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Mining Positive Associations of Urban Criminal Activities Using Hierarchical Crime Hot Spots

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Intelligence and Security Informatics (WISI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3917))

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Abstract

We present a framework for discovering positive associations in urban crime datasets using hierarchical clustering and an association test based on a hybrid minimum bounding circle and average bounding circle approach. We justify the virtue of our framework by comparing its computational speed and quality of associations using real crime datasets.

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Author information

Authors and Affiliations

  1. School of Information Technology, James Cook University, QLD 4811, Australia

    Peter Phillips & Ickjai Lee

Authors
  1. Peter Phillips
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  2. Ickjai Lee
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Editor information

Editors and Affiliations

  1. Department of Management Information Systems, Eller College of Management, The University of Arizona, 85721, AZ, USA

    Hsinchun Chen

  2. Chinese Academy of Sciences, 100190, Beijing, China

    Fei-Yue Wang

  3. Drexel University, 19104, Philadelphia, PA, USA

    Christopher C. Yang

  4. MIS Department, University of Arizona, and Chinese Academy of Sciences, Tucson, AZ, USA

    Daniel Zeng

  5. The University of Hong Kong, Hong Kong, China

    Michael Chau

  6. School of Computer Engineering, Nanyang Technological University, 639798, Singapore

    Kuiyu Chang

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© 2006 Springer-Verlag Berlin Heidelberg

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Phillips, P., Lee, I. (2006). Mining Positive Associations of Urban Criminal Activities Using Hierarchical Crime Hot Spots. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_15

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  • DOI: https://doi.org/10.1007/11734628_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33361-6

  • Online ISBN: 978-3-540-33362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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