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Cityscape: Volume 20 Number 1 | Selected Outcomes of Housing Assistance


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 Research (PD&R) of the U.S. Department of Housing and Urban Development.

Selected Outcomes of Housing Assistance

Volume 20, Number 1

Mark D. Shroder

Michelle P. Matuga

Calculating Varying Scales of Clustering Among Locations

Ron Wilson
University of Maryland, Baltimore County
Alexander Din
Maryland Department of Housing and Community Development

The views expressed in this article are those of the authors and do not represent the official positions or policies of the State of Maryland.

The Nearest Neighbor Index (NNI) is a spatial statistic that detects geographical patterns of clustered or dispersed event locations. Unless the locations are randomly distributed, the distances of either clustered or dispersed nearest neighbors form a skewed distribution that biases the average nearest neighbor distance used in calculating the NNI. If the clustering or dispersion of locations is moderate to extreme, the NNI can be inaccurate if the skew is substantial. Using Housing Choice Voucher program residential locations, we demonstrate in this article the method to derive an NNI based on a median and two quartiles that more accurately represents the midpoint of a set of nearest neighbor distances. We also demonstrate how to use these alternative point estimates to gauge multiple scales of clustering from different positions across the nearest neighbor distance distribution. Finally, we discuss how to use the average and standard deviation distances from the calculation of each NNI to more comprehensively gauge the scale of the geographic patterns. We also include a Python program that creates a randomized set of locations to calculate statistical significance for the median and quartile NNIs.

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