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Co-offending Network Mining

Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

We propose here a computational framework for co-offending network mining defined in terms of a process that combines formal data modeling with data mining of large crime and terrorism data sets as gathered and maintained by law enforcement and intelligence agencies. Our crime data analysis aims at exploring relevant properties of criminal networks in arrest-data and is based on 5 years of real-world crime data that was made available for research purposes. This data was retrieved from a large database system with several million data records keeping information for the regions of the Province of British Columbia. Beyond application of innovative data mining techniques for the analysis of the crime data set, we also provide a comprehensive data model applicable to any such data set and link the data model to the analysis techniques. We contend that central aspects considered in the work presented here carry over to a wide range of large data sets studied in intelligence and security informatics to better serve law enforcement and intelligence agencies.

Keywords

Cluster Coefficient Property Crime Link Prediction Crime Type Eigenvector Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We are thankful to RCMP “E” Division and BC Ministry for Public Safety and Solicitor General for making this research possible by providing Simon Fraser University with crime data from their Police Information Retrieval System. We also like to thank the anonymous reviewer(s) for their constructive criticism and helpful comments on an earlier version of our manuscript for this chapter.

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Copyright information

© Springer-Verlag/Wien 2011

Authors and Affiliations

  1. 1.School of CriminologySimon Fraser UniversityVancouverCanada
  2. 2.School of Computing ScienceSimon Fraser UniversityVancouverCanada

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