Nicklas Guldåker

Deprived neighborhoods in Sweden in which criminal networks have a negative impact on local residents are labeled as “vulnerable neighborhoods” by the police. The method used by the police to classify such neighborhoods is largely based on perceptions, which raises issues of subjectivity and potential biases. The present study explores the characteristics of such neighborhoods based on registry data on socio-demographics and crime. The study employs data in the form of a grid of 250 x 250 meter vector grids (N=116,660) with data on population, foreign background, employment, age characteristics, household type, and eight types of crime. Generalized mixed-effects models of vector grids nested in municipalities were fitted to analyze the characteristics of vector grids classified as vulnerable (N=1678). Several variables are significantly associated with a vector grid being classified as vulnerable, with the proportion of the population that is foreign born, and the proportion with foreign-born parents, being the strongest predictors. In addition, we consider whether there are systematic differences between municipalities and develop a model based on regression coefficients to predict whether a vector grid is vulnerable. The model reclassifies 39.8 percent of the vector grids, identifying locations that statistically resemble vulnerable neighborhoods but are not classified as such, and vice versa.