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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Craglia, M., Haining, R., Wiles, P.: A Comparative Evaluation of Approaches to Urban Crime Pattern Analysis. Urban Studies 37(4), 711–729 (2000)
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)
Ratcliffe, J.: The Hotspot Matrix: A Framework for the Spatio-temporal Targeting of Crime Reduction. Police Practice and Research 5, 5–23 (2004)
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)
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)
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)
Lee, I., Phillips, P.: Urban crime analysis through areal categorized multivariate associations mining. Applied Artificial Intelligence 22(5), 483–499 (2008)
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)
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)
Wartenberg, D.: Multivariate spatial correlation: A method for exploratory geographical analysis. Geographical Analysis 17, 263–283 (1985)
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)
Morrison, D.F.: Multivariate Statistical Methods, 2nd edn. McGraw-Hill, New York (1976)
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)
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)
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)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
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
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)
Australian Bureau of Statistics: Australian Standard Geographical Classification (ASGC) (2005)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Phillips, P., Lee, I. (2009). Criminal Cross Correlation Mining and Visualization. In: Chen, H., Yang, C.C., Chau, M., Li, SH. (eds) Intelligence and Security Informatics. PAISI 2009. Lecture Notes in Computer Science, vol 5477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01393-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-01393-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01392-8
Online ISBN: 978-3-642-01393-5
eBook Packages: Computer ScienceComputer Science (R0)