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Predicting burglars' risk exposure and level of pre-crime preparation using crime scene data
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
Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
Polisen, SWE.
2018 (English)In: Intelligent Data Analysis, ISSN 1088-467X, Vol. 22, no 1, IDA 322-3210Article in journal (Refereed) In press
Abstract [en]

Objectives: The present study aims to extend current research on how offenders’ modus operandi (MO) can be used in crime linkage, by investigating the possibility to automatically estimate offenders’ risk exposure and level of pre-crime preparation for residential burglaries. Such estimations can assist law enforcement agencies when linking crimes into series and thus provide a more comprehensive understanding of offenders and targets, based on the combined knowledge and evidence collected from different crime scenes. Methods: Two criminal profilers manually rated offenders’ risk exposure and level of pre-crime preparation for 50 burglaries each. In an experiment we then analyzed to what extent 16 machine-learning algorithms could generalize both offenders’ risk exposure and preparation scores from the criminal profilers’ ratings onto 15,598 residential burglaries. All included burglaries contain structured and feature-rich crime descriptions which learning algorithms can use to generalize offenders’ risk and preparation scores from.Results: Two models created by Naïve Bayes-based algorithms showed best performance with an AUC of 0.79 and 0.77 for estimating offenders' risk and preparation scores respectively. These algorithms were significantly better than most, but not all, algorithms. Both scores showed promising distinctiveness between linked series, as well as consistency for crimes within series compared to randomly sampled crimes.Conclusions: Estimating offenders' risk exposure and pre-crime preparation  can complement traditional MO characteristics in the crime linkage process. The estimations are also indicative to function for cross-category crimes that otherwise lack comparable MO. Future work could focus on increasing the number of manually rated offenses as well as fine-tuning the Naïve Bayes algorithm to increase its estimation performance.

Place, publisher, year, edition, pages
2018. Vol. 22, no 1, IDA 322-3210
Keyword [en]
Predictive models, Classification, Crime linkage, Offender behavior, Serial crime, Residential burglary
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-13935OAI: oai:DiVA.org:bth-13935DiVA: diva2:1076050
Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-02-23Bibliographically approved

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Boldt, MartinBorg, AntonSvensson, Martin

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CiteExportLink to record
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