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Towards a Burglary Risk Profiler Using Demographic and Spatial Factors

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

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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.

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Notes

  1. 1.

    A discrete random variable Y is said to have a Poisson distribution with parameter \(E(Y) = Var(Y) = \mu > 0\), if, for \(k = 0, 1, 2, \), the probability mass function of Y is given by: \(P(Y=k) = \frac{{\mu }^{k}e^{-\mu }}{k!}\).

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Acknowledgments

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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Authors and Affiliations

  1. Information Management Chair, D-MTEC, ETH Zurich, Zurich, Switzerland

    Cristina Kadar, Grammatiki Zanni, Thijs Vogels & Irena Pletikosa Cvijikj

Authors
  1. Cristina Kadar
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  2. Grammatiki Zanni
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  3. Thijs Vogels
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  4. Irena Pletikosa Cvijikj
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Corresponding author

Correspondence to Cristina Kadar .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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Kadar, C., Zanni, G., Vogels, T., Cvijikj, I.P. (2015). Towards a Burglary Risk Profiler Using Demographic and Spatial Factors. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_39

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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