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Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information

Author

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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)

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.

Suggested Citation

  • Anton Borg & Martin Boldt, 2016. "Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 23-42, January.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:01:n:s0219622015500339
    DOI: 10.1142/S0219622015500339
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    References listed on IDEAS

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    1. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    2. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
    3. Brian J. Reich & Michael D. Porter, 2015. "Partially supervised spatiotemporal clustering for burglary crime series identification," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 465-480, February.
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    Cited by:

    1. Giyasettin Ozcan, 2018. "Unsupervised Learning from Multi-Dimensional Data: A Fast Clustering Algorithm Utilizing Canopies and Statistical Information," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 841-856, May.

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