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.
Date issued
2013-08-21Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Sloan School of ManagementJournal
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
Publisher
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013
Citation
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.
Version: Author's final manuscript