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Redrawing hot spots of crime in Dallas, Texas

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  • Wheeler, Andrew Palmer

    (Gainwell Technologies)

  • 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_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:nmq8r_v1
    DOI: 10.31219/osf.io/nmq8r_v1
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    References listed on IDEAS

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    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.
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    4. 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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