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Detecting Crime Series Based on Route Estimation and Behavioral Similarity
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-8929-7220
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-9316-4842
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
2017 (Engelska)Ingår i: 2017 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC) / [ed] Brynielsson, J, IEEE , 2017, s. 1-8Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.

Ort, förlag, år, upplaga, sidor
IEEE , 2017. s. 1-8
Serie
European Intelligence and Security Informatics Conference, ISSN 2572-3723
Nyckelord [en]
Crime route analysis, crime linkage, residential burglary, Behavioral analysis
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:bth-15985DOI: 10.1109/EISIC.2017.10ISI: 000425928200001ISBN: 978-1-5386-2385-5 (tryckt)OAI: oai:DiVA.org:bth-15985DiVA, id: diva2:1192756
Konferens
European Intelligence and Security Informatics Conference (EISIC), Athens
Tillgänglig från: 2018-03-23 Skapad: 2018-03-23 Senast uppdaterad: 2018-05-18Bibliografiskt granskad

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Borg, AntonBoldt, Martin

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Borg, AntonBoldt, MartinEliasson, Johan
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Institutionen för datalogi och datorsystemteknik
Datavetenskap (datalogi)

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