Context. According to Swedish National Council for Crime Prevention, there is an increase of 19% in residential burglary crimes in Sweden over the last decade and only 5% of the total crimes reported were actually solved by the law enforcement agencies. In order to solve these cases quickly and efficiently, the law enforcement agencies has to look into the possible linked serial crimes. Many studies have suggested to link crimes based on Modus Operendi and other characteristic. Sometimes crimes which are not possible to travel spatially with in the reported times but have similar Modus Operendi are also grouped as linked crimes. Investigating such crimes could possibly waste the resources of the law enforcement agencies. Objectives. In this study, we investigate the possibility of the usage of travel distance and travel duration between different crime locations while linking the residential burglary crimes. A filtering method has been designed and implemented for filtering the unlinked crimes from the estimated linked crimes by utilizing the distance and duration values. Methods. The objectives in this study are satisfied by conducting an experiment. The travel distance and travel duration values are obtained from various online direction services. The filtering method was first validated on ground truth represented by known linked crime series and then it was used to filter out crimes from the estimated linked crimes. Results. The filtering method had removed a total of 4% unlinked crimes from the estimated linked crime series when the travel mode is considered as driving. Whereas it had removed a total of 23% unlinked crimes from the estimated linked crime series when the travel mode is considered as walking. Also it was found that a burglar can take an average of 900 seconds (15 minutes) for committing a burglary. Conclusions. From this study it is evident that the usage of spatial and temporal values in linking residential burglaries gives effective crime links in a series. Also, the usage of Google Maps for getting distance and duration values can increase the overall performance of the filtering method in linking crimes.

Geographical profiling (GP) is a method used to determine an offender’s most likely area of residence. GP helps to better understand the spatial behaviour of an offender, by connecting series of crimes in a specific area. Research have shown that the geographic space plays an important part when solving crimes, especially with respect to serial offenders that tend to offend closer to their home. Despite the fact that GP has had some success, the field has received criticism, and speculations regarding its applicability for actual investigations. This thesis seeks to evaluate whether a specific method within GP could have been applied for an actual investigation. Thus, the aim of this study is to explore if it is possible to apply the Circle Theory of Environmental Range to the case of Marcel Lychau Hansen, ‘Amagermanden’, who is a famous Danish serial offender, and whether the theory could have contributed to the investigation at the time. By conducting a case study, the results showed that Hansen was operating as a commuter, and the theory was therefore not able to predict a narrowed area for his residence, since it was not possible to perform the Hypothesis of Environmental Range. However, the study showed that he had several of his daily routines on Amager – and this finding highlights, that considering the geographical factors would have been an important factor for the investigation. The study concludes the theory to be applicable to the case, though it was found limited in how it could have contributed to the investigation, and the geographical profile could not stand alone for this specific investigation.

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.