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

Anneleen Rummens,Wim Hardyns,Thom Snaphaan,Lieven J R Pauwels,Dirk Van Den Poel,Nico Van De Weghe

doi:10.3390/ijgi10060369

Anneleen Rummens, Wim Hardyns + Show 4 more

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https://doi.org/10.3390/ijgi10060369

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Abstract

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.

Highlights

In many fields, decision-making processes are increasingly based on intelligence gained from big data, complex datasets containing large amounts of data, from which new information can be extracted
To compare the crime rates when calculated with the ambient population versus the residential population, we conducted our analysis at the statistical sector level
All three crime types showed a stronger correlation with ambient population than with residential population

Summary

Introduction

Decision-making processes are increasingly based on intelligence gained from big data, complex datasets containing large amounts of data, from which new information can be extracted. The use of big data offers an opportunity to improve the analysis and prediction of spatiotemporal concentrations of crime It is empirically well-established within environmental criminology that crime patterns show significant spatiotemporal variability, with crime concentrations at specific times (i.e., burning times) and specific places (i.e., hotspots) [5,6,7]. The areas and times under investigation differ in several ways, such as magnitude, population characteristics, and number of visitors (e.g., work-related or tourists). To take those differences into account, crime rates or indexes are frequently used within criminological research.

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