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A Study on the Applicability of Data Mining for Crime Prediction : Focusing on Burglary
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 Title & Authors
A Study on the Applicability of Data Mining for Crime Prediction : Focusing on Burglary
Bang, Seung-Hwan; Kim, Tae-Hun; Cho, Hyun-Bo;
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 Abstract
Recently, crime prediction and prevention are the most important social issues, and global and local governments have tried to prevent crime using various methodologies. One of the methodologies, data mining can be applied at various crime fields such as crime pattern analysis, crime prediction, etc. However, there is few researches to find the relationships between the results of data mining and crime components in terms of criminology. In this study, we introduced environmental criminology, and identified relationships between environment factors related with crime and variables using at data mining. Then, using real burglary data occurred in South Korea, we applied clustering to show relations of results of data mining and crime environment factors. As a result, there were differences in the crime environment caused by each cluster. Finally, we showed the meaning of data mining use at crime prediction and prevention area in terms of criminology.
 Keywords
Crime prediction;Data mining;Criminology;Crime environment;
 Language
Korean
 Cited by
 References
1.
C. Shu, A. Hampapur, M. Lu, L. Brown, J. Connell, A. Senior, and Y. Tian, "IBM Smart Surveillance System(S3): A Open and Extensible Framework for Event based Surveillance," IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 318-323, Como, Italy, September 2005.

2.
S. Chainey, L. Tompson, and S. Uhlig, "The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime," Security Journal, Vol. 21, pp. 4-28, February 2008. crossref(new window)

3.
D. Brown, and R. Oxford, "Data Mining Time Series with Applications to Crime Analysis," IEEE International Conference on Systems, Man, and Cybernetics, Vol. 3, pp. 1453-1459, Tucson, USA, October 2001.

4.
S. Nath, "Advances and Innovation in Systems, Computing Science and Software Engineering," Springer, pp. 405-409, 2007.

5.
H. Chen, W. Chung, J. Xu, G. Wang, Y. Qin, and M. Chau, "Crime Data Mining: A General Framework and Some Examples," Computer, Vol. 37, No. 4, pp. 50-56, April 2004.

6.
V. Grover, R. Adderley, and M. Bramer, "Applications and Innovations in Intelligent Systems XIV," Springer London, pp. 233-237, 2007.

7.
M. Keyvanpour, M. Javideh, and M. Ebrahimi, "Detecting and Investigating Crime by Means of Data Mining: A General Crime Matching Framework," Procedia Computer Science, Vol. 3, pp. 872-880, February 2011. crossref(new window)

8.
P. Thongatae, and S. Srisuk, "An Analysis of Data Mining Application in Crime Domain," IEEE International Conference on Computer and Information Technology Workshops, pp. 122-126, Sydney, Australia, July 2008.

9.
L. Cohen, and M. Felson, "Social Change and Crime Rate Trends: A Route Activity Approach," American Sociological Review, Vol. 44, No. 4, pp. 588-608, August 1979. crossref(new window)

10.
M. Felson, and R. Clarke, "Opportunity Makes the Thief: Practical Theory for Crime Prevention," Police Research Series Paper 98, Home Office, pp. 4-8, 1998.

11.
P. Brantingham, and P. Brantingham, "Environmental Criminology," Wavelend Press Inc, pp. 27-54, 1991.

12.
A. Buczak, and C. Gifford, "Fuzzy Association Rule Mining for Community Crime Pattern Discovery," ACM SIGKDD Workshops on Intelligence and Security Informatics Article, No. 2, New York, USA, July 2010.

13.
D. Dzemydiene, and V. Rudzkiene, "Multiple Regression Analysis in Crime Pattern Warehouse for Decision Support," Proceedings of the 13th International Conference on Database and Expert Systems Applications, pp. 249-258, Aix-en-Provence, France, September 2002.

14.
V. Ng, S. Chan, D. Lau, and C. Ying, "Incremental Mining for Temporal Association Rules for Crime Pattern Discoveries," Proceedings of the Eighteenth Conference on Australasian database, Vol. 63, pp. 123-132, Darlinghurst, Australia, March 2007.

15.
B. R. Mednick, R. L. Baker, and L. E. Carothers, "Patterns of Family Instability and Crime: The Association of Timing of the Family's Disruption with Subsequent Adolescent and Young Adult Criminality", Journal of Youth and Adolescence, Vol. 19, No. 3, June 1990.

16.
H. Liu and D. E. Brown, "Criminal Incident Prediction Using a Point-pattern-based Density Model", International Journal of Forecasting, Vol. 19, No. 4, pp. 603-622, December 2003. crossref(new window)

17.
T. Nakaya, and K. Yano, "Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics," Transaction in GIS, Vol. 14, No. 3, pp. 223-239, June 2010. crossref(new window)

18.
B. Chandra, M. Gupta, and M. Gupta, "A Multivariate Time Series Clustering Approach for Crime Trends Prediction," IEEE International Conference on Systems, Man and Cybernetics, pp. 892-896, Singapore, October 2008.

19.
M. Carglia, R. Haining, and P. Wiles, "A Comparative Evaluation of Approaches to Urban Crime Pattern Analysis", Urban Studies, Vol. 37, No. 4, pp. 711-729, April 2000. crossref(new window)

20.
R. Jamieson, L. Land, D. Winchester, G. Stephens, A. Steel, A. Maurushat, and R. Sarre, "Addressing Identity Crime in Crime Management Information Systems: Definitions, classification, and empirics," Computer Law & Security Review, Vol. 28, No. 4, pp. 381-395, August 2012. crossref(new window)

21.
A. Nasridinov, S. Ihm, and Y. Park, "Information Technology Convergence," Springer Netherlands, Vol. 253, pp. 531-538, 2013.

22.
D. M. Gottfredson, "Prediction and Classification in Criminal Justice Decision Making", Crime and Justice, Vol. 9, pp. 1-20, 1987. crossref(new window)

23.
J. E. Douglas, R. K. Ressler, and C. R. Hartman, "Criminal Profiling from Crime Scene Analysis", Behavioral Science and the Law, Vol. 4, No. 4, pp. 401-421, Autumn 1986. crossref(new window)