Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
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Pentland_Once upon a crime.pdf
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2.56 MB
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Adobe PDF
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Author(s)
Bogomolov, Andrey
Lepri, Bruno
Staiano, Jacopo
Oliver, Nuria
Pianesi, Fabio
Pentland, Alex Paul
Date Issued
November 2014
Journal
Proceedings of the 16th International Conference on Multimodal Interaction - ICMI '14
Publisher
Association for Computing Machinery (ACM)
Citation
Bogomolov, Andrey, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Fabio Pianesi, and Alex Pentland. “Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data.” Proceedings of the 16th International Conference on Multimodal Interaction - ICMI ’14, November 12-16, 2014, Istanbul, Turkey. pp. 427-434.
Version
Original manuscript
Abstract
In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2663204.2663254
