  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.
