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Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information
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.
2016 (English)In: International Journal of Information Technology and Decision Making, ISSN 0219-6220, Vol. 15, no 1, 23-42 p.Article in journal (Refereed) Published
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Abstract [en]

To identify series of residential burglaries, detecting linked crimes performed by the same constellations of criminals is necessary. Comparison of crime reports today is difficult as crime reports traditionally have been written as unstructured text and often lack a common information-basis. Based on a novel process for collecting structured crime scene information, the present study investigates the use of clustering algorithms to group similar crime reports based on combined crime characteristics from the structured form. Clustering quality is measured using Connectivity and Silhouette index (SI), stability using Jaccard index, and accuracy is measured using Rand index (RI) and a Series Rand index (SRI). The performance of clustering using combined characteristics was compared with spatial characteristic. The results suggest that the combined characteristics perform better or similar to the spatial characteristic. In terms of practical significance, the presented clustering approach is capable of clustering cases using a broader decision basis.

Place, publisher, year, edition, pages
World Scientific, 2016. Vol. 15, no 1, 23-42 p.
Keyword [en]
Crime clustering, residential burglary analysis, decision support system, combined distance metric
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-11779DOI: 10.1142/S0219622015500339ISI: 000371127600003OAI: oai:DiVA.org:bth-11779DiVA: diva2:916302
Available from: 2016-04-01 Created: 2016-04-01 Last updated: 2017-02-22Bibliographically approved

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