RT Book
T1 Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?
A1 Hardyns, Wim
A2 Van den Poel, Dirk
A2 Van de Weghe, Nico
A2 Snaphaan, Thom
A2 Rummens, Anneleen
A2 Pauwels, Lieven
LA English
YR 2021
UL https://krimdok.uni-tuebingen.de/Record/1866322060
AB 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
DO 10.3390/ijgi10060369