Unknown

by Unknown

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

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
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nullubesic, Tony null, Timothy C. Matisnullw, and Anulln T. Murray. 20null. nullnullnullnullnder Residence
Restrictions and nullusinnullAnullinullbinulltynullAssessinnullthe nullnullcts onullnullban Mornullonullnull. nullrkinnullnullnullr
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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
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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
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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
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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

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.

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.
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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

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
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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
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nullmogeneous nullts of nulltablishments and nullcilities,null Crime nullevention Stunulles nullnullnullnullnullnull
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nullwrence null nullerman, David P. nullrrington, nullandon C. nulllsh, and Doris null nullcnullnnulle. nullw
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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

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

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
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101Cityscape
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102 Crime and Urban Form
nullnes and nullulsen
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nullantitative Criminology null nullnull nullnullnull
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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
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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
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nullditional Reading
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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.

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
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2005
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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
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2009
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2005
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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.
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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

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
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nullnull
nullnullnull
nullstin, null
nullnullnullnull
null0,nullnull
null
1null
nullnullnull
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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

nullnullinnull

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
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null,0null
Ontanullo, Cnull
nullnullnullnull
null,nullnull
null
11null
nullnull
1null
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Onullnullannullnullnull, null
1null,nullnull
null,nullnull
null
null
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0
1,null0
nullsanullna, Cnull
1null,null1
null,nullnull
null
null
1,0null
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0
nullnull0
nullsanullna, null
1null,nullnull
null,null0
null
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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
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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
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null0null
San nulltonio, null
1,nullnullnull1
nullnull0null
null
1null
10,null1
1,nullnull
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null,nullnull
San nullnull
nanull
nullno, Cnull
nullnullnullnull
null,0null
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1nullnullnull
San nullego, Cnull
1,nullnullnullnull
null0,nullnull
null
null
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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
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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

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
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.

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

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

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.
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198 Crime and Urban Form
O’nullary
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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
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Profiling nullstem,null Benullvioral Sciences nulltnull nullw nullnullnullnullnullnull
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nullographic Profiling,null Blue nullne nullganullne , nullrilnullnull.
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nullycnulllogy nullnullnullnullnullnull
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nullycnulllogy null nullnull nullnullnullnull
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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

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

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

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
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