Abstract
Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ones may have been committed by the same individual(s). If automated, data-driven tools for crime pattern detection are made available to assist analysts, these tools could help police to better understand patterns of crime, leading to more precise attribution of past crimes, and the apprehension of suspects. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a “seed” of a few crimes. Series Finder incorporates both the common characteristics of all patterns and the unique aspects of each specific pattern, and has had promising results on a decade’s worth of crime pattern data collected by the Crime Analysis Unit of the Cambridge Police Department.
Chapter PDF
Similar content being viewed by others
References
Berk, R., Sherman, L., Barnes, G., Kurtz, E., Ahlman, L.: Forecasting murder within a population of probationers and parolees: a high stakes application of statistical learning. Journal of the Royal Statistical Society: Series A (Statistics in Society) 172(1), 191–211 (2009)
Pearsall, B.: Predictive policing: The future of law enforcement? National Institute of Justice Journal 266, 16–19 (2010)
Gwinn, S.L., Bruce, C., Cooper, J.P., Hick, S.: Exploring crime analysis. Readings on essential skills, 2nd edn. BookSurge, LLC (2008)
Ratcliffe, J.H., Rengert, G.F.: Near-repeat patterns in Philadelphia shootings. Security Journal 21(1), 58–76 (2008)
Dahbur, K., Muscarello, T.: Classification system for serial criminal patterns. Artificial Intelligence and Law 11(4), 251–269 (2003)
Nath, S.V.: Crime pattern detection using data mining. In: Proceedings of Web Intelligence and Intelligent Agent Technology Workshops, pp. 41–44 (2006)
Brown, D.E., Hagen, S.: Data association methods with applications to law enforcement. Decision Support Systems 34(4), 369–378 (2003)
Lin, S., Brown, D.E.: An outlier-based data association method for linking criminal incidents. In: Proceedings of the Third SIAM International Conference on Data Mining. (2003)
Ng, V., Chan, S., Lau, D., Ying, C.M.: Incremental mining for temporal association rules for crime pattern discoveries. In: Proceedings of the 18th Australasian Database Conference, vol. 63, pp. 123–132 (2007)
Buczak, A.L., Gifford, C.M.: Fuzzy association rule mining for community crime pattern discovery. In: ACM SIGKDD Workshop on Intelligence and Security Informatics (2010)
Wang, G., Chen, H., Atabakhsh, H.: Automatically detecting deceptive criminal identities. Communications of the ACM 47(3), 70–76 (2004)
Chen, H., Chung, W., Xu, J., Wang, G., Qin, Y., Chau, M.: Crime data mining: a general framework and some examples. Computer 37(4), 50–56 (2004)
Hauck, R.V., Atabakhsb, H., Ongvasith, P., Gupta, H., Chen, H.: Using COPLINK to analyze criminal-justice data. Computer 35(3), 30–37 (2002)
Short, M.B., D’Orsogna, M.R., Pasour, V.B., Tita, G.E., Brantingham, P.J., Bertozzi, A.L., Chayes, L.B.: A statistical model of criminal behavior. Mathematical Models and Methods in Applied Sciences 18, 1249–1267 (2008)
Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. Journal of the American Statistical Association 106(493) (2011)
Short, M.B., D’Orsogna, M., Brantingham, P., Tita, G.: Measuring and modeling repeat and near-repeat burglary effects. Journal of Quantitative Criminology 25(3), 325–339 (2009)
Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: Understanding hotspots. Technical report, National Institute of Justice, NIJ Special Report (August 2005)
Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: International Conference on Machine Learning, pp. 19–26 (2002)
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Int’l Conf. on Machine Learning, pp. 577–584 (2001)
Ghahramani, Z., Heller, K.: Bayesian sets. In: Proceedings of Neural Information Processing Systems (2005)
Letham, B., Rudin, C., Heller, K.: Growing a list. Data Mining and Knowledge Discovery (to appear, 2013)
Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: A comparative evaluation. In: Proceedings of the Eighth SIAM International Conference on Data Mining, pp. 243–254 (2008)
Criminal Justice Policy Research Institute: Residential burglary in Portland, Oregon. Hatfield School of Government, Criminal Justice Policy Research Institute, http://www.pdx.edu/cjpri/time-of-dayday-of-week-0
Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. Springer (2005)
National Law Enforcement and Corrections Technology Center: ‘Calculate’ repeat crime. TechBeat (Fall 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, T., Rudin, C., Wagner, D., Sevieri, R. (2013). Learning to Detect Patterns of Crime. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40994-3_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40993-6
Online ISBN: 978-3-642-40994-3
eBook Packages: Computer ScienceComputer Science (R0)