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A Recommendation Engine to Aid in Identifying Crime Patterns

Author

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  • Alex Chohlas-Wood

    (New York City Police Department, New York, New York 10038)

  • E. S. Levine

    (New York City Police Department, New York, New York 10038)

Abstract

Police investigators are routinely asked to search for and identify groups of related crimes, known as patterns. Investigators have historically built patterns with a process that is manual, time-consuming, memory based, and liable to inefficiency. To improve this process, we developed a set of three supervised machine-learning models, which we called Patternizr , to help identify related burglaries, robberies, and grand larcenies. Patternizr was trained on 10 years of manually identified patterns. Problematic administrative boundaries and sensitive suspect attributes were hidden from the models. In tests on historical examples from New York City, the models perfectly rebuild approximately one-third of test patterns and at least partially rebuild approximately four-fifths of these test patterns. The models have been deployed to every uniformed member of the New York City Police Department through a custom software application, allowing investigators to prioritize crimes for review when building a pattern. They are used by a team of civilian crime analysts to discover new crime patterns and aid in making arrests.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:49:y:2019:i:2:p:154-166
    DOI: 10.1287/inte.2019.0985
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    References listed on IDEAS

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    1. Michael D. Porter, 2016. "A Statistical Approach to Crime Linkage," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 152-165, May.
    2. E. S. Levine & Jessica Tisch & Anthony Tasso & Michael Joy, 2017. "The New York City Police Department’s Domain Awareness System," Interfaces, INFORMS, vol. 47(1), pages 70-84, February.
    3. 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.
    4. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
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    Cited by:

    1. Tschernutter, Daniel & Feuerriegel, Stefan, 2025. "Data-driven dynamic police patrolling: An efficient Monte Carlo tree search," European Journal of Operational Research, Elsevier, vol. 321(1), pages 177-191.
    2. Justin J. Boutilier & Timothy C. Y. Chan, 2023. "Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 64-83, January.
    3. 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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