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Criminal Cross Correlation Mining and Visualization

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

Abstract

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.

Keywords

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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References

  1. 1.
    Chen, H., Atabakhsh, H., Zeng, D., Schroeder, J., Petersen, T., Casey, D., Chen, M., Xiang, Y., Daspit, D., Nandiraju, S., Fu, S.: Coplink: visualization and collaboration for law enforcement. In: Proceedings of the 2002 annual national conference on Digital government research, pp. 1–7 (2002)Google Scholar
  2. 2.
    Craglia, M., Haining, R., Wiles, P.: A Comparative Evaluation of Approaches to Urban Crime Pattern Analysis. Urban Studies 37(4), 711–729 (2000)CrossRefGoogle Scholar
  3. 3.
    Hirschfield, A., Brown, P., Todd, P.: GIS and the Analysis of Spatially-Referenced Crime Data: Experiences in Merseyside. U. K. Journal of Geographical Information Systems 9(2), 191–210 (1995)CrossRefGoogle Scholar
  4. 4.
    Ratcliffe, J.: The Hotspot Matrix: A Framework for the Spatio-temporal Targeting of Crime Reduction. Police Practice and Research 5, 5–23 (2004)CrossRefGoogle Scholar
  5. 5.
    Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: Crime Data Mining: A General Framework and Some Examples. Computer 37(4), 50–56 (2004)CrossRefGoogle Scholar
  6. 6.
    Oatley, G., Ewart, B., Zeleznikow, J.: Decision Support Systems for Police: Lessons from the Application of Data Mining Techniques to Soft Forensic Evidence. Artificial Intelligence and Law 14(1), 35–100 (2006)Google Scholar
  7. 7.
    Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Proceedings of the 4th International Symposium on Large Spatial Databases, Portland, Maine. LNCS, pp. 47–66. Springer, Heidelberg (1995)Google Scholar
  8. 8.
    Lee, I., Phillips, P.: Urban crime analysis through areal categorized multivariate associations mining. Applied Artificial Intelligence 22(5), 483–499 (2008)CrossRefGoogle Scholar
  9. 9.
    Shekhar, S., Huang, Y.: Discovering Spatial Co-location Patterns: A Summary of Results. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 236–256. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Dray, S., Saïd, S., Débias, F.: Spatial ordination of vegetation data using a generalization of Wartenberg’s multivariate spatial correlation. Journal of Vegetation Science 19, 45–56 (2008)CrossRefGoogle Scholar
  11. 11.
    Wartenberg, D.: Multivariate spatial correlation: A method for exploratory geographical analysis. Geographical Analysis 17, 263–283 (1985)CrossRefGoogle Scholar
  12. 12.
    Lee, S.: Developing a bivariate spatial association measure: An integration of Pearson’s r and Moran’s I. Journal of Geographical Systems 3(4), 369–385 (2001)CrossRefGoogle Scholar
  13. 13.
    Morrison, D.F.: Multivariate Statistical Methods, 2nd edn. McGraw-Hill, New York (1976)zbMATHGoogle Scholar
  14. 14.
    Hubert, L.J., Golledge, R.G., Costanzo, C.M., Gale, N.: Measuring association between spatially defined variables: an alternative procedure. Geographical Analysis 17, 36–46 (1985)CrossRefGoogle Scholar
  15. 15.
    Tiefelsdorf, M., Griffith, D.A., Boots, B.: A variance-stabilizing coding scheme for spatial link matrices. Environment and Planning A 31(1), 165–180 (1999)CrossRefGoogle Scholar
  16. 16.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in Large Databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the ACM SIGMOD 1993 International Conference on Management of Data, pp. 207–216. ACM Press, Washington (1993)CrossRefGoogle Scholar
  17. 17.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)zbMATHGoogle Scholar
  18. 18.
    Australian Institute of Criminology: Comparing International Trends in Recorded Violent Crime. In: Crime Facts Info No. 115 (2006), http://www.aic.gov.au/publications/cfi/cfi115.html
  19. 19.
    Murray, A.T., McGuffog, I., Western, J.S., Mullins, P.: Exploratory Spatial Data Analysis Techniques for Examining Urban Crime. British Journal of Criminology 41, 309–329 (2001)CrossRefGoogle Scholar
  20. 20.
    Australian Bureau of Statistics: Australian Standard Geographical Classification (ASGC) (2005)Google Scholar
  21. 21.
    Phillips, P., Lee, I.: Areal Aggregated Crime Reasoning through Density Tracing. In: International Workshop on Spatial and Spatio-temporal Data Mining in conjunction with IEEE International Conference on Data Mining, Omaha, NE, USA (October 2007)Google Scholar

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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