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Filtering estimated crime series based on route calculations on spatiotemporal 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.
2016 (English)In: , 2016Conference paper (Refereed)
Abstract [en]

Law enforcement agencies strive to link serial crimes, most preferably based on physical evidence, such as DNA or fingerprints, in order to solve criminal cases more efficiently. However, physical evidence is more common at crime scenes in some crime categories than others. For crime categories with relative low occurrence of physical evidence it could instead be possible to link related crimes using soft evidence based on the perpetrators' modus operandi (MO). However, crime linkage based on soft evidence is associated with considerably higher error-rates, i.e. crimes being incorrectly linked. In this study, we investigate the possibility of filtering erroneous crime links based on travel time between crimes using web-based direction services, more specifically Google maps. A filtering method has been designed, implemented and evaluated using two data sets of residential burglaries, one with known links between crimes, and one with estimated links based on soft evidence. The results show that the proposed route-based filtering method removed 79 % more erroneous crimes than the state-of-the-art method relying on Euclidean straight-line routes. Further, by analyzing travel times between crimes in known series it is indicated that burglars on average have up to 15 minutes for carrying out the actual burglary event.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Crime filtering, route estimation, crime linkage, residential burglary
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-13934OAI: oai:DiVA.org:bth-13934DiVA: diva2:1076047
Conference
7th European Intelligence and Security Informatics Conference (EISIC), Uppsala
Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-02-23Bibliographically approved

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  • apa
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