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Predictive police patrolling to target hotspots and cover response demand

Author

Listed:
  • Johanna Leigh

    (Loughborough University)

  • Sarah Dunnett

    (Loughborough University)

  • Lisa Jackson

    (Loughborough University)

Abstract

Police forces are constantly competing to provide adequate service whilst faced with major funding cuts. The funding cuts result in limited resources hence methods of improving resource efficiency are vital to public safety. One area where improving the efficiency could drastically improve service is the planning of patrol routes for incident response officers. Current methods of patrolling lack direction and do not consider response demand. Police patrols have the potential to deter crime when directed to the right areas. Patrols also have the ability to position officers with access to high demand areas by pre-empting where response demand will arise. The algorithm developed in this work directs patrol routes in real-time by targeting high crime areas whilst maximising demand coverage. Methods used include kernel density estimation for hotspot identification and maximum coverage location problems for positioning. These methods result in more effective daily patrolling which reduces response times and accurately targets problem areas. Though applied in this instance to daily patrol operations, the methodology could help to reduce the need for disaster relief operations whilst also positioning proactively to allow quick response when disaster relief operations are required.

Suggested Citation

  • Johanna Leigh & Sarah Dunnett & Lisa Jackson, 2019. "Predictive police patrolling to target hotspots and cover response demand," Annals of Operations Research, Springer, vol. 283(1), pages 395-410, December.
    • Handle: RePEc:spr:annopr:v:283:y:2019:i:1:d:10.1007_s10479-017-2528-x
    DOI: 10.1007/s10479-017-2528-x
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    References listed on IDEAS

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    1. Mark S. Daskin & Edmund H. Stern, 1981. "A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment," Transportation Science, INFORMS, vol. 15(2), pages 137-152, May.
    2. Kevin Curtin & Karen Hayslett-McCall & Fang Qiu, 2010. "Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models," Networks and Spatial Economics, Springer, vol. 10(1), pages 125-145, March.
    3. Shirley (Rong) Li & Burcu B Keskin, 2014. "Bi-criteria dynamic location-routing problem for patrol coverage," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(11), pages 1711-1725, November.
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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. Thyago Celso Cavalcante Nepomuceno & Katarina Tatiana Marques Santiago & Cinzia Daraio & Ana Paula Cabral Seixas Costa, 2022. "Exogenous crimes and the assessment of public safety efficiency and effectiveness," Annals of Operations Research, Springer, vol. 316(2), pages 1349-1382, September.
    3. Isloana Karla de França Barros & Thyago Celso Cavalcante Nepomuceno & Fernando Henrique Taques, 2024. "Assessing Police Technical Efficiency and the COVID-19 Technological Change from the Pact for Life Perspective," World, MDPI, vol. 5(3), pages 1-16, September.
    4. Godé, Cécile & Brion, Sébastien, 2024. "The affordance-actualization process of predictive analytics: Towards a configurational framework of a predictive policing system," Technological Forecasting and Social Change, Elsevier, vol. 204(C).

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