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A Statistical Approach to Crime Linkage

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  • Michael D. Porter

Abstract

The object of this article is to develop a statistical approach to criminal linkage analysis that discovers and groups crime events that share a common offender and prioritizes suspects for further investigation. Bayes factors are used to describe the strength of evidence that two crimes are linked. Using concepts from agglomerative hierarchical clustering, the Bayes factors for crime pairs are combined to provide similarity measures for comparing two crime series. This facilitates crime series clustering, crime series identification, and suspect prioritization. The ability of our models to make correct linkages and predictions is demonstrated under a variety of real-world scenarios with a large number of solved and unsolved breaking and entering crimes. For example, a naive Bayes model for pairwise case linkage can identify 82% of actual linkages with a 5% false positive rate. For crime series identification, 74%--89% of the additional crimes in a crime series can be identified from a ranked list of 50 incidents.

Suggested Citation

  • Michael D. Porter, 2016. "A Statistical Approach to Crime Linkage," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 152-165, May.
    • Handle: RePEc:taf:amstat:v:70:y:2016:i:2:p:152-165
    DOI: 10.1080/00031305.2015.1123185
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    Cited by:

    1. Alex Chohlas-Wood & E. S. Levine, 2019. "A Recommendation Engine to Aid in Identifying Crime Patterns," Interfaces, INFORMS, vol. 49(2), pages 154-166, March.
    2. Wheeler, Andrew Palmer & Riddell, Jordan R. & Haberman, Cory P., 2019. "Breaking the chain: How arrests reduce the probability of near repeat crimes," SocArXiv 7tazd, Center for Open Science.

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