Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Detecting Crime Series Based on Route Estimation and Behavioral Similarity
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 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.
2017 (English)In: 2017 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC) / [ed] Brynielsson, J, IEEE , 2017, p. 1-8Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1-8
Series
European Intelligence and Security Informatics Conference, ISSN 2572-3723
Keywords [en]
Crime route analysis, crime linkage, residential burglary, Behavioral analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15985DOI: 10.1109/EISIC.2017.10ISI: 000425928200001ISBN: 978-1-5386-2385-5 OAI: oai:DiVA.org:bth-15985DiVA, id: diva2:1192756
Conference
European Intelligence and Security Informatics Conference (EISIC), Athens
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-05-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Borg, AntonBoldt, Martin

Search in DiVA

By author/editor
Borg, AntonBoldt, MartinEliasson, Johan
By organisation
Department of Computer Science and Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 32 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf