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- W2401250972 abstract "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." @default.
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- W2401250972 date "2015-01-01" @default.
- W2401250972 modified "2023-09-25" @default.
- W2401250972 title "Towards a Burglary Risk Profiler Using Demographic and Spatial Factors" @default.
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- W2401250972 doi "https://doi.org/10.1007/978-3-319-26190-4_39" @default.
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