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Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-8929-7220
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
2016 (engelsk)Inngår i: International Journal of Information Technology and Decision Making, ISSN 0219-6220, Vol. 15, nr 1, s. 23-42Artikkel i tidsskrift (Fagfellevurdert) Published
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Text
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.

sted, utgiver, år, opplag, sider
World Scientific, 2016. Vol. 15, nr 1, s. 23-42
Emneord [en]
Crime clustering, residential burglary analysis, decision support system, combined distance metric
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-11779DOI: 10.1142/S0219622015500339ISI: 000371127600003OAI: oai:DiVA.org:bth-11779DiVA, id: diva2:916302
Tilgjengelig fra: 2016-04-01 Laget: 2016-04-01 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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