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Personalized crime location prediction

Published online by Cambridge University Press:  28 April 2016

MOHAMMAD A. TAYEBI ,
UWE GLÄSSER ,
MARTIN ESTER  and
PATRICIA L. BRANTINGHAM
MOHAMMAD A. TAYEBI
Affiliation:
School of Computing Science, Simon Fraser University, B.C., Canada email: [email protected], [email protected], [email protected]
UWE GLÄSSER
Affiliation:
School of Computing Science, Simon Fraser University, B.C., Canada email: [email protected], [email protected], [email protected]
MARTIN ESTER
Affiliation:
School of Computing Science, Simon Fraser University, B.C., Canada email: [email protected], [email protected], [email protected]
PATRICIA L. BRANTINGHAM
Affiliation:
School of Criminology, Simon Fraser University, B.C., Canada email: [email protected]
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Abstract

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.

Keywords

Spatial crime analysisRandom walk modelActivity spaceCrime occurrence spaceCo-offending networks
Type
Papers
Information
European Journal of Applied Mathematics , Volume 27 , Issue 3: Mathematical Modelling of Crime and Security , June 2016 , pp. 422 - 450
Copyright
Copyright © Cambridge University Press 2016 

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