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Counterterrorism and Open Source Intelligence pp 73–102Cite as

Co-offending Network Mining

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Part of the book series: Lecture Notes in Social Networks ((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.

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Notes

  1. 1.

    Visual analytics is an emerging field using computers to analyze and visually convey massive amounts of data in a form that human experts can more readily understand.

  2. 2.

    The Institute for Canadian Urban Research Studies (ICURS) is a university research centre at Simon Fraser University.

  3. 3.

    Every crime data record in the crime data set refers to a different crime incident.

  4. 4.

    In implementing the analysis tasks, we used SNAP library which is publicly available at http://snap.stanford.edu/.

  5. 5.

    See also www.sfu.ca/viva/.

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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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Authors and Affiliations

  1. School of Criminology, Simon Fraser University, Vancouver, BC, Canada

    Patricia L. Brantingham & Richard Frank

  2. School of Computing Science, Simon Fraser University, Vancouver, BC, Canada

    Martin Ester, Uwe Glässer & Mohammad A. Tayebi

Authors
  1. Patricia L. Brantingham
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  2. Martin Ester
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  3. Richard Frank
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  4. Uwe Glässer
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  5. Mohammad A. Tayebi
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Corresponding author

Correspondence to Uwe Glässer .

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Editors and Affiliations

  1. The Maersk McKinney Moller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark

    Uffe Kock Wiil

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Brantingham, P.L., Ester, M., Frank, R., Glässer, U., Tayebi, M.A. (2011). Co-offending Network Mining. In: Wiil, U.K. (eds) Counterterrorism and Open Source Intelligence. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0388-3_6

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  • DOI: https://doi.org/10.1007/978-3-7091-0388-3_6

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  • Online ISBN: 978-3-7091-0388-3

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