Approaches for estimating the Uniqueness of linked residential burglaries
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Context: According to Swedish National Council for Crime Prevention, there is an increase in residential burglary crimes by 2% in 2014 compared to 2013and by 19% in the past decade. Law enforcement agencies could only solve three to five percent of crimes reported in 2012. Multiple studies done in the field of crime analysis report that most of the residential burglaries are committed by relatively small number of offenders. Thus, the law enforcement agencies need toinvestigate the possibility of linking crimes into crime series.
Objectives: This study presents the computation of a median crime which is the centre most crime in a crime series calculated using the statistical concept of median. This approach is used to calculate the uniqueness of a crime series consisting of linked residential burglaries. The burglaries are characterised using temporal, spatial features and modus operandi.
Methods: Quasi experiment with repeated measures is chosen as research method.The burglaries are linked based on their characteristics(features) by building a statistical model using logistic regression algorithm to formulate estimated crime series. The study uses median crime as an approach for computing the uniqueness of linked burglaries. The measure of uniqueness is compared between estimated series and legally verified known series. In addition, the study compares the uniqueness of estimated and known series to randomly selected crimes. The measure of uniqueness is used to know the feasibility of using the formulated estimated series for investigation by the law bodies.
Results: Statistical model built for linking crimes achieved an AUC = 0.964,R 2 = 0.770 and Dxy = 0.900 during internal evaluation and achieved AU C =0.916 for predictions on test data set and AUC = 0.85 for predictions on known series data set. The uniqueness measure of estimated series ranges from 0.526to 0.715, and from 0.359 to 0.442 for known series corresponding to differentseries. The uniqueness of randomly selected crimes ranges from 0.522 to 0.726 for estimated series and from 0.636 to 0.743 for known series. The values obtained are analysed and evaluated using Independent two sample t-test, Cohen’s d and kolmogorov-smirnov test. From this analysis, it is evident that the uniqueness measure for estimated series is high compared to the known series and closely matches with randomly selected crimes. The uniqueness of known series is clearly low compared to both the estimated series and randomly selected crimes.
Conclusion: The present study concludes that estimated series formulated using the statistical model has high uniqueness measures and needs to be furtherfiltered to be used by the law bodies.
Place, publisher, year, edition, pages
2016. , 67 p.
Residential burglaries, Median crime, Uniqueness, Logistic Regression, Machine Learning, Data Mining, Swedish police, Serial Crimes, Linked crimes
IdentifiersURN: urn:nbn:se:bth-11823OAI: oai:DiVA.org:bth-11823DiVA: diva2:921537
Subject / course
DV2566 Master's Thesis (120 credits) in Computer Science
DVAXA Master of Science Programme in Computer Science
2016-01-25, J1640, Hogskolan Grasvik, Blekinge Institute of Technology, Karlskrona, 14:00 (English)
Boldt, Martin, University Lecturer
Sundstedt, Veronica, University Lecturer