Name Crime Prediction from Demographics and Mobile Data
Source Università degli Studi di Trento
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Description This paper presents 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, the 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 the researchers experimental results with real crime data from London they obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, they provide a discussion of the implications of their findings for data-driven crime analysis.
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Lead Institution Università degli Studi di Trento
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Partners Telefonica I+D, Massachusetts Institute of Technology
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Type Research
Potential for reuse High
Open source N/A
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Plug & Play N/A
Outputs compatible with indicators from Official Statistics N/A
Status Pilot
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Statistical Institute
Other Government Agencies
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  • Areas of management practice innovations
    Use of Alternative Data Sources
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    Created On 2014-04-12
    Updated On 2014-04-12
    Status Published
    Develop capability improvements
    Manage consumers