Towards a Burglary Risk Profiler Using Demographic and Spatial Factors

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9418)


According to modern crime victimization theories, the offender, the victim, and the spatial environment equally affect the likelihood of a crime getting committed, especially in the case of burglaries. With this in mind, we compile an extensive list of potential drivers of burglary by aggregating data from different open data sources, such as census statistics (social, demographic, and economic data), points of interest, and the national road network. Based on the underlying data distribution, we build statistical models that automatically select the risk factors affecting the burglary numbers in the Swiss municipalities and predict the level of future crimes. The gained information is integrated in a crime prevention information system providing its users a view of the current crime exposure in their neighborhood.


Social issues Open data Risk factors Data mining Crime prevention information systems 



The authors would like to acknowledge the data contract Nr. 140221 (Typ B) with Ref. 650.1-1 from September 2014 for the delivery of the confidential police criminal statistics.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Information Management Chair, D-MTECETH ZurichZurichSwitzerland

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