Learning to Detect Patterns of Crime
Name
Rudin_Learning to.pdf
Size
755.76 KB
Format
Adobe PDF
Checksum (MD5)
778b73e97812bc2c2ea1ef63962450ef
Author(s)
Wang, Tong
Rudin, Cynthia
Wagner, Daniel
Sevieri, Rich
Date Issued
August 21, 2013
Journal
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
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 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.ecmlpkdd2013.org/accepted-papers/
