Change search
ReferencesLink to record
Permanent link

Direct link
On Descriptive and Predictive Models for Serial Crime Analysis
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Law enforcement agencies regularly collect crime scene information. There exists, however, no detailed, systematic procedure for this. The data collected is affected by the experience or current condition of law enforcement officers. Consequently, the data collected might differ vastly between crime scenes. This is especially problematic when investigating volume crimes. Law enforcement officers regularly do manual comparison on crimes based on the collected data. This is a time-consuming process; especially as the collected crime scene information might not always be comparable. The structuring of data and introduction of automatic comparison systems could benefit the investigation process. This thesis investigates descriptive and predictive models for automatic comparison of crime scene data with the purpose of aiding law enforcement investigations. The thesis first investigates predictive and descriptive methods, with a focus on data structuring, comparison, and evaluation of methods. The knowledge is then applied to the domain of crime scene analysis, with a focus on detecting serial residential burglaries. This thesis introduces a procedure for systematic collection of crime scene information. The thesis also investigates impact and relationship between crime scene characteristics and how to evaluate the descriptive model results. The results suggest that the use of descriptive and predictive models can provide feedback for crime scene analysis that allows a more effective use of law enforcement resources. Using descriptive models based on crime characteristics, including Modus Operandi, allows law enforcement agents to filter cases intelligently. Further, by estimating the link probability between cases, law enforcement agents can focus on cases with higher link likelihood. This would allow a more effective use of law enforcement resources, potentially allowing an increase in clear-up rates.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Institute of Technology , 2014. , 193 p. p.
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 12
Keyword [en]
Machine learning, Linkage, Serial crime analysis, Decision support system
URN: urn:nbn:se:bth-00597Local ID: 978-91-7295-288-1OAI: diva2:833995
Available from: 2014-12-15 Created: 2014-09-08 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(2149 kB)63 downloads
File information
File name FULLTEXT01.pdfFile size 2149 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Borg, Anton
By organisation
Department of Computer Science and Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 63 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 89 hits
ReferencesLink to record
Permanent link

Direct link