Learning to Detect Patterns of Crime
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-drive...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013
2013
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| Online Access: | http://hdl.handle.net/1721.1/79885 https://orcid.org/0000-0003-0517-3843 |
| _version_ | 1811083669445541888 |
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| author | Wang, Tong Rudin, Cynthia Wagner, Daniel Sevieri, Rich |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Wang, Tong Rudin, Cynthia Wagner, Daniel Sevieri, Rich |
| author_sort | Wang, Tong |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:37:10Z |
| format | Article |
| id | mit-1721.1/79885 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:37:10Z |
| publishDate | 2013 |
| publisher | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013 |
| record_format | dspace |
| spelling | mit-1721.1/798852022-09-28T09:00:40Z Learning to Detect Patterns of Crime Wang, Tong Rudin, Cynthia Wagner, Daniel Sevieri, Rich Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Sloan School of Management Wang, Tom Rudin, Cynthia 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. Lincoln Laboratory National Science Foundation (U.S.) (CAREER IIS-1053407) 2013-08-21T14:29:27Z 2013-08-21T14:29:27Z 2013-08-21 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/79885 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. https://orcid.org/0000-0003-0517-3843 en_US http://www.ecmlpkdd2013.org/accepted-papers/ Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013 Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013 MIT web domain |
| spellingShingle | Wang, Tong Rudin, Cynthia Wagner, Daniel Sevieri, Rich Learning to Detect Patterns of Crime |
| title | Learning to Detect Patterns of Crime |
| title_full | Learning to Detect Patterns of Crime |
| title_fullStr | Learning to Detect Patterns of Crime |
| title_full_unstemmed | Learning to Detect Patterns of Crime |
| title_short | Learning to Detect Patterns of Crime |
| title_sort | learning to detect patterns of crime |
| url | http://hdl.handle.net/1721.1/79885 https://orcid.org/0000-0003-0517-3843 |
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