Abstract
Criminal activities are unevenly distributed over space. The concept of hotspots is widely used to analyze the spatial characters of crimes. But existing methods usually identify hotspots based on an arbitrary user-defined threshold with respect to the number of a target crime without considering underlying controlling factors. In this study we introduce a new data mining model – Hotspots Optimization Tool (HOT) – to identify and optimize crime hotspots. The key component of HOT, Geospatial Discriminative Patterns (GDPatterns), which capture the difference between two classes in spatial dataset, is used in crime hotspot analysis. Using a real world dataset of a northeastern city in the United States, we demonstrate that the HOT model is a useful tool in optimizing crime hotspots,and it is also capable of visualizing criminal controlling factors which will help domain scientists further understanding the underlying reasons of criminal activities.
This is a preview of subscription content, log in via an institution to check access.
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
Bailey, T.C., Gatrell, A.C.: Interactive spatial data analysis. Longman Scientific & Technical Essex (1995)
Bates, S.: Spatial and temporal analysis of crime. Research Bulletin (April 1987)
Chainey, S., Ratcliffe, J.: GIS and crime mapping, vol. 6. John Wiley & Sons Inc. (2005)
Chainey, S., Tompson, L., Uhlig, S.: The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal 21(1), 4–28 (2008)
Cohen, L.E., Felson, M.: Social change and crime rate trends: A routine activity approach. American Sociological Review, 588–608 (1979)
Cook, W., Ormerod, P., Cooper, E.: Scaling behaviour in the number of criminal acts committed by individuals. Journal of Statistical Mechanics: Theory and Experiment 2004, 07003 (2004)
Ding, W., Stepinski, T.F., Salazar, J.: Discovery of geospatial discriminating patterns from remote sensing datasets. In: Proceedings of SIAM (2009)
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of the 5th ACM SIGKDD, pp. 43–52. ACM (1999)
Eck, J.E., Chainey, S., Cameron, J.G., Leitner, M., Wilson, R.E.: Mapping crime: Understanding hot spots (2005)
Gladwell, M.: The tipping point: How little things can make a big difference. Little, Brown and Company (2000)
Harries, K.D.: Mapping crime: Principle and practice. US Dept. of Justice, Office of Justice Programs, Crime Mapping Research Center (1999)
Hirschfield, A.: Mapping and Analysing Crime Data: Lessons from research and practice. CRC (2001)
Jenks, G.F.: The data model concept in statistical mapping. International Yearbook of Cartography 7, 186–190 (1967)
Li, J., Liu, G., Wong, L.: Mining statistically important equivalence classes and delta-discriminative emerging patterns. In: Proceedings of the 13th ACM SIGKDD, pp. 430–439. ACM (2007)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering Frequent Closed Itemsets for Association Rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Short, M.B., Bertozzi, A.L., Brantingham, P.J.: Nonlinear patterns in urban crime: Hotspots, bifurcations, and suppression. Journal on Applied Dynamical Systems 9, 462 (2010)
Skogan, W.G.: Disorder and decline: Crime and the spiral of decay in American neighborhoods. Univ. of California Pr. (1992)
Wand, M.P., Jones, M.C.: Kernel smoothing, vol. 60. Chapman & Hall/CRC (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, D., Ding, W., Stepinski, T., Salazar, J., Lo, H., Morabito, M. (2012). Optimization of Criminal HotSpots Based on Underlying Crime Controlling Factors Using Geospatial Discriminative Pattern. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-31087-4_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31086-7
Online ISBN: 978-3-642-31087-4
eBook Packages: Computer ScienceComputer Science (R0)