Criminal Cross Correlation Mining and Visualization

  • Peter Phillips
  • Ickjai Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5477)


Criminals are creatures of habit and their crime activities are geospatially, temporally and thematically correlated. Discovering these correlations is a core component of intelligence-led policing and allows for a deeper insight into the complex nature of criminal behavior. A spatial bivariate correlation measure should be used to discover these patterns from heterogeneous data types. We introduce a bivariate spatial correlation approach for crime analysis that can be extended to extract multivariate cross correlations. It is able to extract the top-k and bottom-k associative features from areal aggregated datasets and visualize the resulting patterns. We demonstrate our approach with real crime datasets and provide a comparison with other techniques. Experimental results reveal the applicability and usefulness of the proposed approach.


Spatial Association Spatial Weight Matrix Crime Analysis Collection District Density Trace 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Phillips
    • 1
  • Ickjai Lee
    • 1
  1. 1.Discipline of ITJames Cook UniversityAustralia

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