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Redrawing hot spots of crime in Dallas, Texas

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  • Wheeler, Andrew Palmer

    (University of Texas at Dallas)

  • Reuter, Sydney

Abstract

In this work we evaluate the predictive capability of identifying long term, micro place hot spots in Dallas, Texas. We create hot spots using a hierarchical clustering algorithm, using law enforcement cost of crime estimates as weights. Relative to the much larger current hot spot areas defined by the Dallas Police Department, our identified hot spots are much smaller (under 3 square miles), and capture crime harm at a higher density per the Predictive Accuracy Index statistic. We also show that the hierarchical clustering algorithm captures a wide array of hot spot types; some one or two addresses, some street segments, and others an agglomeration of larger areas. This suggests identifying hot spots based on a specific unit of aggregation (e.g. addresses, street segments), may be less efficient than using a hierarchical clustering technique in practice. Code and data to reproduce the analysis can be downloaded from https://www.dropbox.com/sh/kcask6pinaaaz4v/AAC4CXk6NzUweyld2n4OznzWa?dl=0

Suggested Citation

  • Wheeler, Andrew Palmer & Reuter, Sydney, 2020. "Redrawing hot spots of crime in Dallas, Texas," SocArXiv nmq8r, Center for Open Science.
    • Handle: RePEc:osf:socarx:nmq8r
    DOI: 10.31219/osf.io/nmq8r
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    References listed on IDEAS

    as
    1. Hunt, Priscillia Evelyne & Saunders, Jessica & Kilmer, Beau, 2019. "Estimates of Law Enforcement Costs by Crime Type for Benefit-Cost Analyses," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 10(1), pages 95-123, April.
    2. Larson, Richard C., 1975. "What happened to patrol operations in Kansas city? A review of the Kansas city preventive patrol experiment," Journal of Criminal Justice, Elsevier, vol. 3(4), pages 267-297.
    3. Drawve, Grant & Wooditch, Alese, 2019. "A research note on the methodological and theoretical considerations for assessing crime forecasting accuracy with the predictive accuracy index," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    4. Wheeler, Andrew Palmer & Gerell, Manne & Yoo, Youngmin, 2019. "Testing the Spatial Accuracy of Address Based Geocoding for Gun Shot Locations," SocArXiv hrtcf, Center for Open Science.
    5. G. O. Mohler & M. B. Short & Sean Malinowski & Mark Johnson & G. E. Tita & Andrea L. Bertozzi & P. J. Brantingham, 2015. "Randomized Controlled Field Trials of Predictive Policing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1399-1411, December.
    6. Wheeler, Andrew Palmer & Steenbeek, Wouter, 2020. "Mapping the risk terrain for crime using machine learning," SocArXiv xc538, Center for Open Science.
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