Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information

Anton Borg () and Martin Boldt ()
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Anton Borg: Department of Computer Science and Engineering, Blekinge Institute of Technology, 371 79, Karlskrona, Sweden
Martin Boldt: Department of Computer Science and Engineering, Blekinge Institute of Technology, 371 79, Karlskrona, Sweden

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 01, 23-42

Abstract: 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.

Keywords: Crime clustering; residential burglary analysis; decision support system; combined distance metric (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622015500339

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