The current paper is aimed at providing an overview of the current state of research regarding the potential of linking series of crimes to single offenders through repeated modus operandi behaviors. A systematic literature review was conducted to document findings from previous evaluation research as to the predictive accuracy of crime linkage specific to residential burglary. The findings indicate that predictions of linked burglaries can be made with moderate to high predictive accuracy. In order to get an understanding of the extent to which residential burglary offenders repeat their crime scene behaviors, the findings are discussed in relation to the criminological theories Routine activities theory and the Rational Choice perspective. Future research is suggested.

Denna avhandling avser att beskriva hur polisen i Mellersta Skåne arbetar förebyggande med bostadsinbrott. Eftersom inbrott har blivit ett problem i detta område under det senaste decenniet är det intressant att veta vilka åtgärder polisen vidtar för att minska och förebygga dem. Resultaten visar att polisen i mellersta Skåne behandlar bostadsinbrott som en viktig fråga och genomför både särskilda förebyggande åtgärder, såsom en specifik intervention, och dagliga förebyggande åtgärder, t.ex. genom att sprida information eller kartläggning och patrullering av vissa områden. De flesta av de använda förebyggande åtgärderna bygger på rutinaktivitetsteorin, implicit eller explicit. Grannsamverkan rekommenderas av polisen, men även att bara lära känna sina grannar. Upprepad viktimisering verkar, intressant nog, inte vara ett problem i Skåne. Vidare, är det inte mycket som är känt om den typ av gärningsmannen som begår bostadsinbrott. För att förebyggande insatser ska ha en större inverkan, bör det finnas både kortsiktiga och långsiktiga metoder. / This thesis aims to describe how the police work with residential burglary prevention in police area Middle Scania. Since burglary has become a problem in this area in the past decade, it is interesting to know what measures the police take to reduce and prevent it. The results show that the police in Middle Scania treat domestic burglary as a serious issue and implement both specific prevention measures, such as a specialised intervention, and day-to-day prevention measures, e.g. spreading the information or mapping and patrolling certain areas. Most of the used prevention measures are based on Routine Activity Theory, implicitly or explicitly. Neighbourhood watch is recommended by the police, as well as simply getting to know one’s neighbours. Interestingly enough repeat victimisation does not appear to be a problem in Middle Scania. Not much is known about the type of the offender that commits domestic burglary. Both short term and long term measures should be applied in order for prevention to have a greater impact.

No description available.

The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.

This study examines geographic patterns and geographic factors of residential burglary at the Nashville, TN area for a twenty year period at five year interval starting in 1988. The purpose of this study is to identify what geographic factors have impacted on residential burglary rates, and if there were changes in the geographic patterns of residential burglary over the study period. Several criminological theories guide this study, with the most prominent being Social Disorganization Theory and Routine Activities Theory. Both of these theories focus on the relationships of place and crime. A number of spatial analysis methods are hence adopted to analyze residential burglary rates at block group level for each of the study year. Spatial autocorrelation approaches, particularly Global and Local Moran's I statistics, are utilized to detect the hotspots of residential burglary. To understand the underlying geographic factors of residential burglary, both OLS and GWR regression analyses are conducted to examine the relationships between residential burglary rates and various geographic factors, such as Percentages of Minorities, Singles, Vacant Housing Units, Renter Occupied Housing Units, and Persons below Poverty Line. The findings indicate that residential burglaries exhibit clustered patterns by forming various hotspots around the study area, especially in the central city and over time these hotspots tended to move in a northeasterly direction during the study period of 1988-2008. Overall, four of the five geographic factors under examination show positive correlations with the rate of residential burglary at block group level. Percentages of Vacant Housing Units and Persons below Poverty Line (both are indicators of neighbor economic well-being) are the strong indicators of crime, while Percentages of Minorities (ethnic heterogeneity indictor) and Renter Occupied Housing Units (residential turnover indictor) only show modest correlation in a less degree. Counter-intuitively, Percentage of Singles (another indicator of residential turnover) is in fact a deterrent of residential burglary; however, the reason for this deterrence is not entirely clear.