Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data

TL;DR

This paper introduces a novel method for crime prediction using aggregated mobile phone and demographic data, achieving nearly 70% accuracy in identifying crime hotspots in London.

Contribution

It demonstrates that combined mobile behavioral and demographic data can effectively predict crime hotspots, advancing beyond traditional profiling methods.

Findings

Achieved 70% accuracy in crime hotspot prediction.

Validated approach with real London crime data.

Discussed implications for data-driven crime prevention.

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.