Learning to detect patterns of crime

Published

Conference Paper

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. © 2013 Springer-Verlag.

Full Text

Duke Authors

Cited Authors

  • Wang, T; Rudin, C; Wagner, D; Sevieri, R

Published Date

  • October 31, 2013

Published In

Volume / Issue

  • 8190 LNAI / PART 3

Start / End Page

  • 515 - 530

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

International Standard Book Number 13 (ISBN-13)

  • 9783642409936

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-40994-3_33

Citation Source

  • Scopus