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Article

Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

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Department of Criminology, Criminal Law and Social Law, Ghent University, 9000 Ghent, Belgium
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Department of Geography, Ghent University, 9000 Ghent, Belgium
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Department of Marketing, Innovation and Organization-Data Analytics, Ghent University, 9000 Ghent, Belgium
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Faculty of Social Sciences, University of Antwerp, 2000 Antwerp, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Matt Ashby, Patricio Estevez-Soto, Sophie Curtis-Ham, José Luis Hernandez, Spencer Chainey and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(6), 369; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060369
Received: 6 April 2021 / Revised: 25 May 2021 / Accepted: 27 May 2021 / Published: 31 May 2021
(This article belongs to the Special Issue Geographic Crime Analysis)
This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for predictive policing models is evaluated, using mobile phone data (with a total of 9,397,473 data points) as a proxy. The results show that ambient population correlates more strongly with crime than residential population. Crime rates based on ambient population designate different problem areas than crime rates based on residential population. The prediction performance of predictive policing models can be improved by using ambient population instead of residential population. These findings support that ambient population is a more suitable population-at-risk measure, as it better reflects the underlying dynamics in spatiotemporal crime trends. Its use has therefore much as-of-yet unused potential not only for criminal research and theory testing, but also for intelligence-led policy and practice. View Full-Text
Keywords: ambient population; mobile phone data; crime rates; predictive policing; intelligence-led policing ambient population; mobile phone data; crime rates; predictive policing; intelligence-led policing
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MDPI and ACS Style

Rummens, A.; Snaphaan, T.; Van de Weghe, N.; Van den Poel, D.; Pauwels, L.J.R.; Hardyns, W. Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime? ISPRS Int. J. Geo-Inf. 2021, 10, 369. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060369

AMA Style

Rummens A, Snaphaan T, Van de Weghe N, Van den Poel D, Pauwels LJR, Hardyns W. Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime? ISPRS International Journal of Geo-Information. 2021; 10(6):369. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060369

Chicago/Turabian Style

Rummens, Anneleen, Thom Snaphaan, Nico Van de Weghe, Dirk Van den Poel, Lieven J.R. Pauwels, and Wim Hardyns. 2021. "Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?" ISPRS International Journal of Geo-Information 10, no. 6: 369. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060369

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