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Learning to Detect Patterns of Crime

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8190)

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.

Keywords

  • Pattern detection
  • crime data mining
  • predictive policing

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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

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  • 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)