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Clustering residential burglaries using multiple heterogeneous variables
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-9316-4842
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-8929-7220
(English)In: International Journal of Information Technology & Decision MakingArticle in journal (Refereed) Accepted
Abstract [en]

To identify series of residential burglaries, detecting linked crimes performed bythe same constellations of criminals is necessary. Comparison of crime reports today isdicult as crime reports traditionally have been written as unstructured text and oftenlack a common information-basis. Based on a novel process for collecting structured crimescene information the present study investigates the use of clustering algorithms to groupsimilar crime reports based on combined crime characteristics from the structured form.Clustering quality is measured using Connectivity and Silhouette index, stability usingJaccard index, and accuracy is measured using Rand index and a Series Rand index.The performance of clustering using combined characteristics was compared with spatialcharacteristic. The results suggest that the combined characteristics perform better orsimilar to the spatial characteristic. In terms of practical signicance, the presentedclustering approach is capable of clustering cases using a broader decision basis.

Keyword [en]
Crime Clustering; Residential Burglary Analysis; Decision Support System; Combined Distance Metric
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-10807OAI: oai:DiVA.org:bth-10807DiVA: diva2:861200
Available from: 2015-10-15 Created: 2015-10-15 Last updated: 2017-02-22Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Output format
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