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Detecting serial residential burglaries using clustering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-8929-7220
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
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2014 (English)In: Expert Systems with Applications, ISSN 0957-4174 , Vol. 41, no 11, 5252-5266 p.Article in journal (Refereed) Published
Description
Abstract [en]

According to the Swedish National Council for Crime Prevention, law enforcement agencies solved approximately three to five percent of the reported residential burglaries in 2012. Internationally, studies suggest that a large proportion of crimes are committed by a minority of offenders. Law enforcement agencies, consequently, are required to detect series of crimes, or linked crimes. Comparison of crime reports today is difficult as no systematic or structured way of reporting crimes exists, and no ability to search multiple crime reports exist. This study presents a systematic data collection method for residential burglaries. A decision support system for comparing and analysing residential burglaries is also presented. The decision support system consists of an advanced search tool and a plugin-based analytical framework. In order to find similar crimes, law enforcement officers have to review a large amount of crimes. The potential use of the cut-clustering algorithm to group crimes to reduce the amount of crimes to review for residential burglary analysis based on characteristics is investigated. The characteristics used are modus operandi, residential characteristics, stolen goods, spatial similarity, or temporal similarity. Clustering quality is measured using the modularity index and accuracy is measured using the rand index. The clustering solution with the best quality performance score were residential characteristics, spatial proximity, and modus operandi, suggesting that the choice of which characteristic to use when grouping crimes can positively affect the end result. The results suggest that a high quality clustering solution performs significantly better than a random guesser. In terms of practical significance, the presented clustering approach is capable of reduce the amounts of cases to review while keeping most connected cases. While the approach might miss some connections, it is also capable of suggesting new connections. The results also suggest that while crime series clustering is feasible, further investigation is needed.

Place, publisher, year, edition, pages
Elsevier , 2014. Vol. 41, no 11, 5252-5266 p.
Keyword [en]
BigData@BTH, Cut clustering, Residential burglary analysis, Crime clustering, Decision support systems
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-6673DOI: 10.1016/j.eswa.2014.02.035ISI: 000336191800022Local ID: oai:bth.se:forskinfoF76EC18DE9BD633BC1257CFC002E0642OAI: oai:DiVA.org:bth-6673DiVA: diva2:834197
Note
http://www.sciencedirect.com/science/article/pii/S0957417414001110Available from: 2014-07-17 Created: 2014-06-19 Last updated: 2017-02-22Bibliographically approved

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Borg, AntonBoldt, MartinLavesson, Niklas
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CiteExportLink to record
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