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An Algorithm for Deploying a Crime Directed (Tactical) Patrol Force

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  • Kenneth Chelst

    (Wayne State University)

Abstract

This paper presents an algorithm for deploying a crime directed patrol force. The optimization problem is formulated as the allocation of N patrol among R high crime regions so as to maximize the weighted probability of a patrol intercept of a crime. The algorithm has full sensitivity analysis capabilities. This capability is critical because the model's input parameters include (1) crime weights, which in general will have a subjective component, (2) crime descriptive data, which are difficult to estimate, and (3) crime frequency data, which are likely to change with time. The paper presents an illustrative application of the algorithm. The resultant allocation is compared to a strategy which allocates patrol units in direct proportion to each region's total crime rate. The optimal allocation significantly increased the probability of an intercept.

Suggested Citation

  • Kenneth Chelst, 1978. "An Algorithm for Deploying a Crime Directed (Tactical) Patrol Force," Management Science, INFORMS, vol. 24(12), pages 1314-1327, August.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:12:p:1314-1327
    DOI: 10.1287/mnsc.24.12.1314
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    Cited by:

    1. Abdolmajid Yolmeh & Melike Baykal-Gürsoy, 2018. "Urban rail patrolling: a game theoretic approach," Journal of Transportation Security, Springer, vol. 11(1), pages 23-40, June.
    2. Sukanya Samanta & Goutam Sen & Soumya Kanti Ghosh, 2022. "A literature review on police patrolling problems," Annals of Operations Research, Springer, vol. 316(2), pages 1063-1106, September.
    3. Verma, Arvind, 1998. "The fractal dimension of policing," Journal of Criminal Justice, Elsevier, vol. 26(5), pages 425-435, September.
    4. Steve Alpern & Alec Morton & Katerina Papadaki, 2011. "Patrolling Games," Operations Research, INFORMS, vol. 59(5), pages 1246-1257, October.
    5. Kyle Y. Lin & Michael P. Atkinson & Timothy H. Chung & Kevin D. Glazebrook, 2013. "A Graph Patrol Problem with Random Attack Times," Operations Research, INFORMS, vol. 61(3), pages 694-710, June.
    6. Kyle Y. Lin & Michael P. Atkinson & Kevin D. Glazebrook, 2014. "Optimal patrol to uncover threats in time when detection is imperfect," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(8), pages 557-576, December.

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