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Predictive police patrolling to target hotspots and cover response demand

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journal contribution
posted on 21.07.2017, 12:51 by Jo Leigh, Sarah DunnettSarah Dunnett, Lisa JacksonLisa Jackson
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.

Funding

This work was supported by the Economic and Social Research Council [ES/K002392/1].

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Annals of Operations Research

Volume

283

Pages

395 - 410

Citation

LEIGH, J.M., DUNNETT, S.J. and JACKSON, L.M., 2017. Predictive police patrolling to target hotspots and cover response demand. Annals of Operations Research, 283, pp.395-410.

Publisher

© The Authors. Published by Springer Verlag

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

20/04/2017

Publication date

2017

Notes

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

0254-5330

eISSN

1572-9338

Language

en