Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2663204.2663254 | ICMI |
Keywords | DocType | Volume |
pattern recognition,mobile sensing,social and behavioral sciences,crime prediction,urban computing | Journal | abs/1409.2983 |
Citations | PageRank | References |
32 | 1.60 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrey Bogomolov | 1 | 75 | 5.60 |
| Bruno Lepri | 2 | 981 | 72.52 |
| Jacopo Staiano | 3 | 449 | 28.27 |
| Nuria Oliver | 4 | 4368 | 357.22 |
| Fabio Pianesi | 5 | 1109 | 88.84 |
| Alex Pentland | 6 | 18006 | 4853.13 |