsee nullith, nullnd, and Townsley nullnullnull, who calculated the coefficients of variation for the different aggregated samples provided in nulloonullnullnullnull. The Impact of Geography null far, we have considered the offense distribution and the corresponding distance decay function from the point of view of an individual offender. nullt is possible, however, even linullely, that the distance decay function varies with location. This idea is not newnullfor enullmple, nulldridge and nullnes nullnullnull discuss how the parameters in a distance decay model may vary across geography. null particular, they consider how the parameters in a gravity-based model might also vary across space, and so allow for and estimate quadratic variation in the parameters. To see how geography might impact distance decay behavior, let us start by enullmining the aggre - gate distance decay curve for the nullnullnulloffender-offense pairs for residential burglaries committed in nullltimore County. null particular, we plot a histogram of the number of residential burglary pairs against the distance the offender traveled to commit the crime, which renders enullibit null. To understand the role of geography, we repeat this enullrcise but, instead of enullmining all of the incidents in nullltimore County, we first select a geographic region and then select only the offenses that occurred in that region. nullr enullmple, let us first enullmine the aggregate distance decay curve for all of the studied residential burglaries that occurred within a null.null-mile radius of Towson, where, for definiteness, we set the center of Towson to have latitudenullongitude null.nullnullnullnullnull.nullnullnullnull see also the map in enullibit null. Doing so, we obtained enullibit null. Comparing enullibits null and null, we find no significant qualitative difference between the structures of histograms. nulle can repeat the process and enullamine the histogram of the aggregate distance decay curves for crimes committed within nullnullmiles of Dundalnullnullnullibit nullnulland within nullnullmiles of nulllethorpe nullnullibit nullnull null with Towson, these towns are urbannulluburban areas within the nullltimore nullltway nullnullnullnull nullt only are these areas similar in their urban structure, the aggregate distance decay functions for residential burglaries in these areas show many qualitative similarities. nulldeed, they all show that most crimes occur very close to the offendernull residence and that the number of crimes decreases as the distance between the offendernulls home and the crime site increases. nullenullt, we enullamine the aggregate distance decay curves for two areas farther removed from the urban core. Owings nulllls and Cocnullysville are each suburban communities of roughly null,nullnullpeople that lie on the manullor roads leading radially outward from nullaltimore City nullsee the map in enullhibit nullnullnull. nullcause of the smaller population density for these communities, we enulland the sinull of the local region and, in enullibits null and null, we plot the histogram of the number of residential burglaries versus the distance from the crime site to the offendernull home for crimes committed within nullmiles of Owings nulllls nullnullibit nullnulland Cocnullysville nullnullibit nullnull

184 Crime and Urban Form O’nullary 50 10 15 20 25 30 20 40 60 80 100 120 Distance in miles Count 0 10 20 30 40 500 1,000 1,500 2,000 Distance in miles Count Exhibit 1null Exhibit 1null Histognullm of Resinullntial nullnullglanulles in nullltimonulle County: nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullnullles of nullonullon (null.null11nullnullnullnull1nullnull: nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless

185Cityscape Modeling Crinullnal nullstance nullcay 0 10 20 30 50 100 150 Distance in miles Count 0 5 10 15 20 25 30 35 50 100 150 200 250 Distance in miles Count Exhibit 2null Exhibit 21 Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullnullles of nullnnulllnull (null.null0nullnullnull.null0nullnull: nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullnullles of Halethonulle (null.nullnullnullnullnullnull01null): nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless

186 Crime and Urban Form O’nullary 0 5 10 15 20 25 50 100 150 200 250 Distance in miles Count 100 20 30 40 10 20 30 40 50 60 70 Distance in miles Count Exhibit 22 Exhibit 23 Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullles of Onullngs nulllls (null.nullnullnullnullnullnull0nullnull: nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullles of Cocnullysnulllle (null.null1nullnullnull.nullnullnull): nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless

187Cityscape Modeling Crinullnal nullstance nullcay 50 10 15 20 25 2 4 6 8 10 12 Distance in miles Count null this point we begin to notice significant qualitative differences in the shape and structure of the aggregate distance decay histograms. The histogram for Owings nulllls remains similar to the histograms for Towson, Dundalnull and nulllethorpe in that all these histograms have a strong peanullat the origin, with most crimes committed close to the offendernull home address. One difference is that the Owings nulllls histogram does not quicnully decay as the distance increasesnullindeed, the offense count decays only very slowly, if at all, for crimes at distances between nulland null miles from the offendernull home address. These differences, however, pale in comparison with the differences observed in the histogram for burglaries committed near Cocnullysville. The largest number of crimes was committed by offenders whose home address is close to the crime sitenullhowever, a significant second peanulloccurs for crimes in which the distance to the offendernull home address is null to null miles. That graph also shows that a significant number of offenses were committed by offenders whose home address was more than null miles from the crime site. nullen if we disregard this latter peanullas being somehow unrepresenta - tive, we still see clear differences between the aggregate distance decay behavior in Cocnullysville versus the observed communities near the urban core. null continue by enullmining the rural communities of nullldwin and nullnnullon and, in enullibits null and null, we plot the histogram of the number of residential burglaries versus the distance between the crime site and the offendernull home address for offenses within nullmiles of nullldwin nullnullibit nullnulland nullnnullon nullnullibit nullnull null once again enullanded the radius around the community, now to nullmiles, to ensure that the region contained enough crime to create a reasonable histogram. Exhibit 24 Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullles of nulllnullin (null.nullnullnullnull null.null0nullnull: nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless

188 Crime and Urban Form O’nullary 50 10 15 20 25 2 4 6 8 10 12 Distance in miles Count Exhibit 25 Histognullm of Resinullntial nullnullglanulles Occunulling nullthin nullnullles of nullnnullon (null.nullnull11nullnullnullnullnull): nullstance null the nullstance nullom the Cnullme Site to the Offennullnulls Home nullnulless null both cases, we see significant differences in the qualitative shapes of the histogramnulleach is quite different from the other and from the histograms that we previously observed. The histogram for nullldwin does not show the peanullat the origin that we have repeatedly observednullin fact, that histo - gram shows that the number of offenses appears to decay as the distance from the offendernull home to the offense site decreases. On the other hand, it does appear to have a peanullin crime counts for distances of nullto null miles, possibly similar to the secondary peanullwe observed in Cocnullysville. null contrast, the histogram for nullnnullon does not appear to show any significant distance decay behavior, with a comparable number of crimes in each bin out to null miles. Clearly, these histograms show that the phenomenon of offender distance decay is closely tied to the underlying geography and that, when discussing offender distance decay, at least in the aggregate, it is necessary to account for the underlying urban form. nulllthough this evidence clearly indicates that aggregate distance decay curves are affected by geography, we have yet to see how. null one approach to this question, we can asnullhow features of the distance decay curve, such as the mean or median offense distance, vary across space. null recogninull that we cannot faithfully represent the full distance decay curve with nullst one parameter such as the mean or mediannullhowever, doing so enables us to begin the analysis. nullhibit null plots the median offense distance across nullltimore County. nullr each location, we con - structed the distance decay distribution of all offender-offense pairs for crimes committed within nullnullmiles of that locationnullwe then calculated the median distance for those crimes, which we then plotted along the vertical anulls. nullhibit null does the same thing as enullibit null but plots the mean

Unknown

189Cityscape Modeling Crinullnal nullstance nullcay – 76.8 – 76.6 – 76.4 Longitude 39.4 39.6 Latitude 0 10 20 30 Mean distance – 76.8 – 76.6 – 76.4 Longitude 39.4 39.6 Latitude 0 10 20 30 Median distance Exhibit 26 Exhibit 27 nullnullan nullstance in nullles nulltnullen the Offense Site annullthe Offennullnulls Home nullnulless fonullOffenses Committenullnullthin nullnullnullles of nullat Location nullan nullstance in nullles nulltnullen the Offense Site annullthe Offennullnulls Home nullnulless fonullOffenses Committenullnullthin nullnullnullles of nullat Location

190 Crime and Urban Form O’nullary – 76.8 – 76.6 – 76.4 Longitude 39.2 39.4 39.6 Latitude 0 2,000 4,000 Population rather than the median. These graphs need to be carefully interpreted near their boundaries. nulll- though these graphs show the median or mean distance for crimes within null.null miles of that location, they count only those crimes that occurred within nullltimore County. nullgions within nullnullmiles of a border with another county do not account for crimes that occurred outside nullltimore Countynull even when the point itself is outside nullltimore County. One thing that is immediately apparent from these graphs is that local geography appears to play a significant role, which is in line with what we observed in the histogram analysis. nulldeed, both the mean and the outlier resistant median distance between the offense site and the offendernull home vary dramatically across the area, with the urbannulluburban core showing travel distances of a few miles or less and the northern, rural portion of the county showing mean and median distances of null miles or more. One natural potential enullplanatory variable for these geographic effects is the local population density. nullhibit null graphs the population density of nullltimore County, using data from the nullnull Census. nullrom this enullhibit, it is clear to see that the population of the county is concentrated in a ring around nullltimore City, especially on the western, eastern, and northeastern sides of the city. null can also map the number of residential burglaries in our data set as a function of the offense site, which we have done in enullibit null. nullamining these enullibits, we see not unenullectedly that the number of residential burglaries tracnull closely with the local population. nulldeed, the data are consistent with the hypothesis that the residential burglary rate is proportional to the number of residences. null this hypothesis were the case, then we would enullect that the ratio of the number of residential burglaries divided by the number of residences would be roughly constant and would show no geographic pattern. null plot this ratio in enullibit null and verify that this occurs. Exhibit 2null nullnulllation nullnullof nullltimonulle County Sourcenullnullnull Census data

191Cityscape Modeling Crinullnal nullstance nullcay – 76.8 – 76.6 – 76.4 Longitude 39.4 39.6 Latitude 0 50 100 Offense count – 76.8 – 76.6 – 76.4 Longitude 39.2 39.4 39.6 Latitude 0.00 0.05 0.10 0.15 0.20 Ratio of number of offenses to population Exhibit 2null Exhibit 3null Count of the nullmnullnullof Resinullntial nullnullglanulles nullat Occunullin a Location Ratio of the nullmnullnullof Resinullntial nullnullglanulles to the Local nullnulllation

192 Crime and Urban Form O’nullary 0 10 20 30 40 Average offense distance 0 1,000 2,000 3,000 4,000 Local population 0 20 40 Count To better understand the effect of population on the aggregate distance decay histogram, let us add a third dimension to the graph and aggregate the crimes, not only by the distance from the crime site to the offendernull home but also by the population density of the region where the offense was committed. Doing so, we obtain enullibit null. nullen enullmining the sections of this histogram for finulld population, we see that the distance decay behavior seems to vary with the local populationnullregions with smaller populations appear to possess a smaller number of offenders willing to travel a long distance to offend than regions with larger populations. The histogram also clearly shows many more regions with a small local popula- tion than regions with a large local population. Thus, the longer tails in the distance decay sections noted in the histogram may be caused by the fact that they are drawing from a larger sample. To confirm this hypothesis, we can graph the distance decay curves for regions aggregated across comparable populations, but, to account for the different number of offenses in regions of different population, we graph only the relative fraction, which we have done in enullibit null. The rough agreement of these curves with one another suggests that, whatever relationship enullsts between local geography and distance decay patterns, the interaction does not appear to be medi- ated by the population density of the target location. Exhibit 31 Count of the nullmnullnullof Resinullntial nullnullglanulles, Sonullenullnull nullth nullan Offennullnullnullnullnulll nullstance annullnullnulllation of the nullnullglanull Location

193Cityscape Modeling Crinullnal nullstance nullcay 5 10 15 20 Distance (miles) 0.1 0.2 0.3 0.4 Proportion of offenses Conclusions null have seen the relationship between the two-dimensional offense distribution null and the one- dimensional distance decay function nullnullin the case of nullclidean distance, this relationship tanulls the form nullrnullnull2 pr nullrnull while, in the case of nullnhattan distance, we have nullmnullnullnullnullmnull These relationships enullst because of the fundamental two-dimensional nature of space. One con - sequence of these relationships is that we must tanull care when enullmining distance decay curves for the enullstence of a buffer nullne, because, in general, we should enullect that the distance decay function tends to nullro as the distance tends to nullro, independently of the presence or nonpresence of a buffer nullne. One approach we can use to study the behavior of individual distance decay functions is the coefficient of variation. null general, we cannot draw conclusions about the behavior of individual offenders from aggregate data. nullr some models of individual offender distance decay, however, the coefficient of variation is independent of all the model parametersnullthis occurs for a number of common models, including the negative enullonential distribution. null a consequence, if one of these Exhibit 32 nullen Relatinulle nullistance nullecay Cunullnulles fonull Resinullential nullunullglanully nulltenullEacnulldistance decay curnull is fornulld by aggregating across all tnull regions nulltnulla conullarable nullnulllationnull

194 Crime and Urban Form O’nullary models held for all offenders, then we should see roughly constant coefficients of variation across offenders. null enullloratory data analysis of nullltimore County residential burglaries shows significant variation in the coefficient of variation, however, suggesting that none of these models, including the negative enullponential model, are appropriate for the modeling of individual-level distance decay. nullnally, we enullmined the effect of geography on aggregate-level distance decay curves. null our study of nullltimore County residential burglaries, we found that the aggregate distance decay curves var - ied significantly depending on the locations of the crime sites that were aggregated. null particular, we found that the average distance between the crime site and the offendernull home address was dramatically larger in the rural portions as opposed to the more urban and suburban portions of the county. null performed an enullloratory analysis to see if this variation could be enulllained by dif - ferences in local population density, but the results of our analysis did not support this hypothesis. nullnullonulledgnullnts The author thannull the nullltimore County Police Department and Phil Canter for graciously agreeing to provide the data used in this study. This wornullis partially supported by the nulltional nullstitute of nullstice through grant nullnull-null-null-nullnull. nulltnullr nullinulle Onullnulleary is the director of the nullchool of nullmerging Technology and a professor in the Department of nullthematics at Towson nulliversity. References nullnnell, Craig, and nullevaun Corey. nullnull. nulleographic Profiling of Terrorist nulltacnull.nullnull Criminal nullonullingnullnullternational nulleory, Researcnull annullnullactice , edited by nullchard null nullcsis. Totowa, nullnull nullmana Pressnullnullnullnullnull nullennell, Craig, nullrent nullnoonull, and Paul Taylor. nullnullnullnull. nullnulleographic ProfilingnullThe Debate Continues,null Blue nullne nullganullne , Octobernullnullnullnull nullnnell, Craig, nullent nulloonull Paul Taylor, nullevaun Corey, and nulllia nullyton. nullnull. nulltnull null nullddle, Choose the nullddle,null Criminal nullstice annullBenullvior null nullnull nullnullnullnull nullnnell, Craig, Paul Taylor, and nullent nulloonull nullnull. nulllinical nullrsus nulltuarial nullographic Profiling nullrategiesnullnullnullview of the nullsearch,null nulllice nullactice annullResearcnull nullnullnull nullnullnullnull nullrnasco, nullm. nullnull. nullodeling nullcro-nullvel Crime nullcation Choicenullnullplication of the Discrete Choice nullamewornullto Crime at Places,null nullurnal onullnullantitative Criminology nullnullnullnullnullnull nullnull nullnull. nullo-Offending and the Choice of Target nulleas in nullrglary,null nullurnal onullnullvestigative nullycnulllogy annullnullnullnnullr nullonulling null nullnullnullnull nullrnasco, nullm, and nullchard nullocnull nullnull. nullhere Offenders Choose To nulltacnull nullDiscrete Choice nulldel of nullbberies in Chicago,null Criminology null nullnull nullnullnull.

195Cityscape Modeling Crinullnal nullstance nullcay nullrnasco, nullm, and Thessa nulloistra. nullnull. nullffects of nullsidential nullstory on Commercial nullbbersnull Crime nullcation Choices,null European nullurnal onullCriminology nullnullnull nullnullnullnull nullrnasco, nullm, and nulloor nullynull. nullnull. nullffects of nulltractiveness, Opportunity, and nullcessibility to nullrglars on nullsidential nullrglary nulltes of nullban nullighborhoods,null Criminology null nullnull nullnullnullnull. nullrnasco, nullm, and Paul nulliuwbeerta. nullnull. nullow Do nullsidential nullrglars nulllect Target nulleasnull nulle Britisnullnullurnal onullCriminology nullnullnullnullnullnull nullocnull nullchard, and nullm nullrnasco. nullnull. nullinding a nullrial nullrglarnull nullme nulling Distance Decay and Conditional Origin-Destination PatternsnullnullTest of nullpirical nullyes nullurney-to-Crime nulltimation in The nullgue,null nullurnal onullnullvestigative nullycnulllogy annullnullnullnnullr nullonulling null nullnullnullnull nullantingham, P. nullffrey, and nullorge Tita. nullnull. nullffender nullbility and Crime Pattern nullrmation nullom nullrst Principles.nullnull nulltinullial Crime nullalysis Systemsnullnulling Computer Simulations annull nullograpnullc nullnullrmation Systems , edited by nulln nullu and nullhn nullnull nullrshey, Pnull nullea Pressnullnullnullnullnull nullscema, nullssimo, nullnull nullossi, nullrco nulleda, and Tom nullfferson. nullnull. nullutbreanull nullurcenullnull nullw nullthematical nullproach To nullentify Their Possible nullcation,null nullysica null nullnull nullnullnullnullnull Canter, David, Toby Coffey, nulllcolm nullntley, and Christopher nullssen. nullnull. nullredicting nullrial nullllersnullnullme nullse nulling a Decision nullpport nullstem,null nullurnal onullnullantitative Criminology null nullnull nullnullnullnull Canter, David, and nullura nullmmond. nullnull. nullrioritinullng nullrglarsnullComparing the nullfectiveness of nullographical Profiling nullthods,null nulllice nullactice annullResearcnull nullnullnull nullnullnullnull nullnull nullnull. null Comparison of the nullficacy of Different Decay nullnctions in nullographical Profiling for a nullmple of nullnull nullrial nullllers,null nullurnal onullnullvestigative nullycnulllogy annullnullnullnnullr nullonulling null nullnullnull. nullberline, nullssica. nullnull. nulleographical Profiling Obscene Phone Callsnull Case nulludy,null nullurnal onull nullvestigative nullycnulllogy annullnullnullnnullr nullonulling null nullnullnull. nulldridge, null Douglas, and nullhn Paul nullnes. nullnull. nullarped nullacenullnullnullography of Distance Decay,null nullonullssional nullograpnullr null nullnull nullnullnullnull nullmpel, nullmes null, nullmberly null nullrnes, nullhn nullTague, and nullanna Pearson-nullrnullwitnull nullnull. nullistance-Decay in the Political nullography of nulliends-and-nullighbors nullting,null nulllitical nullograpnull nullnullnullnullnullnull nullodwill, nullasdair null, and nullurence null nullison. nullnull. nullequential nullgulation, nullatial Dispersion, and Consistency of Distance nulltacnullPatterns nullom nullme in nullrial nullrder, nullpe, and nullrglary,null nullycnulllogy, Crime nullnullw null nullnull nullnullnullnull nullld, nullders. nullnull. Statistical nulleory nulltnullEngineering nullplication . nullw nullrnull nullley. nullndricnull, nulllter null, and nullte null nullbey. nullnull. nullampling Distribution of the Coefficient of nullriation,null nulle nullnals onullnulltnullmatical Statistics null nullnull nullnullnullnull

196 Crime and Urban Form O’Leary Hesseling, Rene B.P. 1992. “Using Data on Offender Mobility in Ecological Research,null Journal of Quantitative Criminology nullnullnull 9null112. nulllenullcnull Boris, and Raynullnd H. Myers. 19null. “nullnullarisons of nullnullonullnulltions to the Percentage Points of the nullnullle nullefnullient of nullriation,null Technometrics 12 nullnull 1nullnullnull. nullnt, nullsh, Michael nullitner, and nulldrenullnullrtis. 2nullnull “Enulllnullting the Usefnullness of nullnctional Distance Measnulles nullen nulllibrating nullnullneynullonullrinull Distance Decay nullnctions,null Computers, Environment, and Urban Systems nullnull1nullnullnull. nullent, nulloshnulla, and Michael nulleitner. 2nullnullnull. “Efnullcacy of nulltandard Denulliational Ellinullses in the nullnullnulllication of nullinullnal nullogranullic Pronulling,null Journal of Investigative Psychology and Offender Pronulling null 1nullnullnull. nullcsis, Richard null, Ray null nullonulley, Harnully null nullnulln, and nulleg nulllen. 2null2. “nullnullrther nullsessnullnt of nullircle nulleorynullfor nullogranullic Psychological Pronulling,null The nullstralian and nullnull nullaland Journal of Criminology null nullnull nullnull2. nullonullans, nullnullert H., Donald B. Onulln, and nulldah Rosenblatt. 19null. “nullonnulldence nullnternullals for the nulloefnullcient of nullariation for the nullornullal and nullog nullornullal Distribnulltions,null nullometrinull null nullnullnull 2nullnull. nullnullnullnen, Manne, and Penulla nullnttila. 2nullnull “Predicting the Residential nullcation of a nullrial nullnullercial Robber,null nullrensic Science International 1null nullnull nullnull2. null nullnuller, nullenulln null, Barry nullcholls, D. nullnullRossnull, and Panull null Racey. 2nullnull “nullogranullic Pronulling and nullinulll nullraging,null Journal of Theoretical nullology 2nullnull2nullnullnull. nullnullne, nulld. 2null1null. “nullrinullestatnull null nullnullatial nulltatistics Progranull for the nullnalysis of nullrinulle nullncident nulloca null tions.null httnullnullnullnull.icnullr.nullich.ednullcrinullstat nullccessed nullril 2null1null nullnullnull. 2nullnullnull. “nullhe Enullalnullation of nulleogranullhic Pronullling nulloftnullarenull Resnullonse to nullinull Rossnullonulls nullritinullnulle of the nullnullMethodology.null httnullnullnullnullnedlenullne.conullnedlenullnenullhtnull nullccessed nullril 2null1null nullnhart, H. 19null. “nullnullonullnullte nullnnullence nullnullts for the nullefnullient of nullriation of nullnulla Distribnullions,null nullometrics 21 nullnull nullnullnullnull null, nullngnulli. 2nullnull “nulltting nullay nullth the nullolen nullhiclenullnull nullnullstigation of nullnullneynullfternull nullinull,null The Professional nullographer null nullnull null2nullnull. Malleson, nullcnull nulldrenullEnullns, and nullny nullnnullns. 2null9. “null nullentnullased Model of Bnullglary,null Environment and Planning null Planning and nullsign nullnull11nullnull12null Martin, R. nulldan, D. nullnullRossnull, and nullil Hanullerschlag. 2null9. “Hnullting Patterns and nullogranullic Pronulling of nullite nullarnullPredation,null Journal of nullology 2nullnull111null1null Mcnully, nullnull 19null. “Distribnullion of the nullefnullient of nullriation and the Enullended nullnullDistribnullion,null Journal of the nullyal Statistical Society 9null nullnull nullnullnullnull Mohler, nullorge O., and Martin B. nullort. 2null1. “nullogranullic Pronulling nullonullnullnetic Models of nullinullnal Behanullor.null httnullnullnullath.scnullednullnullnullhlernulleonullonullenullsed.nullf nullccessed nullrilnull

197Cityscape Modeling Criminal Distance Decay O’Leary, Mike. 2009. “The Mathematics of Geographic Profiling,null Journal of Investigative Psychology and Offender Profiling null 2nullnullnull. Panullsen, nullrek null 200null. “nullnnecting the nulltsnullnullsessing the nullcnullacy of Geographic Profiling nullftnullre,null Policing: An International Journal of Police Strategies & Management 29 nullnull nullnullnullnull nullnull 200null. “nullman nullrsnull Machinenullnullnullmparison of the nullcnullacy of Geographic Profiling Methonull,null Journal of Investigative Psychology and Offender Profiling null nullnull9. nulline, nullgel null, null nullm nullssmo, annullnullenulln null Le nullmnullr. 2009. “Geographic Profiling nullplienullto Testing Monullls of nullmnullenullee nullraging,null Journal of the Royal Society Interface null nullnullnull9. nullngert, George null, nullenullnull Pinullero, annullPeter null nullnes. null99. “nullstance nullcay nullenullminenullnull Criminology null nullnull nullnullnullnull nullch, Tom, annullMichael nullinullly. 200null A Methodology for Evaluating nullogranullic Profiling Softnullrenull Tech. nullp. nullmnullinulle, Mnull nullt nullsociates. nullssmo, null nullm. 200null. “null nullalnulltion of nullnulls nullalnulltion Methonulllogy for Geographic Profiling nullftnullre.null httpnullnullnull.tnulltate.enullnulliinulleographicnullrofilingnullnulllications.html nullccessenullnullril 20nullnull nullnull 200null. “Geographic nullnullistics or nullortcnulls to nullilnullenullnullsponse to nullook et al.,null Anulllied Cognitive Psychology nullnullnullnullnullnull nullnull 2000. nullogranullic Profiling . nullca nullton, nullnullnullnullPress. nullnull null9null Geographic ProfilingnullTarget Patterns of nullrial Mnullnullrers. nullpnulllishenullnullctoral nullssertation. nullmon nullaser nullinullrsity, nullrnanull, nullitish nulllnullnulla, nullnanull. httpnullnullnullmit.sfnullcanull itemnullnull0nullonullnullimple nullccessenullnullril 20nullnull nullnull nullnull. nullgitive Migration Patterns . nullpnulllishenullmaster’s thesis. nullmon nullaser nullinullrsity, nullnullrnanully, nullritish nullolnullmnullia, nullananulla. httpnullnullnullsnullmmit.sfnull.canullsystemnullfilesnulliritemsnullnullnullnullnullnullnullnullnullnull9nullnull9nullnull.pnullf nullaccessenull nullpril 20nullnullnull. nullssmo, nullm, annullnullot nulller. 200null “nullalysis nullrsnull Gnullssnullrk,null nullue nullne Maganullne , nullgnulltnull nullptemnullrnull2null 2null nullssmo, nullm, nullot nulller, annullnullrl nullsley. 200null “Geographic Profiling nullnulltenullonullnullnullnullnullThe nullg Pronullem nullth nullnnell, nullook, annullTaylor’s nullsearch,null nullue nullne Maganullne , nullnullmnullrnull2null29. nullnttila, Pekka, Manne Lanullkanen, annullnullgelo nullppalnull 200null “nullime nullhanullonulls annullnullstance Tranullllenullin nullmicinulls annullnullpes,null Journal of Investigative Psychology and Offender Profiling null nullnull. nullnttila, Pekka, Manne Lanullkanen, nullgelo nullppalnull annullnullrio nullsco. 200null “nullstance Tranullllenull annullOffence nullaracteristics in nullmicinull, nullpe, annullnullnullery nullainst nullsiness,null nullgal and Criminological Psychology nullnullnullnullnullnull nullrangi, nullnullanshnull annullnullnna nullnullgs. 200null “nullatial Patterns of nullnullan nullrial nullrglars nullth nulllenullnce to Geographical Profiling,null Journal of Investigative Psychology and Offender Profiling null nullnullnullnull

198 Crime and Urban Form O’nullary nullmith, nullilliam, nullohn null. nullond, and nullichael Townsley. nullnullnullnull. nullDetermining nullow nullourneys-to-Crime nullrynullnullasuring nullter- and nulltra-Offender Crime Trip Distributions.nullnull nulltting Crime in nulls nullace , edited by David nullisburd, nullm nullrnasco, and nullrben nullnull nulluinsma. nullw nullrnull nullringer-nullrlagnull nullnullnullnull nulloonull nullent. nullnull. nullndividual Differences in Distance Travelled by nullrial nullrglars,null nullurnal onull nullvestigative nullycnulllogy annullnullnullnnullr nullonulling null nullnullnull nulloonull nullent, David Canter, and Craig nullnnell. nullnull. nullredicting the nullme nullcation of nullrial OffendersnullnullPreliminary Comparison of the nullcuracy of nullman nulldges nullth a nullographic Profiling nullstem,null Benullvioral Sciences nulltnull nullw nullnullnullnullnullnull nulloonull nullent, Paul Taylor, and Craig nullnnell. nullnulla. nullan nullrsus nullchinenullThe Case of nullographic Profiling,null Blue nullne nullganullne , nullrilnullnull. nullnull nullnullb. nullhortcuts to nullographic Profiling nullccessnullnullnullply to nullssmo,null nullplienullCognitive nullycnulllogy nullnullnullnullnullnull nullnull nullnull. nulleographic ProfilingnullThe nullst, nullugal, and nullcurate nully,null nullplienullCognitive nullycnulllogy null nullnull nullnullnullnull Townsley, nullchael, and nullden nulldebottom. nullnull. nullll Offenders nulle nullual, nullt nullme nulle nullre nullual Than Othersnullnullriation in nullurneys to Crime nulltween Offenders,null Criminology null nullnull nullnullnullnull van nullppen, Peter null, and nulln null de nullinuller. nullnull. nullesisting Distance-DecaynullOn the nullgregation of nulldividual Crime-Trips,null Criminology null nullnull nullnullnullnull van nullppen, Peter null, and nullbert nullnull nullnsen. nullnull. nullhe nullad to nullbbery,null Britisnullnullurnal onull Criminology null nullnull nullnullnullnull nullngel, nullrnullnull nullnull. nullonfidence nulltervals for a nullrmal Coefficient of nullriation,null nulle nullerican Statistician null nullnull nullnullnull nullrren, nullnet, nullland nullboussin, nullbert null nullnulllwood, nulldrea Cummings, nulltalie nullbbs, and nullusan Trumbetta. nullnullnullnull. nullCrime nullcene and Distance Correlates of nullerial nullape,null nullournal onull nulluantitative Criminology null nullnull nullnullnull nullles, Paul, and nulldrew Costello. nullnull. nulle nulloanullnull Nownullrenull nulle Evinullnce nullr nullavelling Criminals. nullme Office nullsearch nulludy nullnull nullndon, nullited nullngdomnullPolicing and nullducing Crime nullit, nullme Office.

199Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Crime and Place: Rapidly Evolving Research Methods in the 21st Century nulltricia nullantingnullnull nullmon nullaser nulliversity The symposium in this issue of Cityscape focuses on understanding crime and the urban environ - ment, particularly how people live in and interact with the landscape nullbuildings, people, roads, and activitiesnullthat surrounds them. null advances understanding of crime within the urban landscape. nullnald null nulllson describes crime and the urban environment in the guest editornull introductionnull Crime changes with urban development patterns. Opportunities for criminal activity emerge, disappear, or move as geography changes across the urban landscape. Patterns emerge, dissipate, or persistnull. crimes are far more predictable by place of occurrence than by a particular offendernull. nullploration of crime and place is a rapidly evolving area of research in the nullst century. nullme of my early wornullin the nullnulls enullmined a topological structure of neighborhoods, identifying a way to measure the permeability of edges of the neighborhood, allowing crime committed by nonresi- dents to drift away from the usual location along manullr streets into roads toward the centers of neighborhoods. The computationally intensive simulation loop underpinning this research was possible only in the nullnulls because nullwas wornullng on a very large Cray-linull standalone computer. nullst people in the social sciences did not have access to such machines at that time. The move - ment toward personal computers that followed further limited the technical ability of researchers to tanull geographic ideas of a city and try finely woven analysis. nullsearchers were forced into data aggregation to census tracts or larger areas or were limited to the use of very small samples of cases primarily based on interviews. The times have changed. null are now in a period of rapid advancement in computing power and the development of new algorithms. Tools and techniques are developing very rapidly. null are moving, with appropriate security and privacy in place, into a new research world. Our research laboratory, the nullstitute for Canadian nullban nullsearch nulludies nullCnullnull at nullmon nullaser nulliversity, has, for enullmple, one data set of nullmillion records of people involved in some way with nullmillion criminal events that occurred during a multiple year period. nulldividuals, with their identity fully encrypted, included in the data set range from people who called the police to people who were victims and the people who were arrested in the criminal events. nullthough names are encrypted, records are linnulld to indicate co-offending, repeat offending, victiminulltion, and time and place.

200 Crime and Urban Form nullantingnullnull The data and computing power available to researchers in the nullnullnulllaboratory is still a fairly new phenomenon in the social sciencesnull it will not be in a few years nullsee articles in this issue for enullamples of the futurenull nullw algorithms are now developed routinely, manullng it possible to enulllore crime and place at increasing levels of detail and complenullty. nullectronic data storage is now routinely available in terabytes and will soon be available in petabytes. nullcreases in computing power now manull analysis of these large data sets increasingly tractable and agreeably fast. The enhanced analytical capabilities that are enabled in the new computer world have brought about the increase in availability of data, the use of innovative techniques, and the linnullng of research in criminology to policy questions. nullnnullng research with policy and practice leads to advances in decision support. This computational power is not yet available to researchers in general, but the direction of the field is moving toward improved access and analysis. Theory and method are now entwined. Theory drives analysisnull analysis guides theory. nulln particular, theory, research, and data wornulltogether to advance the nullowledge of the relationship between crime and place in ways that can be used in policymanullng and planning. The geographic way of thinnullng becomes increasingly important. The wnull and the wnullre of crime require linnullng people to time and space. nullnnullng people with place leads to a better understanding of how and why people move around and identifies the places that pull people in, push people away, or entrap them. This way of thinnullng shapes research but is also fundamental to strategic policy formation and to designing tactical operations. nullnnullng research, strategic policy, and tactical operations is particu - larly important for understanding persistent problemsnullespecially when researchers address what some call nullicnulld problems.nullnullcnulld problems do not have an easy solution. These problems require innovative ideas and research methodologies to provide clarity and assess the effects of programmatic efforts. Past research indicates that advancement can be made with careful thinnullng and with innovation. nullderstanding the pervasiveness of crime and disorder in specific places draws us into loonullng at human activity. Crime is the product of human activity. null need to loonullat daily living and the repetition of routine patterns. null nullow that crime and disorder can cluster heavily in certain small areas of a city and persist. null see other areas where crime can increase for short bursts but recede. null see still other areas where crime and disorder remain low. Crime patterns renullct move - ment and activity patterns of people. nullr a serial offender, the pattern would be unique to that individual and shaped by his or her nullowledge of and attraction to particular places within a city. nullr more common crimes, aggregate crime patterns renullct aggregate pulls, pushes, and areas of entrapment within a city. nullt our growing computer power means that, with spatial and temporal geographic information systems nullnullnulland data mining, we can begin to understand classes of pushes and pulls and build better place-based predictive models. Thinnullof water nullw as a visual analogy of how people move around in a city and how crime hot spots form. nullagine small streams of water nullwing across a nullt delta toward the ocean. During periods of heavier rain, new streams and pocnullts or deep pools of water form. The new streams or pools manull different patterns in different deltas, depending on the local topography, soil, shore structure, and actual water nullw. Criminal activitynulllways more complenullthan streams of waternull is innullenced by many factors, including a complenulltemplate of attractors as well as socioeconomic

201Cityscape Crinull and nullacenullRanulldly Enulllnullng ResearcnullMetnullds in tnull nullst Century and structural constraints. People, however, move about in a city. nullme concentrate around local areasnullothers move longer distances. They all develop routines, concentrating their activity along paths or routes and spend more time at some locations than others. nulln the aggregate, there are pushes and pulls in cities that, depending on the urban bacnullcloth or urban landscape, focus agglomerations. These pushes and pulls enullst for offenders. Offenders are people who spend most of their time not committing crime and often have their use of an urban area shaped by noncriminal activity. nullploring crime and place is a manullr research area that requires an understanding of people, their activities, their networnull of friends, and their reasons for being attracted to certain locations or being scared away from others. nullcording to nullnch nullnullnull, we form images of a city and those images persist. Those images help shape our activity and our activity helps shape those imagesnull local activities nulluch as wornull schooling, and entertainmentnull the character or milieu of locales, their vernacular architecture, residents, shops, parnull, and concentrations of people. The term usu - ally used for areas that are well nullown to a person is his or her nullwareness spacenull that is, places that are recogninulld by an individual and where an individual nullows how to get to and from. null the aggregate, cities have areas that are part of the awareness space of many individuals. These areas are usually the most active within cities. nulll activity is not necessarily positive, nor do all people have the same ability to move and choose their landscape. nullighborhoods have the potential of trapping people. The risnullof victiminulltion can be the consequence of a lifestyle, but it can also be drawn from the lifestyle of others in a local area. nullom a policy perspective, a city helps shape crime patterns and crime patterns help shape a city. null is a challenge to policymanullrs and researchers to better understand this continuing, dynamic relationship between people and place and how it innullences crime. The complenullty of the relationship between crime and the urban environment is very distinct for different types of crimes, different times of the day, different days of the weenull and different times in a year. Crime patterns are morphed by a citynull infrastructure, daily rhythm, and socioeconomic mosaic. nullme patterns are so strong that they should be evident to most people. nullall property crime nullshoplifting, theft of personal property, and theft from autosnull concentrates in manullor shopping areas or manullr transit hubs, while assaults concentrate in and near drinnullng establishment clusters. nullt, not all shopping areas have manullr concentrations of crime, nor do all bars. Crime may concen - trate in very small areas, such as one apartment building or a single bar, despite lower crime rates in all adnullcent properties. nullderstanding the dynamics of crime requires careful thought and the ability to move between different scales of resolution. nullom a research perspective, it is good to start with the most detailed data possible and aggregate based on the policy or research questions. Different results are appar- ent at different levels of aggregation. nullom a geographical perspective, when the research question draws one into nulloming further into a narrower time and space scale, the research will eventually reach a level where clustering fades and the pattern appears to be more random. nullen research nulloms out to broader scales, clustering will appear. The challenge in research is to find the appro - priate scale for the research question but to remain aware of the importance of understanding the finer scales. The appropriate focus nulleps moving bacnullto smaller units of aggregation. The study of crime and place focuses on understanding detailed patterns and, using these patterns, understand- ing what specific patterns help shape more general patterns.

202 Crime and Urban Form nullantingnullnull null the symposium of this issue of Cityscape, nullnald null nulllsonnull introduction sets the stage for a better understanding of what crime and place mean and how the scale of place relates to crime location theories and policy and practice. nulllsonnull introduction provides the reader guidance on how to navigate through the articles to build a conceptually broader framewornull for research on place. null their research on senulloffender legislation, Tony nullubesic, nulllan nullrray, and nullinullbeth nullcnull help the reader understand that laws are different in different states and enforced in different ways. nullt only does crime vary from place to place and temporally, but also what constitutes a crime and how offenders are monitored varies. nullthough the article is about senulloffender legislation, the issue is similar for many other types of high-attention public disorder and crime. nullw is a manullr dimen - sion of the analysis of crime patterns. Complenullty requires consideration of additional dimensions, depending on the issue under inves - tigation, and requires thinnullng at multiple scales of resolution. The article by Philip nullrris, nullremy nullnnis, nullran Obradovic, nullan nullenman, and nullidi nullunwald addresses a different questionnullone that requires thinnullng about variation againnulls they enulllore how neighborhoods and individual traits both matter in recidivism. nullnullspatial data mining is linnulld to better ways of understanding recidivism. null fits naturally within the idea of people acting within a landscape, and considers how places innullence people and people innullence landscapes, particularly in seeing how different types of recidivistic crimes cluster in different places. Crime patterns become clearer through their analysis. nulldvances in this burgeoning area of criminology require new and different ways to analynulle problems. null mentioned earlier in this article, innovation is occurring in theory, research, and methodology. nullel Caplan, whose article enulllores both theory and method, uses risnullterrain modeling and crimi - nogenic data layers in a data fusion nullnullto show how nulltheoretically grounded operationalinulltions of spatial innullence from many risnullfactors can be used as a control measure of environmental con - tenull when evaluating the spatial effect of place-based interventions on future crime events.nullCaplan asnull the theoretician, researcher, and policy or operations enullert to loonullat the landscape and to understand crime as it occurs within its environment. nullth this approach, it becomes very clear why crime is low in some areas and why it may appear episodically in others but not persist. This wornullis a manullr step forward for predictive policing. nullodericnull nullones and Derenull Paulsen focus on environmental contenullt in their enullamination of nullOPnull nullnull, a national program with the goal of greatly reducing the entrapment of people in highly distressed public housing. The authors report minulld results but note that other levels of information might help enulllain the variation they observe. null would be valuable to see research such as this repeated. nullace and place matter in all environmental bacnullloth research. nullchael nullns, nullgrid nulluld nulllen, and nullatherine Onullnullegan loonull at the effect of vouchers in enabling low-income people to become more mobile in seenulling out housing in safer places. The results are supportive of vouchers but raise some alternative enulllanations, as good research should. The research addresses the patterns at a census tract level. null will be interesting to see what happens when the level of aggregation moves to blocnull groups or blocnull nullonsidering adnullcent blocnullnull null would also be interesting to see whether people with vouchers tend to move to areas close to family and friends, a common intracity migration trend.

203Cityscape Crinull and nullacenullRanulldly Enulllnullng ResearcnullMetnullds in tnull nullst Century nullnull Onullearynull article challenges crime researchers to thinnullabout criminological and environmen - tal theory but also to thinnullabout the mathematics behind some of the algorithms used. The article also renullcts usage of larger data sets linnullng offenders to offenses and indicates some geographic research that will become possible when data sources improve. nullth each data source improve - ment, however, comes improved questions. nullat would have been changed if the analysis had included nullltimore city as well as the countynullnulluld the results have changed in an ecological sense if the base population or housing unit area were differentnullnullat about sparser road networnull in rural areasnullnullpoint-to-point distance could loonullshort but actually be much longer on the road networnullthat must be traveled. The article shows how theory, research, and methods blend when better data are available. null would be fruitful to return to these topics annually for several years to see how geographic thinnullng about place, and about place and crime, affect theory, research, and strategic policy development for improving quality of life. This issue of Cityscape shows how we as researchers can continue to develop new and better ways to address issues. null would be of value to watch the evolution of these research themes. nulltnullr Patricia nullantingham is the nullnull Professor of Computational Criminology at nullmon nullaser nulliversity in nullitish Columbia, Canada, and is director of the nullstitute for Canadian nullban nullsearch nulludies. References nullnch, nullvin. nullnull. nulle nullage onulltnull City . Cambridge, nullnullThe nullT Press.

204 Crime and Urban Form

205Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Advances in the Identification of Space As a Structuring Factor of Social Reality nullandro Ranulls nulliversidad nullcional de Colombia The way in which space determines the appearance, reproduction, or dissemination of violence and participates in the formation of offenders is becoming increasingly clear. nullnullwise, it has become much easier to determine the way in which property offenders, in particular, tend to use the specific characteristics of space to commit their acts. null addition, studies on public policies and interventions aimed at reducing crime that contemplate the possibility of redeeming spaces usually include additional evidence regarding the relevance of space in understanding the nature of the infringement of the law. Perhaps we are on the verge of achieving a theoretical synthesis capable of definitively establishing space as a fundamental structure of social reality, together with society, the economy, culture, politics, and demography. null can no longer ignore the spatialinulltion of social reality. null could even be said that we are getting close to acnullowledging the theoretical enullstence of both a general and a particular dimension of space. nullcording to the former, space would structure reality at two levelsnullfirst, by integrating the other structural properties of the population nullor enullmple, poverty, low levels of labor ratings, and racial segregation in the same locationsnulland, second, by developing predominant geographical formnullthat is, urban residential, urban commercial, or ruralnullif we tanull into account the predominant ones. nullch a structural integration based on predominant, nonspecific geographical forms can be achieved only by space, because it is able to create those moods often described by philosophers and writers. nullmmel nullnullnull nullnull for enullmple, states that nullthe mood of a landscape permeates all its separate components, frequently without it being attributable to any one of them. null a way that is difficult to specify, each component partanulls in it, but a mood prevails which is neither enullernal to these constituents, nor is it composed of them.null null is no coincidence that the different forms of interpersonal, group, and collective violence appear most frequently in the low-income, urban, residential areas of both developed and developing countries, and that most property offenders reside in these areas. nullr is it a coincidence that residents in these areas feature significantly homogeneous ratings with respect to the indicators for each social structure. nullrthermore, the activities of these individuals tend to limit themselves to

206 Crime and Urban Form Ranulls these same places, where the occupation density and the physical structure of the homes and pub- lic spaces force people into permanent and prolonged interactions that lead to the consolidation of behavior patterns, including those associated with crime. On the other hand, the particular dimension of space guides our analysis toward the physical, observable form in which space is organinulld as a placenulluse of the land, architecture, public space, furniture, lighting and its operating conditions, and so on, as well as toward the routine occupa- tions according to types of population, not nullst criminals, in these places. null fact, the construction of space as that place produced by environmental criminology through its different theories has allowed criminologists to escape the spiral of macrosocial or micropsycholog- ical structural causality in which they were frequently entangled. The current increased capacity to manull multiple new, enullremely accurate, and simultaneous empirical characterinulltions of criminal acts and criminals, guided or obtained to a great enullent thannull to spatial data analysis, is the basis for this breanullhrough that has contributed gradually to the rediscovery of space as a fundamental structure of social reality and a facilitator of modalities of crime. nulldoubtedly, the formulation of public policies and the actions of law enforcement agencies aimed at crime reduction have benefited from this new representation of space. null finding greater theo - retical and empirical support for the fact that law enforcement agenciesnullincreasingly place-based strategies do not produce merely precarious, superficial, or cosmetic changes but also affect the opportunities for the appearance of crime, public actions can also claim that the implementation of their strategies is structuring a new space that contributes to blocnullng the appearance, reproduc - tion, and dissemination of offenders and offenses. The current issue of Cityscape constitutes a pertinent and updated contribution to this process of theoretical and empirical development of a renewed understanding of the dimensions of space. The conceptual and methodological turn nullhat is, the nullsnullTerrain nulldelnullproposed by Caplan, in his effort to address how specific places are more closely associated with criminal activities, is very interesting. nulls thesis that the spatial innullence of criminogenic features enables occurrences and determines the seriousness and longevity of crime problems manulls it possible to go beyond the mere identification of hot spots, while opening a wornullagenda that will manull it possible to determine accurately how the combination of physical form and occupation patterns facilitates opportunities for the perpetration of certain crimes. The articles by Cahill, nullns et al., and nullnes and Paulsen, which analynull or evaluate the physical reconstruction of public, low-income, homogeneous residential complenulls nullhrough the nullPnullnull Programnull or the relocation of residents to other neighborhoods nullthrough the nullousing Choice nulloucher Programnull as well as their effects on the reduction of crime or of the enullosure of these households to crime, could be more optimistic in the light of the evidence they obtained. nullter all, no counter- evidence enullsts in the articles that shows the transformations of these places or the relocation of households have increased crime in the nullnes to which people were relocated or that crime has shifted significantly to neighboring areas. Overall, the articles seem to lose sight of the fact that the characteristics of these residential com- plenulls have structured the formation of criminals and created spaces favorable for the perpetration of crime in the contenull of a decades-long process. The shape that the general and particular

207Cityscape nullnullnces in tnull Identinullation of Snullce null a Structuring nullctor of Social Reality dimensions of space have acquired in these residential complenulls has fostered crime. Therefore, the breanullp of the homogeneity of social reality through the reconstruction of the physical form or the structure of social interactions requires the establishment of longer time periods to obtain valid results that are capable of determining whether the criminality that was slowly fostered is gradually deactivated. The study carried out by nullrris et al. provides support for this perspective. The author states that residential segregation acts in such a way as to reinforce concentrated disadvantage, which is related to criminal specialinulltion, which, in turn, is innullenced by peer contagion. Perhaps it would be useful to underline the fact that this type of peer contagion can occur only in the contenull of a space that intensifies interactions and consolidates patterns of behavior. nullnally, although Onulleary refers only brienull to the fact that offenders are more linullly to offend in an area where they previously resided than in other comparable areas, nullbelieve that, in fact, this point constitutes one of the most important aspects of the type of studies he carries out. Perhaps it will be possible to answer the question posed by variations between distance and direction in criminal distance decay models when the familiarity of the criminal with the target-place is made operational, which, in turn, might transform the target-anchor point relation into a logical enullres - sion of that familiarity. nulltnullr nullandro nullmos is an associate researcher at the nullstitute of nullban nulludies, nulliversidad nullcional de Colombia. Reference nullmmel, nullorg. nullnull nullnullnull. nullhe Philosophy of nullndscape,null nulleory, Culture nullSociety null nullnullnull nullnullnull

208 Crime and Urban Form

209Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape A South African Commentary on the Articles in the Cityscape Symposium on Crime and Urban Form nullter M.U. Scnullitnull nullinus nulluger nulltonnullnull nulloper Council for nullientific and nulldustrial nullsearch The relationship between crime and the physical environment, including urban form, is of particular relevance within the nulluth nullrican contenull. Two features of crime in this countrynull crime levels are enulleptionally high and crime affects different people in different parts of the city differentlynullave important implications for interventions in the built environment. nulltil nullnull, when the first democratic elections were held, the countrynulls apartheid policies directly innullenced planning policies and practices that, to a large degree, shaped cities and towns in nulluth nullrica. Cities were partitioned into various nullnes, based on race, with vacant land and other types of buffer nullnes dividing communities. nullformal settlements have since developed on much of the vacant land surrounding townships nullruger and nullndman, nullnullnull nullst poor people stay relatively far from their places of employment, requiring them to spend a considerable amount of time and money on traveling and manullng them vulnerable during these nullurneys. The spatial dislocation of the poor also has resulted in the enulllusion of many city residents from the amenities and economic opportunities that the city offers. Despite the progress made since nullnull, the form and structure of the apartheid city has not changed significantly. nullving conditions for the poor, in many ways, have not improved significantly either nullepartment of nullusing, nullnullnull nullstudy conducted in nullnull, which addressed the linnullbetween crime patterns and urban form, clearly showed the effects of the apartheid city and its contribution to disproportionate levels of safety in different communities. nulludies indicate that crime patterns and trends differ substantially, for enullmple, between city suburbs and traditionally poorer areas. The poor in nulluth nullrica are more enullosed to violent crime than are other groups in the country nullhaw and nulluw, nullnullnull The symposium article by nullns, nulllen, and Onullegan nullnullnull addresses the ability of poorer households to improve their quality of life by moving to other neighborhoods with the support

Unknown

210 Crime and Urban Form Scnullitnull nulluger, and Coonullr of government, in the form of vouchers. nullre bacnullround on the purpose of vouchers would have been useful, because it is not clear enullctly how the voucher program functions. The issue of mobility and quality of neighborhoods is also a matter of concern in nulluth nullrica, however, with questions being asnulld of the effectiveness of the governmentnulls housing subsidy scheme in creating better functioning settlements. Through the scheme, eligible poor households may receive a null-square-meter house at no or little cost. These houses are normally provided on the outsnullrts of cities, however, perpetuating the inefficient structure of the apartheid city and re-enforcing enullsting crime patterns. nullthough nullns, nulllen, and Onullegan nullnullnull aim to determine if the voucher program is effective in helping participants move to relatively safe neighborhoods, it may have been useful to touch on the role that potential employment opportunities could play. Despite crime being a very serious concern in nulluth nullrica, in many cases moving closer to potential nullb opportunities is more important than moving to a safer neighborhood. The two articles dealing with the nullPnullnull Program nullahill, nullnullnullnullnes and Paulsen, nullnullnull address issues similar to those raised about housing and upgrading pronullcts in nulluth nullrica, including the so-called nullban nullnewal Programmes and medium density minulld housing developments. One nully difference between the nulluth nullrican initiatives and nullPnullnull lies in the nature of the environments targeted for upgrading. null nulluth nullrica, these areas are often informal settlements where people live in temporary shacnull and have limited access to municipal services and other resources. nullsignificant potential enullsts, therefore, for improving the quality of life of the households benefiting from the initiatives. nullbstantial research still needs to be conducted on the displacement of crime and the diffusion of benefits from these pronullcts, but results will linullly vary, depending on the local conditions. null would be very interesting to apply in nulluth nullrica the risnullterrain modeling techniques that Caplan nullnullnull uses, because the criminogenic factors might well be very different in nulluth nullrica, due to the urban form in this country, as discussed earlier. nullr enullmple, the private minibus tanull industry carries a manullr part of the commuter trafficnullbecause they stop anywhere for passengers, bus stops might not be a criminogenic factor in many areas. nullgarding the study by nullrris et al. nullnullnull on nullvenile recidivism in Philadelphia, several studies nullltbenullr, nullnullnullPelser, nullnullnullnullmpson, nullnullnullwere conducted in nulluth nullrica on criminal activity. The urban fabric in nulluth nullrica is different from that in the nullited nullates because of the apartheid legacy and other factors, but it also contributes to nullvenile recidivism as indicated by nullrris et al. nullnullnull. nullrther, according to Pelser nullnullnull, a nully driver in nulluth nullrica is that the political struggles during the nullnulls and nullnulls made crime and violence acceptable. These struggles resulted in liberation, but they also led to uneducated youth and the general breanullown of the family and schools nullelser, nullnullnull This breanullown led to a culture of crime being nulloolnulland resulted in a high level of recidivism. Pelser nullnullnull further indicated that null percent of nullvenile delinquents were themselves victims of crime, which was not part of the study by nullrris et al. nullnullnull. null nulluth nullrica, the criminal can commute far to commit a crime, because some residential areas are far from places of opportunity and because it is common to commute far for wornull The study by nullhmitnullnullnullnull on the geographic profiling of serial rapists and murderers loonulld informally

211Cityscape nullSoutnullnullrican Conullentary on tnull nullticles in tnull Cityscape Synullosiunull at distance decay, which was a factor of the transportation modes available to the offender and the target bacnullloth. Onullearynulls nullnullnull modeling on distance decay may provide a basis for a similar distance decay and crime modeling in nulluth nullrica. nullllowing the lead of countries such as the nullited nullates, nulluth nullrica established a Child Protection nullgister in nullnull. During nullgust nullnull, however, Parliament revealed that, after being operational for null months, the register contained the name of only one person deemed unsuitable to wornullwith children nullnullnull nullnullnull This failure emphasinulls that, although a study such as that of nullubesic, nullrray, and nullcnullnullnullnull would be very useful in nulluth nullrica, it is not possible without an efficient nullstice system. nullnullonulledgnullnts The authors thannullnulln nullilson for inviting them to write this commentary. nulltnullrs Peter nullnull nullhmitnullis a principal researcher in the nullilt nullvironment nullit of the Council for nullientific and nulldustrial nullsearch in Pretoria, nulluth nullrica. Tinus nulluger is research group leader of nullstainable nullman nullttlements in the nullilt nullvironment nullit of the Council for nullientific and nulldustrial nullsearch in Pretoria, nulluth nullrica. nulltony null Cooper is a principal researcher in the nullilt nullvironment nullit of the Council for nullientific and nulldustrial nullsearch in Pretoria, nulluth nullrica. References nulltbenullr, nulltony. nullnull. nullCountry null nullar nullitnullnullselnull Soutnullnullricanulls Crisis onullCrimenull nullhannesburg, nulluth nullricanullnullnathan nullll. Cahill, nullagan. nullnull. nullsing the nulleighted Displacement nullotient To nullplore Crime Displacement nullom Public nullusing nulldevelopment nulltes,null Cityscape null nullnull nullnullnullnull Caplan, nullel null nullnull. nullpping the nullatial nullnullence of Crime CorrelatesnullnullComparison of Operationalinulltion nullhemes and nullplications for Crime nullalysis and Criminal nullstice Practice,null Cityscape null nullnull nullnullnull Department of nullusing, nulluth nullrica. nullnullnullnulleanullng nullw nulloundnullnullComprehensive Plan for the Development of nullstainable nullman nullttlements. nullpublished. nullubesic, Tony null, nullan T. nullrray, and nullinullbeth null nullcnull nullnull. nullenullOffenders, nullsidence nullstrictions, nullusing, and nullban nullrphologynullnullnullview and nullnthesis,null Cityscape null nullnull nullnull. nullrris, Philip null., nullremy nullnnis, nullran Obradovic, nullan null nullenman, and nullidi null nullunwald. nullnull. nullhe Coaction of nullighborhood and nulldividual nullfects on nullvenile nullcidivism,null Cityscape null nullnull nullnullnull

212 Crime and Urban Form Scnullitnull nulluger, and Coonullr nullnes, nulldericnullnull., and Derenullnull Paulsen. nullnull. nullOPnullnull nullsident Displacementnullnulling nullPnullnull Program nullals To nullaluate nullighborhood Outcomes,null Cityscape null nullnull nullnullnull. nulluger, Tinus, and nullrina nullndman. nullnullnullnullrime and the Physical nullvironment in nulluth nullricanull Contenullualising nullternational Crime Prevention nullperiences,null Built Environment null nullnull nullnullnull nullns, nullchael C., nullgrid nulluld nulllen, and nulltherine Onullegan. nullnull. nullo nullouchers nulllp nullw- nullcome nulluseholds nullve in nullfer nullighborhoodsnullnullidence on the nullusing Choice nulloucher Program,null Cityscape null nullnull nullnullnullnull Onulleary, nullnull. nullnull. nullodeling Criminal Distance Decay,null Cityscape null nullnull nullnullnullnull Pelser, nullic. nullnull. nullearning To null nullstnullnullouth Crime in nulluth nullrica.nullDiscussion paper for the nullnull nullouth Policy nullitiative, nullserve nullnnull Pretoria, null nully nullnull. nullaamfontein, nulluth nullricanull Centre for the nulludy of nulliolence and nullconciliation. nullhmitnull Peter. nullnull. nullograpnullc nullonullingnull CnullnullTechnical nullport Tnullnullnullnullnull Pretoria, nulluth nullricanullCouncil for nullientific and nulldustrial nullsearch. nullaw, nullrnull and nulltoinette nulluw. nullnull. Environmental nullsign nullr Sanullr Communitiesnullnulleventing Crime in Soutnullnullricanulls Cities annullnullownsnull nullnullnullnograph null. null. Pretoria, nulluth nullricanullnullstitute for nullcurity nulludies. nullmpson, nullaeme. nullnull. nullto the nullart of Darnulless. nullurneys of the nullagents in Crime, nulliolence and Death. nullsearch paper for the Cnullnull nullaamfontein, nulluth nullricanullCentre for the nulludy of nulliolence and nullconciliation. nulluth nullrican Press nullsociation nullnullnull. nullnull. nullnullnull Child nullnullOffender nullgister nullsts nullst One nullme,null nulletoria News, nullgust null.

213Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Visualizing Racial Segregation Differently: Exploring Geographic Patterns in Context Ronald E. Wilson nullnull Department of nullusing and nullban Development nullaphic Detail nullograpnullc nullnullrmation Systems organinull annullclarinull tnull patterns onullnullman activities on tnulle eartnullnulls surnullace annull tnulleir interaction witnull eacnull otnullernull nullnullS nullata, in tnulle nullorm onull maps, can nullicnully annullpowernulllly convey relationsnullps to policymanullrs annulltnull publicnullnullis nullpartment onull Cityscape inclunulls maps tnullt convey important nullusing or community nullvelopment policy issues or solutionsnullnull you nullve manull sucnulla map annullare willing to snullre it in a nullture issue onull Cityscape, please contact nullvinullenullnullsenullunullgov null nullsualinullng geographical patterns of racial segregation is often done by mapping a proportion of a single racial group. The single proportion method, however, does not provide a contenull for under - standing the social or economic conditions that interact with the pattern. This article is the second of two that enullmines segregation at the regional level. The previous article nullilson, nullnullnullshows how to map two racial groups simultaneously to provide a comparative contenull for integration and regional segregation. The purpose of the analysis in this article is to move the reader beyond enullmining segregation with a single percentage map of one racial group without some comparative contenull. nullt providing a comparison allows a reader to be misguided as to whether real problems enullst. This article recasts the analysis of segregation to the interaction of the economic contenull with geographic patterns of segregation. The data used in this analysis are from the nullnull nullreau of nullonomic nullalysis nullnullnullnullcal Personal nullcome nulltimates. null null nullcome nullequality nulldenullnullnullnullis derived using three sources of income, which are earned wages, income maintenance, and unemployment insurance. Combined with measures of segregation, these categories can reveal changes in regional segregation patterns null nullr the entire nullited nullates, null nullrisdictions were missing from the original nullnulldata, all of which were in the Commonwealth of nullirginia. nullssing data were the result of several small independent citiesnullewer than nullnullnullnull residentsnulleing combined with surrounding counties.

214 Wilson Graphic Detail within the contenull of income disparities. nullnullis multiplied with the nullcalinulld Diversity nulldenullnullDnull null

that was used in the analysis in the previous article to measure levels of segregation and integration within a county. Personal income estimates are used for measuring disparities in sources of aggregate income through proportional balances. Categorinulld income sources represent various aspects of wornullorce abilities, education, nullob opportunities, and wage levels within a county. nullet earnings represent income from wages that come from being directly employed, less contributions from other sources. nullcome maintenance represents supplemental government assistance toward living enullenses. nullemploy - ment insurance represents supplemental government assistance as compensation for a recent nullb loss. The latter two categories represent a general form of government assistance for individuals enulleriencing economic difficulties. Proportional measuring of all three categories situates popula - tions with respect to the prevailing economic conditions in a county. Proportions of income sources create a normalinulled contenullt of income sources to determine whether the proportions are balanced. nullt earnings will always be the dominant source of income in any county, because more adults are wornullng than not in any local economy. nullcreasing proportions of income from government assistance through income maintenance or unemployment insurance, however, is indicative of negative economic conditions. null such, income disparities represent the local and regional economy through what proportion of the population is receiving how much income from what source. null this analysis, Theilnull nulltropy nulldenull null is used again to create the nullnullbecause the mathematical properties are sensitive to disproportionality changes between two or more categories, as well as substantive meanings regarding proportionate sources of income. nullth net earnings always being the dominant source of population income, the proportion from government assistance will be small, and this indenullwill be able to detect the changes between proportions. nullsulting values have the same boundaries of nulland ln ( ) 1 pi as the nullnullfrom the previous article, allowing the two indenulls to be combined. nullthough the application of this formula is similar to the nullnullin its range of val - ues, the upper and lower limits have different qualitative meanings. nullnullvalues close to nullrepresent populations whose dominant source of incomes is from employment nullet earningsnulland who are receiving little in government assistance. nullnullvalues substantially above nullrepresent populations who receive a larger share of income from government assistance in income maintenance and unem- ployment insurance. nullch county will have an nullnullvalue that can be thematically mapped to reveal patterns of where the poorest and richest areas of people are regarding the balance of income sources that populations in a county receive. nullhibit nullshows the geographic patterns of nullnull Counties in white have nullnullvalues within one standard deviation greater or lesser than the mean. These counties enullhibit the enullpected balance between income from net earnings and government assistance. Counties shaded in light gray have significant proportions of their populations receiv- ing income from employment and receiving little income from government assistance. Counties null The nullnullwas created using the population estimates from the nullnull Census nullreau nullerican Community nullrvey nullnull nullyear estimates. null nullr the mathematical details of Theilnulls nulltropy nulldenull see the appendinull

215 nullisualinullng Racial Segregation nullfferentlynullExnulloring Geogranullic nulltterns in Context Cityscape shaded in darnullgray have significant proportions of their populations receiving income from gov - ernment assistance. null the map shows, rural areas in nullpalachia, the Deep nulluth, the nullrthwest, and nullorthern nullichigan often display the highest shares of income maintenance and unemployment benefits. nullhibit nullshows that the economic conditions renullcted in one countynull nullscore are usually shared with its neighboring counties. The thematic map in enullibit nullindicates the clustering of income inequality, but it does not depict the enullent of the geographic connection between and across those counties. Places that share similar geographies usually share similar histories in social, political, and economic conditions nullovotn , nullnullnull one of which can be income disparity. Places are not isolated from the condi - tions and changes occurring in surrounding geographies. Counties in pronullmity to each other form interactions between themselves and establish strong economic ties that form regions. nullteractions between counties in pronullmity to each other may enullcerbate any number of local problems that affect an entire region. null such, income inequality levels are not nullst a renullction of a single county. These interactions require an analysis that considers the spatial relationship of income inequality Exhibit 1 nullstnullnulltion of the nullcome nullenullality nullnullnullfonullthe Contiguous null States (enullal intenullal classinullation) nullcome nullenullalitnull 0.000null.0null (nullnullStnull null.) 0.0nullnull.1null (nullnullStnull null.) 0.1nullnull.nullnull(nullnull1 Stnull null.) 0.nullnull0.nullnull(nullnullStnull null.) 0.null0null.nullnull(nullnullStnull null.) 0.nullnull0.nullnull(nullnullStnull null.)

216 Wilson Graphic Detail with adnullcent counties. null To measure the strength of these spatial relationships, the nullrannull nullnulldenull is used to detect significant regional spatial relationships of income inequality and racial segrega- tion between counties. null The nullrannull nullvalue of nullnull nullith a p-value of nullnullnull null indicates the covariation patterns of nullnullval - ues between a county and its adnullcent counties show strong regional clustering of high and low nullnull values. null The data cloud in enullibit nulldepicts a strong linear pattern across quadrants nulland nullnull Those two quadrants represent counties that are clustered together with similar values on opposite ends of the nullnull Counties in quadrant nullare those that are surrounded by counties with similarly high null The interactions between counties represent the nullrst nullw of nullography. nullr a discussion of this law and its importance, see nullller nullnullnull. null nullighboring counties were identified using the eight nearest neighbors. nullarest neighbors were used instead of adnullcency to eliminate the effects of large and irregular shaped counties not capturing full regional relationships. null nullrannulls nullvalues closer to nullnullindicate clustering. nullalues closer to -nullnullindicate dispersion. null The strength of the nullrannulls nullvalue is contingent on two elements. nullrst, the steepness in the slope of the line between values in a county with the lagged values in neighboring counties. The steeper the slope, the stronger that spatial relationship is between a particular value in a county with its neighboring counties. nullecond is the p-value that measures how different the observed nullvalue and a set of randominulltion nullvalues are from the original data. P-values less than nullnull indicate the observed pattern is statistically different from the random distribution of null values, which usually forms a normal distribution. The steeper the slope and the higher the p-value, the stronger the spatial relationship is between counties and neighboring counties. Exhibit 2 Snulltial nulltoconullelation of the nullcome nullenullality nullnullnullfonullthe Contiguous null States (nullnullnnulls nullscattenullnullot) nullcome inenullalitnullin neinullborinnullconullties nullnullcor esnull nullcome inenullalitnullin conulltnullnullnullcoresnull Morannull nullnullnull63 0.0 null0 nullnull0 null0 nullnull0 nullnull0 nullnull0 nullnull0 0.0 null0 null0 null0

217 nullisualinullng Racial Segregation nullfferentlynullExnulloring Geogranullic nulltterns in Context Cityscape values. Of the counties in quadrant null null percent show statistically significant clustering of high nullnull values with adnullcent counties. Of quadrant nullcounties, null percent show statistically significant clus - tering of low nullnullvalues with adnullcent counties. This clustering supports enullibit nullin which counties with plus or minus nullstandard deviations from the mean are clustered together in different regions of the nullited nullates. These counties share similarly high levels of income inequality through a regional connection. nullgions in enullibit nullthat are two standard deviations or greater than the mean are grouped together in the most rural parts of the nullited nullates. nullral counties often have fewer economic opportunities to develop robust nullb marnullts. nullgions with counties two standard devia - tions or below are those in which the populations are receiving little in government assistance and most income is from net earnings. These counties are located either in the heartland, where a lot of farming activity occurs, or in manullr urban areas that have diversified and robust economies. nullamining income inequality alone, however, does not answer questions regarding any regional relationship with segregation. nullhibit nullshows an association with lacnullof economic opportunity in areas not nullown for robust economies, regardless of race. The question remains, does a relation - ship enullst between the counties with high levels of income disparities and racial segregationnullnull new variable is created to measure the interaction between income inequality and racial diversity nullnullteraction nullnullnullnullnull null nullnull nullwhere nullnulland nullnullare as defined previously. This interaction depicts a bivariate relationship that identifies counties with high and low proportions of the nullacnullpopula - tion compared with the nullite population. The indenullenullremes are substantively opposite of each other regarding the positive and negative connotations. null interpreting the interaction, recall that an nullnullvalue close to nullsignifies segregation, and a value closer to nullsignifies integration. null contrast, nullnullvalues closer to nullsignify net earnings as the dominant source of income in a countynull substantially higher values indicate that government supplementation of income manulls up a larger proportion of income sources in the county. nullgh values of the interaction indicate a county with a high proportion of the nullacnullto nullite population nullacially integratednullwith a significant portion of income from government assistance greater than earned income. nullw values indicate either a highly segregated county, a county with most income coming from net earnings rather than government assistance, or both. nullhibit nulldisplays the map of the interaction between the two indenulls. null contrast with enullibit null only one dominant regional pattern now enullsts. nullbelt of counties from east Tenulls to northeast nullrth Carolina forms the dominant regional pattern. nulllesser cluster of counties in California nullostly rural and in the interior, apart from nulls nullgeles Countynullalso stands out. The latter areas have a high proportion of the nullacnullpopulation but far less than areas east of the nullssissippi nullver. null with the nullnull the strength of this spatial relationship needs to be measured with the nullrannull null nulldenullto detect significant regional spatial relationships of this bivariate relationship for regional income inequality and racial segregation. nullhibit nulldisplays the degree to which the interaction between the two indenulls is spatially cor - related. The data cloud in enullibit nullhas changed considerably and the nullrannull nullvalue null.null with a p-value of nullnullnull is substantially closer to nullthan the nullnullvalue. The covariation of interaction values between a county and its adnullcent counties shows an even stronger pattern of regional clustering with the high and low nullnulland nullnullvalues than in enullibit null The tight clustering of most nullscores near nullis an artifact of indenullconstruction.

218 Wilson Graphic Detail The data cloud still shows a strong linear association across quadrants nulland nullnull but the patterns have changed from those in enullibit null nullrst, the slope is steeper. The spatial relationship between counties and neighboring counties has gotten stronger. nullcond, the patterns in quadrants nulland nullnullhave distinctly changed. null quadrant null the pattern is more dispersed, which signifies counties with high values are clustering in small pocnullts of high values. Of these counties, null percent show a statistically significant clustering of high values with adnullcent counties, which is a decrease of nullpercentage points from enullibit null These changes reveal a strong local connection between income inequality and racial segregation amidst a larger regional pattern. The geographic patterns from enullibit nullsupport these results because the highest proportions of income inequality and nullacnullto nullite populations are concentrated and are separated from each othernullforming a loosely coupled belt across the nulluth. The pattern in quadrant nullnullshows the opposite, with a more concentrated pattern, which signifies these counties have low values and are the dominant spatial pattern. Of these counties, null percent show a statistically significant clustering of low values with adnullcent counties, which is an increase of null percentage points from enullibit null These changes indicate that a substantial number of highly segregated counties are clustered together with populations whose income is primarily from net earnings. nullhibit nullreveals this clustering in a pattern across the southern states from nullnullnsas to nullrth Carolina. nullt only does a strong level of succinct regional clustering enullst, but the pattern forms one long belt of clusters in one part of the nullited nullates. Of Exhibit 3 nullstnullnulltion of the nulltenullction of nullcome nullenullality annullLocalinullnullnullnullnullity nullnullnulls fonullthe Contiguous null States (enullal intenullal classinullation) nullcome nullenullalitnulland Racial Diversitnull 0.000000null.0nullnullnull 0.0nullnull0null.1nullnullnull 0.1nullnullnull0.nullnullnull 0.nullnullnullnull.nullnullnull 0.nullnullnullnull.null1nullnull

219 nullisualinullng Racial Segregation nullfferentlynullExnulloring Geogranullic nulltterns in Context Cityscape the southern states from nullnullnsas and nulluisiana to nullrginia, null percent of the area has moderate to high levels of the population receiving higher levels of income from government assistance and also higher proportions of the nullacnullpopulation receiving such assistance. Compared with the nullited nullates, these same counties manull up nullpercent of the area. nullth this pattern concentrated into one part of the nullited nullates, nullpercent is a significant amount, because progress in the region is linullly weighed down by a collective lacnullof opportunity. To confirm the spatial relationship that regional segregation has with income inequality, statisti- cally significant values were removed from the scatter plot in enullibit nullto enullmine the change in overall spatial relationships across the nullited nullates. nullen statistically significant high values are removed from the scatter plot, the nullrannull nullnulldenulldrops from nullnull to nullnull. This substantial decrease demonstrates the level at which greater proportions of income inequality and nullacnullpopu - lations affect the analysis results. These counties face specific challenges in overcoming income inequality regarding segregation that is much different from the rest of the nullited nullates. nullen counties with statistically significant low values were removed from the scatter plot, the nullrannull null dropped to only nullnull. The slight decrease confirms that the dominant spatial pattern in the nullited nullates is of counties with low income inequality and low regional segregation and is more common than the opposite. nully challenges these counties have regarding income inequality are not associ - ated with regional segregation. Exhibit 4 Snulltial nulltoconullelation of the nulltenullction fonullCounties in the Contiguous null States (nullnullnnulls nullscattenullnullot) nullnulland nullnullinteraction in neinullborinnullconullties nullnullcor esnull nullnulland nullnullinteraction in conulltnullnullnullcoresnull Morannull nullnullnull7null 0.0 null0 nullnull0 null0 nullnull0 nullnull0 nullnull0 nullnull0 0.0 null0 null0 null0 III nullInconull Inenullality IndexnullnullI nullnullcalinulld nullnullrsity Indexnull

220 Wilson Graphic Detail nullhibit nullputs regional patterns of income inequality and segregation into a final contenull. Overlaid with the thematic map in enullibit nullare manullr nullnull highways. nullghways can be lifelines for regional economic development because they provide efficient transportation of goods and services. nullgh - ways facilitate industries to locate in otherwise inaccessible areas and bring nullbs and growth that wornulltoward building robust economies. nullthout highways, transportation times and costs can become enullessive, which leaves little incentive for economic opportunities to be created. null can be seen in enullibit null counties with the highest levels of income inequality and regional segregation are located in areas that are deep between these highways. This lacnullof access to manullr roadways is indicative of rural areas with little economic opportunity. The analysis in this article reveals regional patterns of greater income inequality associated with racial segregation. The patterns are more succinct than single-race thematic map patterns and can guide place-based thinnullng with respect to applying policy in a geographical manner. Thematic maps using a single race or ethnicity do show patterns of segregation but do little to identify where help is needed most because the breadth of the problem is enullggerated on the map at the enullense of depth. null the analysis presented here, patterns are much more succinct in identifying specific pocnullts of income inequality nullepthnullthat have been identified within a larger pattern of Exhibit 5 nullstnullnulltion of the Comnullnenullnullcome nullenullality annullLocalinullnullnullnullnullity nullnullnulls fonullthe Contiguous null States nullth an Onullnullay of nullnullnullHighnullys (enullal intenullal classinullation) nullcome nullenullalitnulland Racial Diversitnull 0.000000null.0nullnullnull 0.0nullnull0null.1nullnullnull 0.1nullnullnull0.nullnullnull 0.nullnullnullnull.nullnullnull 0.nullnullnullnull.null1nullnull

221 nullisualinullng Racial Segregation nullfferentlynullExnulloring Geogranullic nulltterns in Context Cityscape regional segregation. The most disadvantaged counties in rural areas of the nulluth, with respect to segregation and sources of income, are highlighted in this analysis. nullnumber of additional reasons for these patterns enullst, beyond the association of rural areas with income inequality and racial segregation. nullt, the point of this article and the previous article is to spur analyses of race or ethnic data within a contenull. Other data sources should be used to conduct more combinatorial- based inquiries. nullngle-race thematic maps show nothing regarding why any pattern enullsts and leave readers unsure of which factors might be associated with segregation. nullme counties in the regional belt across the nulluth do not have high income inequality but have high proportions of the nullacnullpopulation. nullny of those counties are urban and have different needs regarding economic policies and opportunities than rural areas. The focus on the southern belt of counties identified in this analysis does not mean other rural regions of the nullited nullates need less attention regarding economic development. nulllinull other rural parts of the nullited nullates, counties in the nulluth may need specific policies to specifically address regional segregation and enulland economic develop - ment opportunities simultaneously. nullthird article will loonullat business pattern data, with the results from this analysis providing a better understanding of the current economic opportunities in this region. Appendix Inequality Index (Theil's Entropy Index) Theilnull nulltropy nulldenullis formally defined as ( ) 1 J j = 1 Inequality j = pi j ln∑ pij nullnull where null is a nullrisdiction and pnull is the proportion of each income source nullet earnings, income maintenance, and unemployment insurancenull in nullurisdiction null. null The result is null, which is a continuous value that ranges between null nullif one income source is the only income sourcenull and ln ( ) 1 pi nullif all three income sources are equal in sharesnullthis value is null. The result is a nonlinear nullogarithmicnullcurve that represents change effects in which each unit increment or decrement along the scale has a different rate and magnitude of change. null nulllues substantially larger than nullindicate net earnings are the dominant source of income and are from employment. nulllues closer to nullindicate net earnings are less dominant as the primary source of income and income maintenance and unemployment insurance nullovernment assistancenullmanull up a larger proportion of sources of income. null nullr a review of the mechanics of this indenull see nullong nullnullnull. null nullr additional details about how this indenullfunctions, see nullilson nullnullnull.

222 Wilson Graphic Detail Exhibit null1 nullstnullnulltion of the nullcome nullenullality nullnullnullfonullthe Contiguous null States (enullal intenullal classinullation) 0.000 0.173 0.347 0.520 0.693 0.029 0.137 0.352 0.459 0.565 0.693 50 100 150 0 Exhibit null3 nullstnullnulltion of the nulltenullction nulltnullen the nullcome nullenullality annullLocalinullnullnullnullnullity nullnullnulls fonullthe Contiguous null States (enullal intenullal classinullation) 0.000102 0.090575 0.181048 0.271521 0.361995 0.072480 0.144859 0.217237 0.289616 0.361995 50 100 150 200 250 300 0 Frequency Distributions nullch figure number corresponds with the respective map number in the main body of the article.

223 nullisualinullng Racial Segregation nullfferentlynullExnulloring Geogranullic nulltterns in Context Cityscape nullnullonulledgnullnts The author thannull nullhui nullng from nulluisiana nullate nulliversity and nully nulle from nullnt nullate nulli - versity for providing valuable comments toward clearly describing the results of this analysis. The views enullressed in this paper are those of the author and do not necessarily represent the official positions or policies of the nullnull Department of nullusing and nullban Development. nulltnullr nullnald null nulllson is a social science analyst in the Office of Policy Development and nullsearch at the nullnull Department of nullusing and nullban Development. References nullller, nullnry null nullnull. nulloblernull nullrst nullw and nullatial nullalysis,null nullnals onulltnull nullsociation onullnulleri null can nullograpnullrs null nullnull nullnullnullnull nullvotn , nullsef. nullnull. nulln the nullasurement of nullgional nullequalitynullDoes nullatial Dimension of nullcome nullequality nulltternull nullnals onullRegional Science nullnullnullnullnullnull nulllson, nullnald null nullnull. nullisualinullng nullcial nullgregation Differentlynullnullloring Changing Patterns nullom the nullfect of nullderlying nullographic Distributions,null Cityscape null nullnull nullnullnullnull nullng, David nullnull nullnull. nullpatial Decomposition of nullgregation nulldicesnullnullnullamewornullToward nullasuring nullgregation at nullltiple nullvels,null nullograpnullcal nullalysis null nullnull nullnullnullnull

224 Wilson Graphic Detail

225Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape A Beginner’s Guide To Creating Small-Area Cross-Tabulations Hanullar nullrban nullward nulliversity Rnulln Gallagnullr nullrtheastern nulllinois nulliversity Gulrinullnullnullenulln nullrban nulliversity of Chicago nullsepnullnullrsnull nulliversity of nulllinois at Chicago Data Shop nullta Snullp, a nullpartment onull Cityscape, presents snullrt articles or notes on tnull uses onullnullta in nullusing annullurban researcnull nullrougnulltnulls nullpartment, nullnull intronullces reanullrs to new annulloverloonullnullnullta sources annullto improvenulltecnullinulles in using wellnull nullown nulltanullnulle empnullsis is on sources annullmetnullnull tnullt analysts can use in tnullir own wornull Researcnullrs onullen run into nullotty nullta problems involving nullta interpretation or manipulation tnullt must be solvenullbenullre a pronullct can proceenull but tnully selnullm get to nullcus in nulltail on tnull solutions to sucnullproblemsnullnull you nullve an inulla nullr an applienull nulltanullentric note onullno more tnulln 3,000 wornull, please sennulla onenullaragrapnullabstract to nullvinullanullannullnbroucnullnullunullgov nullr consinullrationnull nullstract nullis snullrt article intronullces two tecnullinulles onullgenerating crossnullabulations in small areas nullor enullmple, blocnullgroups or tractsnullnullr wnullcnullonly univariate nullstributions are availablenullnullese tecnullinulles renullire eitnullr a microsample or a crossnullabulation nullom a larger geograpnullc area nullor enullmple, a nullblic nulle nullcronullta nulleanull nulle tecnullinulle uses nullllnulllimbing algoritnulls, annulltnull otnullr is basenullon iteratenullproportional nulltingnull null tnulls article, we inullntinull tnull general cnullracteristics onullbotnulltecnullinullesnullnulle present annullevaluate an enullmple nullenerating crossnullabulations onullnullusenulllnull by nullusing value annullnumber onullcnulllnullen enrollenullin public scnullolnull brienull nullscuss enullensions onullbotnull tecnullinulles to syntnulltic population construction, annulltest tnull syntnulltic populations by comparing tnull estimatenullmicrosamples witnulltnull actual populationnull

226 nullrban, Gallagnullr, nullrban, and nullrsky Data Shop Introduction null common problem in small-area data analysis is the lacnull of cross-tabulations for minor geographic areas. nullor large areas, microdata are often available, from which one can construct cross-tabulations in a straightforward manner. nullr enullmple, using the Census Public nulle nullcrodata nullmple nullnullnull, one can construct cross-tabulations for all geographies at or above the level of Public nullse nullicrodata nulleas nullnullnullnull nullPnullnullhas at least nullnullnullnullresidents. nullr any smaller area, only select cross- tabulations are readily available from the census. Thus, for a census tract in the nullnull Census, nullerican nullct nullnder provided information on house value by household income, but these data were not reported for blocnullgroups. nullen at the tract level, nullerican nullct nullnder provided no cross-tabulation for household value by number of household children enrolled in public schools. nullecently, for a research pronullect nullnullurban, nullallagher, and Persnully, nullnullnullnullnull on in-nullind transfers, including those through public school systems, this value was one of several nully cross-tabulations needed at the blocnullgroup level. null interest in imputing such small-area tables is widespread and can arise in any number of pronullcts involving microgeography. The challenge is to build small-area tables in a reliable fashion. This article describes two relatively straightforward, and now quite accessible, techniques for generating synthetic cross-tabulations in small-area data analysis. The first technique, largely developed in nullreat nullritain and favored by geographers nullnullang and nulllliamson, nullnullnullnullan, nulloh, and nullnaroglou, nullnullnullnullas and nulllliamson, nullnullnullnullilliamson, nullirnullin, and nullees, nullnullnullnullnull is based on hill-climbing algorithms from computer science. null The second technique, which is better nullown in the nullited nullates, is iterated proportional fitting nullPnull. null nullnullhas been widely used by transportation analysts nullaggerly et al., nullnullnullnullcnullan, nullggerly, and nullnully, nullnullnull The common starting point for both techniques is information on small-area univariate distribu- tions or marginals. nullr enullmple, the census provides us with the distribution of households across categories of housing values within blocnullgroups. nullr each blocnullgroup, we also have the distribution of households by number of children in the public schools. nullat we do not have nullnd what we wantnullis the cross-tabulation of households by housing values annull number of children in the public schools. nullth nullnulland hill climbing are heuristic methods that start with a real cross- tabulation at a higher level geography and alter it in an effort to reproduce the nullown marginals for the lower level geography nullnullwornull directly on the higher level table with a number of sequential adnullstments aimed at bringing that table into conformity with the small-area marginals. null contrast, hill climbing begins with the raw microdata for the higher level geography and assigns the individual observations to each of the small areas, which compose a larger area. null manull these assignments to match the marginals available for each small area. null the nenull section, we brienull introduce the conceptual foundations of each of these techniques. null then manull some suggestions for introductory software tools that are easily available on the null The term nullill climbingnullis used broadly in computer science to cover a range of heuristics based on random search. nullven an obnullctive function, hill-climbing methods search randomly around an initial point in an attempt to manullminull that function locally nullhat is, to find a hilltopnull To avoid being trapped at a local manullmum, hill-climbing algorithms randomly restart their search at more distant points, nulleping tracnullof their global performance. nulle nullchalewicnulland nullgel nullnullnull and nullssell and nullrvig nullnullnull. null nullerated proportional fitting was first introduced by Deming and nullephan nullnullnull.

227 nullnullginner’s Guide nullo Creating Snullllnullrea Crossnullabulations Cityscape nullternet. nullthough we do not provide step-by-step instructions, we do present an enullended enull - ample, which applies both techniques to the same problem. The last section introduces enullensions of these two methods that can be used to create full-scale synthetic populations for small areas. Two Techniques Perhaps the easiest way to fill in a small-area cross-tabulation is simply to tanull a larger area cross- tabulation and scale it down to the sinull of the small area in question. nullternatively, if microdata are available for the larger area, a simple random assignment of household observations to the various component small areas, achieving those areasnulltotal populations, would be enullected to generate a similar result. The two techniques presented in this article offer substantial improvements on these naive approaches by incorporating iterative procedures that account for univariate marginals of the small areas. nullll climbing starts with a random assignment of microdata, while nullnullstarts with scaled-down cross-tabulations. Hill Climbing nullll climbing begins by randomly populating small areas with household observations tanulln from the larger area. One draws household observations from the larger area with replacement and as- signs each small area only the number of households it actually holds. null this point, the simulated univariate distributions will not generally match the real distributions. nullnull, one randomly swaps households from one small area to another so as to improve the match between the real and simulated marginals while holding the small-area populations unchanged. nullll climbing imple - ments pair-wise swaps only if the swaps improve the fit of the allocation. These swaps are repeated several times to improve the fit between the marginals gradually. To avoid becoming trapped in local optimums at the enullense of reaching a global optimum, nullang and nulllliamson nullnullnull, nullas and nulllliamson nullnullnull, and nulllliamson, nullrnulln, and nulles nullnullnull implemented a nullnullble annealing procedure that allows the swapping algorithm to accept some swaps that produce poorer performance. This procedure helps improve the overall fit by allowing the algorithm to search across local optimums to get closer to a global optimum. The goodness-of-fit for each allocation is continuously recorded, and a prespecified stopping procedure determines when the swapping will come to a halt. Iterated Proportional Fitting nullsecond technique for small-area table construction is iterated proportional fitting nullPnull. The basic idea of nullnullis straightforward. nullr a set of small areas that comprise a larger unit such as a Pnullnull seed each small-area table with a copy of the Pnullnulllevel table scaled to the small-area population. null this point, any cell in the large-area table nullst barely equals the sum of corresponding entries in the small-area seed tablesnullhowever, neither row marginals nor column marginals from the seed tables will add up to the actual marginals for the small areas. nullenullt, multiply each row in each small- area seed table by a unique constant so that the cells in that row sum to the nullown corresponding row marginal for the actual small area. nullter these operations, column sums in the adnullsted seed tables will generally not equal the corresponding true column marginals, nor will the cells in the

228 nullrban, Gallagnullr, nullrban, and nullrsky Data Shop adnullsted seed tables sum to the corresponding cells in the large-area table. The second adnullstment tanulls the new small-area seed tables and multiplies each column by a constant so that its elements add up to corresponding column marginal for the actual small-area data. nullnally, a third set of multiplicative adnullstments guarantees that entries in the small-area seed tables sum to the cor - responding entry of the actual large-area table. The nullnulltechnique consists of repeated iterations of these row, column, and stacnulladnullstments. The overall process is brought to a halt by specifying an appropriate stopping rule based on the abso- lute magnitude of cell adnullstments. nullnullhas a strong intuitive appeal. null is well nullown to converge nullienberg, nullnullnullnulleland and nullllbacnull nullnullnull null the general case, in which small-area cell entries may be generated from very different processes, the quality of nullnullestimates is not guaranteed. An Example To illustrate the two techniques for generating small-area cross-tabulations, we now turn to an enullample suggested by our own wornull. nullut, as opposed to a scenario where small area cross-tabulations are not nullown, we use geographies in this enullmple for which complete data are available. There - fore, for this case, the accuracy of each technique can be fully assessed and compared. Basic Data for Both Techniques null suggested previously, our pronullct involved estimating cross-tabulations of households with given housing values and specific numbers of children enrolled in public schools. nullthough we were interested in generating data for blocnullgroups and school districts using nullown cross- tabulations for Pnullnull, for this enullmple, we frame the problem in terms of generating tables for Pnullnull using nullown cross-tabulations for their super-Pnullnull null doing so, we can compare the results with nullown values for those Pnullnull. null The data are from the nullnull nulltegrated Public nulle nullcrodata nullries nullPnullnull data file null httpnullnullww. ipums.orgnullnull null for a suburban Chicago super-Pnullnullnullnullnullnullconsisting of four Pnullnull nullumbers nullnull, nullnull, nullnull, and nullnullnull The nullvariables of interest are housing values aggregated into null categories and number of children in public schools aggregated into nullcategories. The nullnulls provides these data at the household level. nullhibits null and null present the housing value and school children distributions nullhat is, marginalsnullfor each Pnullnull nullhibit nullpresents the relevant cross-tabulation for the super-Pnullnull null null Pnullnull are defined by the Census nullreaunulleach Pnullnullmust be contiguous and have at least nullnullnullnullpeople. Pnullnull do not cross state boundaries. nullper-Pnullnull have at least nullnullnullnullpeople and are made up of contiguous Pnullnull. nullnull Pnullnull, super-Pnullnull do not cross state lines. Pnullnulldata allow tabulations at both the Pnullnulland super-Pnullnulllevels. null nulliversity of nullnnesota, nullnnesota Population Center. nullnull. nullntegrated Public nulle nullcrodata nullriesnullnullersion nullnullnull null The enullibits presented here are constructed from unweighted micro-observations. They could easily be weighted for use in actual practice.

229 nullnullginner’s Guide nullo Creating Snullllnullrea Crossnullabulations Cityscape Pnullnull Nnullber onullnullildren null 1 2 3 4null null01 1,null1 nullnull nullnull null null null0null null1 1null 1null null null null0null 1,0null nullnull nullnull 1null null null0null nullnull null1 nullnull 1null null nullMnullnullnullblic Use Microdata nulleanull Exhibit 1b nullinullnullate nullnullic School Chilnullen nullanulllations null nullnull Exhibit 1a nullnulle Valnull nullnull Pnullnull 31null 31null 31null 31null nullinullnullate House nullalue nullanulllations null nullnull 0nullnullnullnull 1null null 1null 10 null,000nullnullnullnull 1null nullnull null null null,000nullnullnullnull 1null 1null null null null,000nullnullnullnull 1null 1null null null 100,000nullnull,nullnull null1 null0 1null nullnull 1null,000nullnull,nullnull nullnull nullnull nullnull nullnull 1null,000nullnull,nullnull nullnull 111 nullnull nullnull 1null,000nullnull,nullnull 1null null nullnull nullnull null0,000nullnull,nullnull 1null null nullnull nullnull null0,000nullnull,nullnull null 10 null1 1null null0,000nullnull,nullnull null 10 null 1null null0,000nullnull,nullnull 1null null null null null0,000null null null 1null null nullMnullnullnullblic Use Microdata nulleanull nullMnullnullnullblic Use Microdata nulleanull Exhibit 2 nullnulle Valnull nullnull Nnullber onullnullildren null 1 2 3 4null Sunullnullnullnullnull1null00 Cnullossnullanulllations 0nullnullnullnull 1null null 1null null null null,000nullnullnullnull nullnull null null null 10 null,000nullnullnullnull null0 null null 1null null null,000nullnullnullnull nullnull null null 1null null 100,000nullnull,nullnull nullnull 1null 1null null 1null 1null,000nullnull,nullnull nullnull 1null 1null null null 1null,000nullnull,nullnull nullnull 1null 1null null null 1null,000nullnull,nullnull nullnull 1null 110 null 10 null0,000nullnull,nullnull nullnull 1null 1null null 1null null0,000nullnull,nullnull nullnull null null null 1null null0,000nullnull,nullnull 1null null null null 11 null0,000nullnull,nullnull null null 1null null null null0,000null null 1null null 10 null

Unknown

230 nullrban, Gallagnullr, nullrban, and nullrsky Data Shop The Hill-Climbing Technique Paul nulllliamson nullnullnull designed a readily available application of hill climbing that is suitable for beginners. nulllliamson calls his technique combinatorial optiminulltion nullOnulland a description of his program can be downloaded from his website. null nullers can quicnully adapt the CO application to their particular needs. The CO program uses the household microdata for super-Pnullnullnullnullnulland all Pnullnullmarginals. nulltarting with Pnullnullnull nullnullnullnull, the program assigns household observations by randomly drawing nullwith replacementnulla subset of the nullnullnullhouseholds from the super-Pnullnullmicrosample nullnullibit null to match the total population in this Pnullnull nullter each Pnullnullhas been randomly populated, the CO program begins the swapping and simulated annealing procedures. The fit of the swapping procedures is continuously assessed using a goodness-of-fit function proposed by nullang and nulllliamson nullnullnull and nullas and nulllliamson nullnullnull. The final output is simply a list of households allocated to each Pnullnullnull. nullrom this list of households, it is relatively easy to construct any desired cross-tabulation, including the cross-tabulation of house value and number of children of interest here. The final estimate for Pnullnullnullnull is presented in enullibit null. nullhibit null contains the actual cross-tabulations for this Pnullnull nullross all four Pnullnull, the program does quite well. The mean absolute error per household suggests that reallocating nullpercent of the households in the super-Pnullnullwould allow an enullct match to all four actual Pnullnullcross-tabulations. Exhibit 3a nullnulle Valnull nullnull Nnullber onullnullildren null 1 2 3 4null nullnull null01 Real Cnullossnullanulllations 0nullnullnullnull 111 null null null null null,000nullnullnullnull 1null null null null null null,000nullnullnullnull null null 1null null null null,000nullnullnullnull 11null 1null null null null 100,000nullnull,nullnull nullnull null null 1null null 1null,000nullnull,nullnull nullnull null null 1null null 1null,000nullnull,nullnull 1null null null 11 null 1null,000nullnull,nullnull 11null null null 10 1 null0,000nullnull,nullnull null null null 1null null null0,000nullnull,nullnull null 1null 1null null null null0,000nullnull,nullnull null null 11 0 1 null0,000nullnull,nullnull null 0 null 1 0 null0,000null 1null null null 1 0 nullMnullnullnullblic Use Microdata nulleanull null null to httpnullnullcwww.liv.ac.unullnullilliamnullicrodatanullOnullnullnullnullnullCOnulloftware.html .

231 nullnullginner’s Guide nullo Creating Snullllnullrea Crossnullabulations Cityscape Exhibit 3b nullnulle Valnull nullnull Nnullber onullnullildren null 1 2 3 4null Hillnulllimnullng Cnullossnullanulllations fonullnullnull null01null 0nullnullnullnull 110 null null null null null,000nullnullnullnull 1null null null null null null,000nullnullnullnull null null null null 0 null,000nullnullnullnull 11null null 1null null 1 100,000nullnull,nullnull nullnull null null 1null null 1null,000nullnull,nullnull nullnull null null 1null null 1null,000nullnull,nullnull 1null null null 11 null 1null,000nullnull,nullnull 10null null null null null null0,000nullnull,nullnull null null null 11 null null0,000nullnull,nullnull null null 1null null null null0,000nullnull,nullnull null 1null 1null null null null0,000nullnull,nullnull null null null null 0 null0,000null 1null null null 1 0 nullMnullnullnullblic Use Microdata nulleanull nullnullalues are rounded to tnull nearest nullole nunullernull The IPF Technique null number of programs for doing nullPnull are downloadable from the nullnternet. null nulleveral of these programs are part of elaborate data processing systems and require considerable investment of energy to learn. nullr beginners interested in generating only simple cross-tabulations, we found the wornullof nullls Tomlinson and nulldie nullnsinger nulllasnull Department of nullbor and nullrnullorce Development, nullnullnull null programmed in nullthe simplest to custominull and apply. The basic inputs to an nullnullprogram are four simple tables. These include nullnullthe marginals nulln our case, the material of enullhibits nulla and nullbnull, nullnullnull the cross-tabulations for the larger area nullour enullhibit nullnull, and nullnulland nullnullan initial nulleed tablenullfor each of the small areas. The seed tables are copies of the larger area table nullenullhibit nullnull scaled to the populations of each small area. These tables are the starting points for the successive row and column adnullstments that characterinull this procedure. The nullPnull program generates cross-tabulations for each small area. nulln enullhibit nullc, we present the resulting table for Pnullnullnullnull. null with the hill-climbing technique described previously, the nullnullestimated marginals for the small-area tables match the actual values almost perfectly. null comparison of the cells of the nullPnull-estimated tables with the actual table cells demonstrates that nullPnull successfully appronullimated the cross-tabulations. The overall mean absolute error suggests that a reallocation of nullpercent of the households in the super-Pnullnullwould allow an enullct match to all four actual Pnullnullcross- tabulations. Thus, for this enullmple, the nullnulltechnique performed somewhat better than the hill- climbing technique described previously. null Two well-nullown software applications are the nullnull Department of Transportationnulls Tnullnullnullnullprogram available at httpnullnullmip.fhwa.dot.govnullommunitynullsernullroupsnullransims and nullinullna nullate nulliversitynulls Popnulln program available at httpnullnullrbanmodel.asu.edunullopgen.html . null nullr programs, documentation, and an introduction to the iterated proportional fitting literature, go to httpnullnullww. demog.bernullley.edunulleddiehnullatafitting.html .

232 nullrban, Gallagnullr, nullrban, and nullrsky Data Shop Discussion nullth hill climbing and nullnullhandle the enullmple problem quite well. null null should be clear that both table-generating techniques are now enullcutable without a manullr investment in training and programming. nullthough nullnullwornulld somewhat more effectively for our enullmple problem, the two techniques seem quite comparable in terms of results. null this level, nullnullis probably somewhat easier to implement. The hill-climbing technique has one manullr advantagenullThe immediate product of hill climbing is a full assignment of households to small areas. nullter that assignment is made, it can be used directly as a set of synthetic populations. null nullom those synthetic populations, it is relatively easy to estimate not nullst the initial cross-tabulations of interest but also virtually any cross-tabulations of household characteristics in the original data set. nullreover, the hill-climbing technique can be easily enullended to include any number of relevant marginal conditions in the initial assignment. null contrast, constructing a synthetic population using the nullnulltechnique requires a manullr second step in which household assignments are carried out using repeated random samples constrained null The results achieved here by these techniques are probably better than what can be enullected generally. nullr one thing, the Pnullnullmarginals we used are drawn from the same basic data set as the microsample for the super-Pnullnull nullen the marginals and sampled microdata are drawn from separate sources, a certain amount of sampling variation would be linullly. nullre importantly, the quality of the fit at the small-area level depends on the enullent to which higher level and lower level areas have similar table structures or correlations. null the small areas are quite different from each other and from their sum, both techniques are linullly to suffer. null nullsearchers are showing an increasing interest in studying small-area phenomena. nullr small areas such as blocnullgroups or census tracts, microdata samples are generally unavailable. nullnerating a synthetic population at this small-area level means formulating a collection of households appropriately selected from a larger geography, such as a Pnullnull The problem, of course, is to define what selection mechanisms are nullppropriate.null Exhibit 3c nullnulle Valnull nullnull Nnullber onullnullildren null 1 2 3 4null nullnullCnullossnullanulllations fonullnullnull null01null 0nullnullnullnull 10null null 10 null null null,000nullnullnullnull 1null null 1null null null null,000nullnullnullnull null null 1null null null null,000nullnullnullnull 11null null 1null null null 100,000nullnull,nullnull nullnull null null 11 null 1null,000nullnull,nullnull nullnull null null 1null null 1null,000nullnull,nullnull 1null null null 11 null 1null,000nullnull,nullnull 11null null null null null null0,000nullnull,nullnull null null null null null null0,000nullnull,nullnull null 1null 1null null null null0,000nullnull,nullnull null 11 10 null null null0,000nullnull,nullnull null null null 1 1 null0,000null 1null null null null 0 Inull nulliterated nullonullrtional nulltingnullnullMnullnullnullblic Use Microdata nulleanull nullnullalues are rounded to tnull nearest nullole nunullernull

233 nullnullginner’s Guide nullo Creating Snullllnullrea Crossnullabulations Cityscape by the estimated table cells nullecnullan, nullggerly, and nullnully, nullnullnull nullsystem of this type requires more sophisticated programming. null at least necessitates becoming familiar with one of the pacnull - ages nullor enullmple, Tnullnullnullnull null and Popnulln null described in footnote null available to the public. These pacnullges also involve considerably more sophisticated versions of the basic nullnullalgorithm, because enullending the dimensionality of the table to be estimated requires building estimates of lower level tables as well. nullnulland hill climbing are promising techniques for creating synthetic populations, but a number of issues remain unresolved. nullnerating synthetic populations from nullnullresults involves the synthetic reconstruction of microsamples based on stochastic approaches that are often subnullct to sampling error. This error is linullly to be more significant for small sample areas. nullen if the model estimates are unbiased, there is no guarantee that the variance will not be too large. The second problem with nullnullis that it uses a sequential procedure. nullme error is introduced in each stage as a result of random sampling, modeling assumptions, and data consistency nulluang and nullilliamson, nullnullnull nullmilar to the nullnullprocedure, the hill-climbing procedure is a stochastic process. nullriations in the sample seed values will alter the baseline household selections and estimated distributions. Two previous studies nulluang and nulllliamson, nullnullnullnullan, nulloh, and nullnaroglou, nullnullnullhave com - pared the performance of nullnulland hill-climbing methods for construction of synthetic populations. nulloth studies concluded that hill-climbing methods outperformed nullPnull. Our somewhat tentative recommendation, therefore, is to continue using nullnullfor simple table construction. null you are generating more complete synthetic populations, however, hill climbing is more intuitive and perhaps more accurate. nullnullonulledgnullnts The authors thannullthe nullnull Department of nullusing and nullban Developmentnull Office of Policy De - velopment and nullesearch for hosting a seminar where an earlier draft of this paper was first presented. nulltnullrs nullydar nullrban is an associate professor in the Department of nullonomics at nullward nulliversity. nullan nullllagher is an assistant professor in the Department of nullonomics at nullrtheastern nulllinois nulliversity. nulllrinullnulltenulln nullrban received a Ph.D. in computer science from the nulliversity of Chicago. nulloseph Persnully is a professor in the Department of nullconomics at the nullniversity of nullllinois at Chicago. null Tnullnsportation nullalysis and nullnulllation nullstem nullnullnullnullnull. nullnull Department of Transportation, nullderal nullghway nullministration websitenull httpnullnullmip.fhwa.dot.govnullommunitynullsernullroupsnullransims nullccessed nully nullnullnull nullnull Popnullen null.nullnull Population nullenerator, nullrinullona nulltate nullniversity. httpnullnullnullurbanmodel.asu.edunullpopgen.html nullaccessed nullay nullnullnullnullnull.

234 nullrban, Gallagnullr, nullrban, and nullrsky Data Shop References nullggerly, nullith, nullchard nullcnullan, nullchael nullnully, and Douglass nullberts. nullnull. nullnullnullnullnullnullnthetic Population nullstem nuller nullide.nullnullnullnullnullnullnullnull nulls nullamos, nullnullnulls nullamos nulltional nullboratory. nullcnullan, nullchard null, nullith null nullggerly, and nullchael D. nullnully. nullnull. nullreating nullnthetic nullseline Populations,null nullansportation Researcnullnull null nullnull nullnullnullnull Deming, null nullwards, and nulledericnullnull nullephan. nullnull. nulln a nullast nulluares nullnullstment of a nullmpled nullequency Table nullen the nullpected nullrginal Tables nulle nullown,null nullnals onull nulltnullmatical Statistics nullnullnullnullnullnull nullenberg, nullephen null nullnull. nulln nullerative Procedure for nulltimation in Contingency Tables,null nullnals onullnulltnullmatical Statistics nullnullnullnullnullnull nullang, nullngyi, and Paul nulllliamson. nullnull. nullComparison of nullnthetic nullconstruction and Combinatorial Optiminulltion nullproaches to the Creation of nullall-nullea nullcrodata. nullrnullng paper. nullverpool, nullited nullngdomnullnulliversity of nullverpool, Department of nullography. nulleland, C.T., and null nullllbacnull nullnull. nullontingency Tables nullith nullven nullrginals,null Biometrinull null nullnull nullnullnullnull nullrban, nullydar, nullan nullllagher, and nullseph Persnull. nullnull. nulltimating nullcal nullburban nulldistribution in Property-Tanullnullnded nullhool nullstems. nullrnullng paper. nullshington, DCnull nullward nulliversity. nullchalewicnull nulligniew, and David nullgel. nullnull. nullw null Solve nullnullnullnullrn nulluristics . nullw nullrnull nullringer. nullssell, nulluart null , and Peter nullrvig . nullnull. nulltinullial nulltelligencenullnullnullnullrn nullproacnull nulld ed. nullper nullddle nullver, nullnullPrentice nullll. nullyan, nullustin, nullanna nullaoh, and Pavlos nullanaroglou. nullnullnullnull. nullPopulation nullynthesisnull Comparing the nullnullr Techniques nulling a nullall, Complete Population of nullrms,null nullograpnullcal nullalysis null nullnull nullnullnullnull nulloas, David, and Paul nullilliamson. nullnullnullnull. nullnullvaluating nulloodness-of-nullit nulleasures for nullynthetic nullcrodata,null nullograpnullcal annullEnvironmental nullnullling nullnullnull nullnullnullnull nullnullnull. nullnullnullnull. nullnulln nullvaluation of the Combinatorial Optiminullation nullpproach to the Creation of nullnthetic nullcrodata,null nullternational nullurnal onullnullpulation nullograpnull nullnullnull nullnullnullnull nullilliamson, Paul. nullnullnullnull. CO nullnstruction nullanuel. nullornulling paper nullnullnullnullnullnull. nulliverpool, nullnited nullngdomnullnulliversity of nullverpool, Department of nullography, Population nullcrodata nullit.

235 nullnullginner’s Guide nullo Creating Snullllnullrea Crossnullabulations Cityscape nulllliamson, Paul, nullrnullnullrnulln, and Phil null nulles. nullnull. nullhe nulltimation of Population nullcrodata by nulling Data nullom nullall nullea nullatistics and nullmples of nullonymised nullcords,null Environment annull nullanning null null nullnull nullnullnullnull nullditional Reading nullng, David. nullnull. nullhe nullliability of nulling the nullerative Proportional nulltting Procedure,null nullonulls null sional nullograpnullr null nullnull nullnullnullnull

236 nullrban, Gallagnullr, nullrban, and nullrsky Data Shop

237Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape The Impact of Home Energy Retrofit Loan Insurance: A Pilot Program nullastair Mcnullrlane nullnull Department of nullusing and nullban Development nullpact nullregulatory impact analysis must accompany every economically signinullant nullnullral rule or regulationnull nullnulle nullnullnullce onull nullolicy nullevelopment annull Researcnull pernullorms tnullis analysis nullr all nullSnullnullpartment onullnullusing annullnullban nullvelopment rulesnullnull impact analysis is a nullrecast onulltnull annual benenulls annullcosts accruing to all parties, inclunullng tnull tanull null payers, nullom a given regulationnullnullnullling tnullse benenulls annullcosts involves use onullpast researcnullnullnullngs, application onulleconomic principles, empirical investigation, annull pronullssional nullnullmentnull Summary of Impact Analysis The nullwerSaver pilot program will increase the availability of affordable financing for consum- ers who want to manull energy-saving improvements to their homes. The program results from a congressional directive to conduct a pilot of energy-efficient mortgage innovation. The nullnull Department of nullusing and nullban Development nullnullnullresponded to the directive with nullwer null Saver, a program in which the nullderal nullusing nullministration nullnullnulloffers insurance for home improvement loans to finance improvements that foster lower energy consumption. The nullnull guarantee will motivate both lenders and loan investors to participate in financing energy-efficient improvements and to create a secondary marnullt. The pilot program is enullpected to result in the enulltension of as much as nullnullnullnull million in nullnullnull-insured, energy-efficient property improvement loans over nullyears and a resulting energy savings as high as nullnull million nulln present discounted valuenullwith a cost of nullnull million, a total net benefit of nullnullmillion. Over null years, the annualinulld net benefit is null million per year. null scenarios using alternative assumptions, the private benefits of the program could be greater or even negative. The social benefits, however, are significant and include reduced greenhouse gas emissions, reduced morbidity, and increased nullowledge generated concerning the loan program.

238 Mcnullrlane Impact Need for Policy Change nullfordable financing for home energy improvements can unlocnullsubstantial energy, economic, and environmental benefits for individual consumers and for society. Despite the benefits of residential energy efficiency, relatively few homes are as efficient as they could be. nulltimates suggest that less than nullpercent of single-family homes have been fully retrofitted for energy efficiency nullhoi nullanade et al., nullnullnull nullltiple obstacles impede retrofit activity, including inadequate information on the costs and benefits of home energy improvements, limited availability of qualified contrac- tors to perform retrofits, various behavioral barriers, and a lacnullof access to capital to finance the upfront costs of energy improvements. The undersupply of financing may lead to an overcon- sumption of energy and a resulting environmental degradation. Lack of Alternatives to FHA Loans The types of loans currently available are all problematic. nullsecured consumer loans or credit card products for home improvements typically charge high interest rates. nullme equity lines of credit require owners to be willing and able to borrow against the value of their homes during a period when home values are nullt or declining in many marnullts. nullnds for subsidinulld revolving loans are generally limited in availability and do not always cover home improvements. nullility nulln billnull financing nulln which a home energy retrofit loan is amortinulld through an incremental change on a utility billnullhas been resisted by most utilities and only serves a handful of marnullts on a small scale. Property nullsessed Clean nullergy nullnullnull financing programs nulln which financing for retrofits is amortinulled through an incremental increase in property tanull or similar billsnull have generated resistance from federal financial system regulators because of their general requirement to have priority over all enullsting liens on a property, including the first mortgage. nullth the enulleption of a few small pro - grams serving specific marnullts, affordable financing for home energy improvements that renullcts sound lending principles does not enullst nullnull, nullnullnull The programs that generally renullct sound lending principles, such as the nullnnie nulle nullergy nullan, are typically provided as unsecured consumer loans. The lacnull of securitinullation results in higher costs for consumers and a less liquid marnullt for financing than a more conventional mortgage product. nullr mainstream mortgage financing for home energy improvements to be more available and affordable, a viable secondary marnullt for such products is necessary to generate liquidity through capital marnullts investment. Piloting the viability of a secondary marnullt for a federally insured home energy retrofit loan program is thus an appropriate goal for the nullwerSaver program. Market Barriers and Market Failures in Energy-Saving Investment nullrnullt barriers and marnullt failures in energy conservation investment in the residential sector are evident. nullmarnullt barrier is a cost of doing business that is specific to a marnullt and is uncommonly large. null the case of energy-efficient investment, a marnullt barrier could be any private cost of adopting energy-efficient technology null that inhibits the diffusion of the technology compared with null nulle nulltherland nullnullnull for a description of the barriers to energy efficiency in the residential sector.

239Cityscape nulle Inullact of Honull Energy Retronull nullan Insurancenullnullnulllot nullogranull null nullformation has characteristics of a public good. nullcause the supplier of information will be unable to recoup the costs of providing the information, then the marnullt may undersupply information on retrofit technology. null The empirical analysis of DeCanio and nullatnullns nullnullnull suggests the importance of informational diffusion. other types of investment. The enullstence of a marnullt does not require government intervention. null definition, however, a marnullt failure may require government policy to intervene so the marnullt will reach a socially optimal level of investment. nullrnullt failures in energy conservation stem from incentive structures that lead the marnullt to underprovide energy-efficient investment. nullalynullng marnullt barriers may give us insight into why some retrofit pronullcts are not undertanulln. nullme researchers hypothesinull that the simple net present value model that engineers and manu - facturers use overestimates the return on an energy retrofit. null their own studies, these researchers have found that tanullng account of a wider set of variables have led them to conclude that no nullnergy paradonull enullstsnullnot investing in energy-efficient technology is an economically rational decision. Despite such studies, significant debate continues regarding whether investment in energy-saving technology occurs at efficient levels. Two commonly cited marnullt barriers are nullnulluncertainty and nullnullhigh required rates of return. null energy-efficient investment is an irreversible investment for which the returns are uncertain. nullven the nulluctuations in energy prices, consumers may be hesitant to bet on the future direction of energy prices until an obvious long-term trend has emerged. The greater the volatility, the longer investors will postpone their investment in energy-saving equipment. nullssett and nulltcalf nullnullnull found that, accounting for uncertainty, levels of investment in energy-saving technology appear to be optimal. nullgh rates of return are required for energy investments by households because discount rates are high. Discount rates have been estimated for energy-efficient investments and have been found to range from null percent to nullnullpercent nullaffe and nullavins, nullnullnull null contrast, nulltcalf and nullsset nullnullnull found that the median return to an energy-efficient investment nullttic insulationnull is nullnullpercent. Their result is consistent with a Capital nullset Pricing nulldel nullnullnull estimate of a discount rate and provides little evidence of an energy paradonull null is reasonable, however, to doubt the application of Cnullnullto household decisions nullutherland, nullnullnull nullhousehold faces marnullt barriers such as illiquidity and high risnullthat they cannot diversify, so they are linullly to demand a higher compensation than the prevailing average return on business investments. The undersupply of energy-efficient investment can also be enulllained by three marnullt failures nullaffee and nullavins, nullnullnull nullrst, information concerning the energy-saving technology may be in short supply. null nullet, having information on the range of opportunities provided by energy conservation retrofits is critical to optimal decisionmanullng concerning the adoption of a technology. null nullcond, consumers may face artificially low energy prices, which discourage energy-efficient investment. nullnullamples of artificially low energy prices are subsidinulled electricity prices, environmental enullternalities resulting from residential use, and average cost pricing of energy that does not renullct the marginal cost of supply. Third, a manullr incentive problem leading to an undersupply of energy-efficient investment in the residential sector is the inability of current homeowners to recapture the full value of their investment on resale. nullst households manull home purchase decisions infrequently, so it is not linullly that they will have a sufficient bacnullround in property investment to accurately

240 Mcnullrlane Impact assess the value of an energy retrofit. null the homebuyer is not willing to pay for energy efficiency because its value is not transparent, then the homeowner will underinvest in energy efficiency. nullinally, one of the most pressing marnullet failures, the overproduction of emissions, merits a separate discussion. Negative Environmental Externalities of Energy Consumption The primary marnullt failure in the residential energy sector is the overproduction of emissions. The household, however, does not internalinull the public damage that energy consumption brings to the environment, which leads to a nullommon resourcesnullmarnullt failure in which households over - consume energy nullnull, nullnullnull The proposed mortgage

associated with the nullwerSaver program is not the optimal solution because it does not impose the marginal damage of energy consumption on polluters. nulltending the loan for retrofits, however, could reduce the negative enullernalities of energy consumption. The opportunities to reduce residential energy consumption, with resulting reductions in both greenhouse gas emissions and particulate matter, are significant nullPnull nullnullnull nullcording to the nullnull nullergy nullformation nullency nullnull, nullnullnull the residential sector accounts for null percent of the energy consumed in the nullited nullates and null percent of nullnull carbon dionullde emissions behind electricity generation, transportation, and industrial use. The nullnullloan addresses this marnullt failure by lowering the private costs to energy-saving investment. Summary of Notice The Consolidated nullpropriations nullt of nullnull directs nullD to conduct an nullnergy nullficient nullrt - gage nullnovationnullpilot program. The nullt provides nullnullmillion in appropriated funds to support such an initiative. nullmed the nulltrofit Pilot Program, this initiative will be conducted for loans originated during a nullyear period. nullnullhas limited participation in the program to no more than nine lenders and has defined the eligible marnullts that lenders may serve. nullnullenvisions that the pilot program will provide insurance for up to null,nullnullloans during the nullyears, with an enullected average loan sinull of nullnullnullnull The nullnullTitle nullnullme nullprovement nullan program provided an appropriate basis for the nulltrofit Pilot Program. null Therefore, nullnullprovides a set of modifications to the current Title nullProperty null - provement program that yield a new product for use in the pilot. nullthough most of the proposed changes are relatively minor, as a group, and in combination with the appropriated funds, they create an innovative pilot program. nullnullproposes augmenting these changes with incentives for lenders to participate, using funding appropriated under the nullt. Risk Mitigation nullny of the changes that nullnullmade to the Title nullProperty nullprovement program were for mitigating the risnullof loans originated in the nulltrofit Pilot Program. Creating liquidity through null The Title nullprogram insures loans to finance the light or moderate rehabilitation of properties and the construction of nonresidential buildings on the property.

241Cityscape nulle Inullact of Honull Energy Retronull nullan Insurancenullnullnulllot nullogranull securitinulltion requires confidence on the part of the loan investors. nullrst, nullnulladopted stricter underwriting standards null minimum credit score and a manullmum combined loan-to-value rationull to limit the probability of default. nullthough these proposed changes will prevent some consumers from being able to access the program, the changes are appropriate for securitinulld loans. nullcond, nullnullproposes that the holder of the note will be accountable to nullnullfor origination and under - writing errors and that the servicer of the loan will be accountable to nullnullnull for servicing errors. Clarity regarding nullnullclaim payments made because of servicer errors will mitigate risnullfor potential loan investors. Third, nullnullproposes to disallow dealer loans null under the pilot program. Dealer loans have been disproportionately correlated with poor loan performance under Title nulland other home improvement loan programs in the past. null summary, all these changes adnullst the Title nullProperty nullprovement program to enable the program to encourage home improvements that increase energy performance. Interest Cost to Borrowers nullnder the nulletrofit Pilot Program, as under the Title null nullome nullmprovement nulloan program, the marnullet will determine the loan interest rate. nullecent reported interest rates for Title null Property nullmprovement loans have been null and null percent. nullnullnull anticipates that most borrowers under the pilot program will be able to access financing at rates at or below the current interest rate for Title nullloans. nullD will use the funds appropriated under the nullnull Consolidated nullpropriations nullt to support activities that lower costs to borrowers. nullnullproposes to allow other parties to pay discount points or other financing charges in connection with loans under the pilot program. null nullnullpronullcts that nominal interest rates could be reduced significantly as a result of this practice. Changes To Improve Home Energy Performance nullnder the nulletrofit Pilot Program, the loan proceeds may be used primarily for measures that improve home energy performance for single-family, attached, and semidetached owner-occupied homes. Condominiums and fee-simple ownership properties are also eligible. null to null percent of the loan may be used for nonenergy home improvements. nullnullproposes limiting loan maturities to null years, enullept in the case of renewable energy improve- ments, which may be financed with null-year loans. This change better aligns the term of financing with the useful life and benefits of typical home energy improvements. The funds will be disbursed to the borrowernullnullin two incrementsnullnullnullnull percent of the proceeds shall be disbursed at loan fundingnulllosing, and nullnullthe remaining null percent of the proceeds shall be disbursed after the energy retrofit improvements have been completed and verified. This schedule ensures that wornullmay begin but also that the wornullcompleted has been approved under the pilot program. null null general, dealer loans made under the Title nullProperty nullprovement program are marnullted by home improvement contractors and enullcuted in the form of retail sales installment contracts. null nullder the Title nullProperty nullprovement program, the lender may not require or allow any party, other than the borrower, to pay discount points or other financing charges in connection with the loan transaction.

242 Mcnullrlane Impact Targeting Communities nullnulldetermined that the most effective means for testing the program is to target communities that are best suited to deploy the program. nullnulltherefore intends to target the pilot program to communities that received competitive grant funding under the Department of nullergynull nulltter nullildings nullighborhood Program. This program supports place-based retrofit initiativesnullthat is, those initiatives that integrate consumer education and marnullting, audits and other information tools, wornullforce capacity, quality assurance, and financing. nullnullnull is also willing to consider additional marnullts proposed by the lenders seenullng to participate in the program. Targeting the pilot program by choosing communities with an established retrofit program will bias the results of the enullperiment. nullnullnull decided, however, that evaluating a large number of loans was more important than evaluating a wide variety of communities. Benefits and Costs null reduction of energy enullpenditures is one primary benefit to borrowers in the nulletrofit Pilot Program. The nullerican Council for an nullergy-nullficient nullonomy found that residential retrofits deliver an nullrray of benefits beyond energy savings,nullincluding greater comfort, convenience, health, safety, and noise reduction nullmman, nullnullnull These nonenergy benefits have been estimated to be worth from null to nullnullpercent of annual household energy bill savings. Cost of Energy-Efficient Investment The cost of a retrofit includes not only the cost of the energy-efficient investment, but also the cost of financing the investment. The upfront development cost is assumed to be nullnullnullnull null assume that the average loan is characterinulled by a term of nullnull years, a down payment of null percent, an interest rate of null percent, and a null-percent annual insurance premium. The sum of the undiscounted mortgage and insurance premium payments is nullnullnullnull nullr a consumer with a discount rate of nullpercent, the present value of the cost of the investment would be null,nullnull nullr a consumer with a discount rate of nullpercent, the present value of the cost of the loan would be null,nullnull Private Benefits of More Energy-Efficient Homes The net benefit of an energy-saving retrofit depends on the cost of the retrofit, the resulting reduc- tion in energy consumption, the future direction of energy prices, and the consumernulls discount rate. The potential reduction in energy consumption from the retrofit nullechnical efficiencynullprovides us with the value of annual saving at current energy prices. Pinull nullsearch nullnullnull analysis found a null-percent reduction in annual energy bills from an average retrofit of null,nullnull The annual bill savings is nullnull, which is null percent of the original energy-efficient investment. Pinullnull estimate of savings is in line with other studiesnullthe median technical potential for reduced energy consump - tion in the residential sector is null percent for electricity and null percent for gas nulladel et al., nullnullnull null assume a retrofit cost of nullnullnullnullthat provides energy savings over a period of null years. null enullect a higher level of benefits than described in most studies because the level of investment is correspondingly higher. nullr enullmple, if the annual savings were null percent of the investment, then the annual savings would be null,nullnullnull.nullnullnullnullnullnull, or null percent of a householdnull annual

243Cityscape nulle Inullact of Honull Energy Retronull nullan Insurancenullnullnulllot nullogranull energy enullenditures nullnullnullnullfrom the nullnull Consumer nullpenditure nullrveynull nullthough the savings is outside the average by a few percentage points, a high return would be enullected for high levels of investment. nullsuming an energy price growth of nullpercent annually and a discount rate of nullpercent, a pres- ent value of energy saving would be nullnullnullnullover null years. The benefit-cost ratio would be nullnull nullnull,nullnullnullnullnullnull, null which is not far from that of other studies. nullr enullmple, Clinch and nullaly nullnullnull estimated a benefit-cost ratio of nullnull for a discount rate of nullpercent and their estimate is about the midpoint of other studies on energy efficiency. nullen the discount rate is nullpercent, our benefit-cost ratio is null.nullnull, which is below Clinch and nullealynulls of null.nullnull for a discount rate of null percent. Data from the federal nullatherinulltion nullsistance Program indicate that every null invested in weath - erinulltion reduces household energy bills by null.null nullisenberg, nullnullnull The annual benefit, as measured by the potential reduction in energy enullenditure, depends on energy pricesnullas energy prices rise, the energy efficiency is worth more. The longer the life of the investment, the greater the sum of the benefits. nullinally, because benefits are discounted at a higher rate nullhat is, when the future is worth less to a consumernull then the sum of the present value of benefits will be less. nullr a discount rate of nullpercent, no energy price growth, and a lower energy bill savings of null percent, then the investor would only breanulleven after null years. The net benefits of an energy-efficient retrofit are not always positive under the scenarios considered above. null pronullect lifetime of null years would lower the benefit-cost ratio to nullnull. On the other hand, high benefit- cost ratios are attainable under realistic scenariosnullan enullected energy price growth of nullpercent, a discount rate of nullpercent, and a reduction of energy bills of null percent would yield a benefit- cost ratio of nullnull. Offering a loan to pursue the retrofit has two effects on costsnullit adds to the cost through the interest cost and, at the same time, postpones the costs of the investment. The net effect on cost to the consumer depends on the difference between the cost of the loan and the consumernull discount rate. The benefit-cost ratio is higher with the loan if the consumernull discount rate is higher than the mortgage interest rate, and it is lower when the discount rate is lower than the mortgage interest rate. null enullect the loan to have positive effects on investment. nullrst, the consumer discount rates have consistently been at least as high as discount rates in the contenull of energy-saving investment. nullcond, even if the interest cost adds to the costs of the investment, many consumers do not have the necessary funds to undertanull significant investments without a loan. The benefit-cost ratio of the loan-financed investment is nullnull at a discount rate of nullpercent, nullnull at nullpercent, and nullnull at null percent. Emissions Decreasing energy consumption will reduce emissions of pollutants nulluch as particulate matternull that cause health and property damage and greenhouse gases nulluch as carbon dionulldenullthat cause global warming. Data from the Department of nullergy nullnullnull suggest that residential retrofits through the nullatherinulltion nullsistance Program reduced carbon dionullde emissions by an average null nullr a variety of estimates, see table null nullD nullnullnull.

244 Mcnullrlane Impact of nullnull tons per home per year. Over the life of the measures, weatherinulltion is enullected to save null metric tons of carbon dionullde emissions per home. nullcouraging investment in energy-efficient housing is one of the only policy instruments available to nullD for innullencing energy consump - tion in the built environment. Health Benefits nullalth benefits resulting from reduced mortality and morbidity are one of the benefits of energy efficiency. nulleater energy efficiency allows households to afford energy for heating during severe cold or for cooling during intense heat. nulling able to afford energy reduces the risnullof both death and illness for vulnerable populations. Aggregate Benefits and Costs null calculate the aggregate benefits by multiplying the per-loan benefits by the number of loans. null the nullyear nulltrofit Pilot Program, nullnullwould provide insurance on an estimated null,nullnullhome energy retrofit loans. null assume that loans are distributed evenly over that period null,nullnullper monthnull null we tanull the annual saving of null,nullnull nullpercent price growth, and nullpercent discount rate as a base case, then the present value of the technical efficiency of the retrofit is nullnullnullnull and the total benefits would be nullnull million annually nullnullnullnullnullnullnullnullnull. Three primary leanullges to the energy-conserving purpose of the pilot program are nullnulla rebound in energy use, nullnullthe enullent to which the loan product is a windfall versus an incentive, and nullnullthe use of a portion of the loan for purposes other than retrofits. Rebound Effect nullatever the predicted technical efficiencies of an energy retrofit, a householdnull actual savings is linullly to be smaller because of a behavioral response nullown as the nullebound effect.nullnull increasing energy efficiency, the retrofit reduces the enullense of physical comfort and increases the demand for comfort. null fact, the retrofit could be driven by a demand for more heating in the winter and more cooling in the summer. nullthough it is difficult to pinpoint an agreed-upon proportion, Clinch and nullaly nullnullnull found that the rebound effect is usually less than null percent. nullrrel nullnullnull found an upper-end estimate of the rebound effect for space heating and cooling to be null percent. nullnullwise, nullardman nullnullnull found that null percent of the benefits of energy-efficient improvements reduce energy consumption, while null percent go toward increased health and comfort. nullsuming a rebound effect of null percent yields a comfort benefit of null,nullnulland an energy savings of null,nullnull per participant and, given null,nullnullloans annually, there would be nullnullmillion in comfort benefits and nullnull million in private energy savings for each year of the program. The sinull of the rebound effect does not reduce the benefit to a consumer of energy efficiency, but it informs us of how those benefits are allocated between reduced energy costs and increased com- fort. The rebound effect, however, has implications for measuring the public benefit of reducing energy consumption. null the primary goal of an energy efficiency investment program is to reduce emissions, then the amount of benefits going toward reduced energy consumption is critical.

245Cityscape nulle Inullact of Honull Energy Retronull nullan Insurancenullnullnulllot nullogranull Windfall Effect null participants had invested without the loan guarantee, then the program would result in a transfer to consumers nullr windfallnullequal to the decrease in the cost of capital. null have discussed, however, the enullstence of significant marnullt imperfections and the lacnullof affordable financingnullit is, therefore, reasonable to assume that a large proportion, if not all, of the loans would incentivinull new investments. nulldeed, the most complete study of the energy conservation tanullcredit illustrates the effectiveness of federal incentives that reduce the cost of capital by encouraging investment in energy efficiency nullasset and nulltcalf, nullnullnull nullnretronull Innullstnullnt The nullwerSaver loan is not required to finance investments in only energy efficiency. null to null percent may be diverted to other home improvements. null, however, all households elect to use only null percent of the proceeds of the loan to finance energy retrofits, then the energy-related benefits will be proportionally lower. The most common proportion of the loan devoted to energy retrofits is enullected to be null percent. null this case, the total private energy benefits of the program would be nullnull million annually and the energy savings would be nullnullmillion annually. The nonretrofit allowance, however, does offer some benefits. nullrst, the allowance is useful in marnullting the loan and may result in a greater diffusion of the loan product. nullcond, efficiencies may enullst for con - sumers in the nonretrofit portion of the loannullconsumers who need to finance renovations made necessary by the retrofit will not be required to pay the transactions costs for an additional loan. nullt nullgregate Effect The net benefit to the consumer of the loan-financed investment is equal to the total energy benefits nullnergy savings plus comfort benefitsnullless the cost of the investment and the financing costs nullee enullibit null. nullr enullmple, for an annual saving of null,nullnullnullnullpercent reduction of energy billsnull the net benefit to the consumer of the loan-financed nullnullnullnullinvestment is null,nullnull for an annual of reduction of nullnull nullnullpercentnull the net benefit to the consumer is nullnull. The aggregate net benefit to consumers is obtained by multiplying the individual net benefit by the enullected number of loans. null enullect null,nullnullloans annually. nullhibit nullpresents three scenariosnullone in which all of the energy benefits are realinulld by the program, one in which null percent of the benefits are realinulld, and one in which null percent are realinulld. The nullnullpercent scenario assumes there are no leanullgesnullall investment is in energy retrofits. The null-percent Exhibit 1 Retronulls nulldnulled bnullPilot nullnull 42nullRednulltion in nullnnulll Enernull nullll 37nullRednulltion in nullnnulll Enernull nullll nullotal Enernull nullnenulls nullMnull nullotal nullsts nullMnull Net nullnenulls nullMnull nullotal Enernull nullnenulls nullMnull nullotal nullsts nullMnull Net nullnenulls nullMnull nullesent nullalue of Enenullgy nullnenulls annullCosts fonull1 nulleanullof nulllot nullognullm (nullmillions) 100 1null 1null null 1null 1null 10 null 110 null null null null null null null null 1null null null null nullsunulltionsnullnull,nullnullannual energy bill, nullnullrcent discount rate, null,nullnullnullrticinullnts annually, nullnullnullnullcost of retronull, nullnullrcent nullrtgage interest rate, nullnullrcent nullriodic insurance nullenullunull

246 Mcnullrlane Impact scenario assumes the manullmum allocation toward nonconservation uses, with borrowers investing null percent of the loan proceeds in other improvements. The null-percent scenario also assumes nonenergy leanullge but also assumes that nullnull percent would have been invested without the loan nullwindfall effectnull. nullt is reasonable, however, to assume that a large proportion, if not all, of the loans will generate benefits. nullcause a small share of homeowners historically has invested in home energy improvements, nullD believes that the linulllihood that households that receive a loan would have renovated otherwise is small. The estimated energy saving over the lifetime of the program, given a nullnullnull- and nullnull-percent incentive effect, is provided in enullibit null To measure the social value of an investment in energy efficiency, one must add the enullected social benefits of reduced energy consumption. nulln indepth analysis by Clinch and nullealy nullnullnullnullnullnull of domestic energy efficiency found the benefit-cost ratio to be null.nullnull nullwhen the discount rate is null percentnull. nullnergy reduction benefits represent most of the benefits nullnullnullpercentnull followed by health benefits nullnullpercentnull comfort benefits nullnullnullpercentnull and emissions reduction null null.nullpercentnull Their results can be used to estimate the value of nonenergy benefits. nulldding reduced emissions raises the benefit- cost ratio of energy benefits nullsavings plus comfortnull by nullnull percent nullnull.nullnullnullnullnull.nullnullnullnull.nullnullnull from null.nullnull to null.nullnull. The benefit of reduced emissions per consumer is null,nullnullnullnullnull-nullnullnull nullnullnullnullnull. nullth null,nullnull loans, this benefit amounts to nullnullmillion and increases annual net benefits from nullnullmillion to nullnullmillion nulln the nullnullpercent scenarionullor increases the benefit amount from nullnullmillion to nullnullmillion nulln the null-percent scenarionull null The authors assume a rebound effect of null percent. The benefits from reducing emissions may be greater in the current analysis in which the rebound effect is assumed to be null percent. Exhibit 2 1nullnullParticinulltion 75nullParticinulltion nullotal Enernull nullnenulls nullMnull nullotal nullsts nullMnull Net nullnenulls nullMnull nullotal Enernull nullnenulls nullMnull nullotal nullsts nullMnull Net nullnenulls nullMnull nullotal Enenullgy nullnenulls annullCosts of nullognullm (nullmillions) nulleanull1 1null 1null null 110 null null nulleanullnull 1null 11null null 10null null 1null nullotal nullnull nullnull null nullnull 1null null nullnualinullnull null 11 null null null null nullsunulltionsnullnullnullercent reduction in annual energy bill, nullnullrcent discount rate, onullr null yearsnull Transfers Transfers are neither costs nor benefits, because they do not add to or detract from social welfare but instead redistribute income. FHA null is difficult to calculate a precise estimate of the credit subsidy rate for this program absent any data or enullerience. nullwever, we do have estimates for the Title nullProperty nullprovement program

Unknown

247Cityscape nulle Inullact of Honull Energy Retronull nullan Insurancenullnullnulllot nullogranull on which the pilot is based. nullnullestimated the credit subsidy rate for the Title nullProperty nullprove - ment program to be -null.nullnull percent and the enullpected claim rate to be null.nullnull percent. The underwriting and operating features of the pilot program will not contribute to a higher risnullprofile than the Property nullprovement program on which it is based. Thus, the Property nullprovement estimates can be used to illustrate the effect of the pilot program. nullf the credit subsidy rate were -null.nullnull percent and the loan volume nullnull million annually, then the nullnullcould enullect null.null million null.null null nullnull millionnullfrom the pilot. null enullect this program will generate positive transfers for nullnull Consumers The transfer to consumers is equal to the difference between the nullnullinterest rate and the interest rates on alternative types of loans available for retrofits. null the nenull best interest rate for the con - sumer were fairly low, at nullpercent, but above what is enullected for a Title nullProperty nullprovement loan, then this loan would represent a transfer of appronullmately null,nullnullper household nullnull,nullnull - null,nullnull. nullch a transfer, or windfall, would apply for households that would have financed an energy retrofit without the incentive of an nullnullloan. null a previous discussion, however, we concluded that most households would not invest without the nullnullloan. Conclusions The purpose of the nullwerSaver pilot program is to test and demonstrate the feasibility of low-cost financing for secured home energy retrofit loans. nullthough other financing options are available for consumers, these alternative programs typically enullerience minimal usage. nullong the many broader obnullctives of this program are creating a marnullt for a new type of loan, reducing marnullt barriers to investment in energy efficiency, and limiting the carbon footprint of the housing stocnull null nullnullis able to learn from the pilot and launch a broader program, then it may achieve these broader obnullctives. References nullardman, nullenda. nullnull. nulltrategies for nulltion.nullnull nullmestic Energy annullnullnullrnullble nullrmtnull , edited by Thomas null nullrnulls. nullndon, nullited nullngdomnullTaylor nullnullancis. Choi nullanade, nullnnah, nulln Creyts, nullton Dernullch, Phillip nullrese, nullott nullquist, and nulln Ostrowsnull. nullnull nullulynull nulllocnullng Energy Enullciency in tnull nullSnullEconomy . nullsterdam, nulltherlandsnull nullnullnsey nullCo. Clinch, null Peter, and nullhn D. nullaly. nullnull. nullost-nullnefit nullalysis of Domestic nullergy nullficiency,null Energy nulllicy nullnullnullnullnullnull Congressional nulldget Office nullnullnull nullnull. nulle Economics onullClimate Cnullngenullnullnullimernull nullshington, DCnullCongress of the nullited nullates, Congressional nulldget Office. DeCanio, nullephen null, and nulllliam null nulltnullns. nullnull. nullnvestment in nullergy nullficiencynullDo the Characteristics of nullrms nulltternull Review onullEconomics annullStatistics nullnullnullnullnull.

248 Mcnullrlane Impact nullsenberg, nullff. nullnull nullarchnull nullcnullround Data and nullatistics. nullatherinulltion nullsistance Program Technical nullmorandum. Prepared for the nullnull Department of nullergy. Oanullnulldge, Tnull Oanullnulldge nulltional nullboratory. nullsset, nullvin null, and nulllbert nulltcalf. nullnull. nullnergy TanullCredits and nullsidential Conservation nullvestmentnullnullidence nullom Panel Data,null nullurnal onullnullblic Economics nullnullnullnullnullnull nullffee, nullam null, and nullbert null nullavins. nullnull. nullnergy nullficient nullvestments and Public Policy,null nulle Energy nullurnal null nullnull nullnullnull nullint Center for nullusing nulludies of nullrvard nulliversity. nullnull. State onulltnull Nationnull nullusing 200null Cambridge, nullnullnullrvard nulliversity, nullint Center for nullusing nulludies. nulltcalf, nulllbert null, and nullvin null nullssett. nullnull nullugustnull nulleasuring the nullergy nullvings nullom nullme nullprovement nullvestmentsnullnullidence nullom nullnthly nulllling Data,null Review onullEconomics annull Statistics null nullnull nullnullnullnull nulladel, nullteven, nullnna nullonis nullhipley, and null. nulleal nulllliott. nullnullnullnull. nullnulle nullecnullnical, Economic annull nullcnullievable nulltential nullr Energynullnullciency in tnull nullSnullnullnulltanullnalysis onullRecent Stunullesnull Presented at the nullnull nullnullnullnullmmer nulludy on nullergy nullficiency in nullildings. Pinull nullsearch nullC. nullnull. Energy Enullcient nullmes . nullulder, COnullPinull nullsearch nullC. nullrrel, nulleven. nullnull nullctobernull nulle RebounnullEnullectnullnull nullsessment onulltnull Evinullnce nullr Economynullinull Energy Savings nullom nullprovenullEnergy Enullciencynull nullndon, nullited nullngdomnullnull nullergy nullsearch Centre. nulltherland, nullnald null nullnull. nullarnullt nullrriers to nullergy-nullficiency nullvestments,null Energy nullurnal null nullnull nullnullnull nullnull Department of nullergy nullOnull. nullnull nullpril nullnull nulleatherinulltion nullsistance Programnullnullow the nullcts.null httpnullnullww.waptac.orgnullatanulllesnullublicnullnformationnullapfacts.docnull nullccessed October null, nullnullnull nullnull Department of nullusing and nullban Development nullnullnull nullnull nullunenull nullmpact nullalysis of nullderal nullusing nullministration Proposed Pilot Programnullnullowernullvernullnullme nullergy nulltrofit nullan nullsurance nullnullnullnullnullnull.null httpnullnullortal.hud.govnulludportalnullocumentsnulluddocnulldnulla- powersaverretrofit.pdf nullccessed October nullnull nullnull nullergy nullformation nullency nullnullnull nullnull nullarchnull nulluildings nullergy Data nullonullnull httpnullnull buildingsdataboonulleren.doe.govnull nullccessed October nullnull nullnull nullvironmental Protection nullency nullPnull. nullnulla. nulleatures and nullnefits of nullergy nullar nullalified nullw nullmes.null httpnullnullww.energystar.govnullndenullcfmnullnullewnullomes.nhnulleatures nullccessed October nullnull nullnull nullnullb. nulllimate Change.null www.epa.govnulllimatechangenull nullccessed nullbruary nullnullupdated nullbruary nullnull

249Cityscape nulle Inullact of Honull Energy Retronull nullan Insurancenullnullnulllot nullogranull nullditional Reading Carliner, nullchael, nullnull nullwles, and nulle nullbbia. nullnull nullecembernull nulle Valuation onullEnergy Enullciency in nullomesnull Prepared for the null.null. Department of nullousing and nullrban Development. Davidsonville, nullDnull nullwport Partners nullCnullnullnull Thorne nullmman, nullennifer. nullnullnullnull nullnullaynull. Valuation onull NonnullEnergy Benenullts nullo nulletermine CostnullEnullnullectiveness onullnullole nulluse Retronulls nullogramsnullnullnullterature Reviewnull nullshington, DCnullnullerican Council for an nullergy-nullficient nullonomy.

250 Mcnullrlane Impact

251Cityscapenullnullnullurnal of nulllicy Development and nullsearch • Volume 13, Number 3 • 2011 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Cityscape Correction null the volume null, number nullissue of Cityscape, in the article titled, nullental nullusing nullfordability Dynamics, nullnullnullnullnullnullby nullb Collinson, there was an error in the legend in enullibit null The legend indicated that the diamond-point line corresponded to the homeownership rate and the circle-point line renullcted the ratio of renter income to household income. These marnullrs should be reversednullthe homeownership rate is depicted with a circle-point line, while the ratio of incomes is represented by the diamond-point line. The correct version of this enullibit appears below. Exhibit 2 Rentenullnullcomenullll Householnullnullcome nullenullus HomeonullenullhinullRate, 1null0null00null 70 68 66 64 62 1990 1995 2000 Year 2005 2010 Percent Homeownership Rate Median Renter Income/Median All Household Income SourcenullCensus nullreau, Current nullnulllation Surnully, nullblic Use Microdata Sanullle, nullnullnullnullnull

Cityscape U.S. Department of Housing and Urban Development Office of Policy Development and Research Washington, Dnullnullnullnullnullnull Official nullsiness Penalty for Private Use nullnull nullRSnullnullnullSS nullnull U.S. POSnullnullnullPnullD HUD Pnullnullnullnull. nullnullnull U.S. Department of Housing and Urban Development Contents Symposium Crime and Urban Form Guest Editor: Ronald E. Wilson Guest Editor’s Introduction..........................................................................................1 Sex Offenders, Residence Restrictions, Housing, and Urban Morphology: A Review and Synthesis ..............................................................................................7 by Tony H. Grubesic, Alan T. Murray, and Elizabeth A. Mack The Coaction of Neighborhood and Individual Effects on Juvenile Recidivism ...............33 by Philip W. Harris, Jeremy Mennis, Zoran Obradovic, Alan J. Izenman, and Heidi E. Grunwald Mapping the Spatial Influence of Crime Correlates: A Comparison of Operationalization Schemes and Implications for Crime Analysis and Criminal Justice Practice ...........................................................................................57 by Joel M. Caplan HOPE VI Resident Displacement: Using HOPE VI Program Goals To Evaluate Neighborhood Outcomes .........................................................................................85 by Roderick W. Jones and Derek J. Paulsen Using the Weighted Displacement Quotient To Explore Crime Displacement From Public Housing Redevelopment Sites ........................................................................103 by Meagan Cahill Do Vouchers Help Low-Income Households Live in Safer Neighborhoods? Evidence on the Housing Choice Voucher Program ..................................................135 by Michael C. Lens, Ingrid Gould Ellen, and Katherine O’Regan Modeling Criminal Distance Decay ......................................................................... 161 by Mike O’Leary Crime and Place: Rapidly Evolving Research Methods in the 21st Century .................199 by Patricia Brantingham Advances in the Identification of Space As a Structuring Factor of Social Reality ........205 by Leandro Ramos A South African Commentary on the Articles in the Cityscape Symposium on Crime and Urban Form ..........................................................................................209 by Peter M.U. Schmitz, Tinus Kruger, and Antony K. Cooper Departments Graphic Detail Visualizing Racial Segregation Differently: Exploring Geographic Patterns in Context ... 213 by Ronald E. Wilson Data Shop A Beginner’s Guide To Creating Small-Area Cross-Tabulations ..................................225 by Haydar Kurban, Ryan Gallagher, Gulriz Aytekin Kurban, and Joseph Persky Impact The Impact of Home Energy Retrofit Loan Insurance: A Pilot Program ........................237 by Alastair McFarlane Correction............................................................................................................251 Volume 13, Number 3 • 2011