IAENG_ICOR_PolicePatrolling_2016.pdf (220.41 kB)
Download file

Predictive policing using hotspot analysis

Download (220.41 kB)
conference contribution
posted on 19.04.2016, 13:34 by Johanna Leigh, Sarah DunnettSarah Dunnett, Lisa JacksonLisa Jackson
Policing approaches to patrolling and response to incidents can be more effective if predictive policing is used to make decisions. Predictive policing uses information such as historical crime data to predict crime patterns and response demand. This information can then be used to direct resources more efficiently. Historical crime data is used to identify high crime areas through kernel density estimation. It is also used to anticipate the levels of response demand. Both of these factors are used to determine how to direct police patrols. This study looks at identifying high crime areas which require an officer presence and predicting the possible response demand to increase the efficiency of response officer patrols. This study shows how kernel density estimation is used for crime mapping and a maximum coverage location problem is formulated to maximize demand coverage. This is then solved using exhaustive search and tabu search. Tabu search gave sub-optimal results but gave significant reductions in computational time.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

The International MultiConference of Engineers and Computer Scientists 2016

Citation

LEIGH, J.M., DUNNETT, S.J. and JACKSON, L.M., 2016. Predictive policing using hotspot analysis. Presented at The International Multi-Conference of Engineers and Computer Scientists (IMECS 2016), Hong Kong, 16-18th. Mar.

Publisher

Published by IAENG

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

04/01/2016

Publication date

2016

Notes

This paper is a conference paper.

ISBN

9789881925381

Language

en

Location

Hong Kong