Through a national collection, the Swedish Police identify and classify vulnerable neighborhoods. Areas are assessed through police perceptions regarding high concentrations of certain problems and criminal activity, such as public acts of violence with risk of harming third parties, open drug markets and organised crime structures. The purpose of this study has been to see whether it is possible to statistically discover these neighborhoods based on socioeconomic and demographic data. Initially, in a national comparison, areas that are defined as vulnerable neighborhoods by the national collection, was compared with other areas in the country. This was done based on a statistical grid consisting of squares with the dimension of 250 x 250 meters, with each square holding information about socio-demographic data. The main aim has been to identify a statistical model that more objectively can identify squares that are vulnerable or not, compared to the police's more subjective assessment. Result from logistic regression analyses implies that vulnerable neighborhoods from the national collection show greater odds at having high concentrations of residents with foreign background, higher unemployment rates and more households with single parents. Lastly, the best fitted regression model for explaining these areas by the means of pseudo R2-value, were used to calculate a prediction value for each square. This value was then analysed using a GIS-software, to discover any areas that in the national collection was classified as vulnerable, but according to the model no longer met the criteria, and then vice versa. The overall result indicate that it is possible to discover areas with higher concentrations of certain characteristics seen in vulnerable neighborhoods, using spatial analyses and logistic regressions of micro-places, to more objectively classify these areas. By aggregating crime data, the result of this study can in the future mean a more effective implementation for police authorities.

According to the law of crime concentration, a certain percentage of crime is predicted to be concentrated at a certain percentage of microgeographic units, and relatively large amounts of crime are predicted to be accounted for by a small percentage of places. Given the lack of research testing the law in a Swedish context and for a whole country, this study set out to examine the concentration of crime at all densely populated areas in Sweden. Analyzing national grid net data, where all densely built-up areas of Sweden were divided into 250 x 250 meter pixels with added police recorded crime data, the study aimed to examine how many percent of the pixels are required to account for 25, 50 and 80% of the crimes in all densely populated areas; how the concentrations differ between small, medium-sized and big cities; how the concentrations differ between violent and property crimes in all of the country; and how an observed crime concentration compares to a counterfactual, randomized concentration. The results indicated a crime concentration that is stronger than the ones observed in most previous studies, with just 0.4, 2.3 and 10.2% of the pixels accounting for 25, 50 and 80% of all crimes in all densely populated areas, respectively. In line with previous research, the results also showed that crime is more strongly concentrated in smaller cities compared to the big ones, that violent crime is more strongly concentrated than property crime, and that the observed concentration of violent crime is considerably stronger than a counterfactual, randomized concentration in the form of a Poisson distribution. Further research on crime concentration in Sweden is requested to build on these findings.