Learning to Detect Patterns of Crime
Author(s)Wang, Tong; Rudin, Cynthia; Wagner, Daniel; Sevieri, Rich
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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 speci c 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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Sloan School of Management
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013
Wang, Tong, Cynthia Rudin, Dan Wagner, and Rich Sevieri. "Learning to Detect Patterns of Crime." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013, Prague, 23-27 September 2013.
Author's final manuscript