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Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?

Anneleen Rummens, Thom Snaphaan (UGent) , Nico Van de Weghe (UGent) , Dirk Van den Poel (UGent) , Lieven Pauwels (UGent) and Wim Hardyns (UGent)
(2021) ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION. 10(6).
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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.
Keywords
ambient population, mobile phone data, crime rates, predictive policing, intelligence-led policing, AMBIENT POPULATION, SPATIAL-ANALYSIS, VIOLENT CRIME, MODEL, STREET, TRENDS, IMPACT, PLACES, RISK, LAW

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MLA
Rummens, Anneleen, et al. “Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 10, no. 6, 2021, doi:10.3390/ijgi10060369.
APA
Rummens, A., Snaphaan, T., Van de Weghe, N., Van den Poel, D., Pauwels, L., & Hardyns, W. (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(6). https://doi.org/10.3390/ijgi10060369
Chicago author-date
Rummens, Anneleen, Thom Snaphaan, Nico Van de Weghe, Dirk Van den Poel, Lieven 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 (6). https://doi.org/10.3390/ijgi10060369.
Chicago author-date (all authors)
Rummens, Anneleen, Thom Snaphaan, Nico Van de Weghe, Dirk Van den Poel, Lieven 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 (6). doi:10.3390/ijgi10060369.
Vancouver
1.
Rummens A, Snaphaan T, Van de Weghe N, Van den Poel D, Pauwels L, 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).
IEEE
[1]
A. Rummens, T. Snaphaan, N. Van de Weghe, D. Van den Poel, L. Pauwels, and W. Hardyns, “Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?,” ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 10, no. 6, 2021.
@article{8709947,
  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.}},
  articleno    = {{369}},
  author       = {{Rummens, Anneleen and Snaphaan, Thom and Van de Weghe, Nico and Van den Poel, Dirk and Pauwels, Lieven and Hardyns, Wim}},
  issn         = {{2220-9964}},
  journal      = {{ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION}},
  keywords     = {{ambient population,mobile phone data,crime rates,predictive policing,intelligence-led policing,AMBIENT POPULATION,SPATIAL-ANALYSIS,VIOLENT CRIME,MODEL,STREET,TRENDS,IMPACT,PLACES,RISK,LAW}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{19}},
  title        = {{Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?}},
  url          = {{http://doi.org/10.3390/ijgi10060369}},
  volume       = {{10}},
  year         = {{2021}},
}
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