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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.

  • Urban Problems and Spatial Methods
  • Volume 17, Number 1
  • Managing Editor: Mark D. Shroder
  • Associate Editor: Michelle P. Matuga

Risk Terrain Modeling for Spatial Risk Assessment

Joel M. Caplan
Leslie W. Kennedy
Jeremy D. Barnum
Rutgers University

Eric L. Piza
John Jay College of Criminal Justice


Spatial factors can influence the seriousness and longevity of crime problems. Risk terrain modeling (RTM) identifies the spatial risks that come from features of a landscape and models how they colocate to create unique behavior settings for crime. The RTM process begins by testing a variety of factors thought to be geographically related to crime incidents. Valid factors are selected and then weighted to produce a final model that basically paints a picture of places where crime is statistically most likely to occur. This article addresses crime as the outcome event, but RTM can be applied to a variety of other topics, including injury prevention, public health, traffic accidents, and urban development. RTM is not difficult to use for those who have a basic skillset in statistics and Geographic Information Systems, or GISs. To make RTM more accessible to a broad audience of practitioners, however, Rutgers University developed the Risk Terrain Modeling Diagnostics (RTMDx) Utility, an app that automates RTM. This article explains the technical steps of RTM and the statistical procedures that the RTMDx Utility uses to diagnose underlying spatial factors of crime at existing high-crime places and to identify the most likely places where crime will emerge in the future, even if it has not occurred there already. A demonstrative case study focuses on the process, methods, and actionable results of RTM when applied to property crime in Chicago, Illinois, using readily accessible resources and open public data.

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