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

By Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Fabio Pianesi, Alex Pentland. International Conference on Multimodal Interaction, 2014

319 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Sociology and Political Science, Transportation

View Paper: https://www.semanticscholar.org/paper/462065d8e148499ec957ee5682ede1d710f32a68

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Citations in the corpus

The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, methodology): “Recent work has used this type of data to predict crime hotspots through machine-learning algorithms [10, 11, 85].”
A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015, background): “…mm/hr; ● heavy : precipitation intensity de ﬁ ned [10,12] meaning an amount of [16.0,30.0] mm/hr; ● very heavy : precipitation intensity de ﬁ ned [13,15] meaning an amount of [30.0,70.0] mm/hr; ● extreme : precipitation intensity de ﬁ ned [16,18] meaning an amount of more than 70 mm/hr; The…”