The event takes place:
Anton Borg's doctoral defence
Welcome to attend Anton Borg's doctoral defence at the Department of Computer Science and Engineering, Blekinge Institute of Technology.
Time: Friday, 24th of October, at 13.00
Place: J1650, Campus Gräsvik, Karlskrona
Thesis title: "On Descriptive and Predictive Models for Serial Crime Analysis"
Research education subject: Computer Science
Opponent: Associate professor Ulf Johansson, University of Borås
· Professor Henrik Boström, Stockholm university
· Associate professor Tomas Olovsson, Chalmers university of technology
· Doctor Catharina Lewin, the Swedish Police in Kalmar County
Deputy member: Associate professor Mikael Svahnberg, BTH
Main advisor: Associate professor Niklas Lavesson, BTH
The thesis is available at:
After the defence there will be served some refreshments. Please contact Karin Svahnberg (firstname.lastname@example.org) no later than October 17 if you intend to participate and kindly note if you have any food allergies or need special food.
Please forward this information to anyone who might be interested in this matter.
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.
Organizer, personal / school / organizer:
Department of Computer Science and Engineering