NCJ Number
218945
Journal
Journal of Scandinavian Studies in Criminology and Crime Prevention Volume: 8 Issue: 1 Dated: 2007 Pages: 2-21
Date Published
2007
Length
20 pages
Annotation
This study examined the significance of neighborhood conditions on the risk of violent victimization in residential areas in Sweden.
Abstract
The results reveal that the violence that occurs in a victim’s residential neighborhood accounts for only a small proportion of the violent incidents to which they are exposed. It was also found that, to a large extent, the violence that occurred within the neighborhood of the victim actually occurred within the victim’s own home rather than on the streets. In fact, violence occurring outside of homes accounted for only 10 percent of reported violent incidents. These findings from Sweden differ markedly from the findings of similar studies in the United States. In this study, the results did not indicate any clear neighborhood effects on violent crime. Thus, it may be assumed that exposure to violence in Sweden is not as significantly associated with social disorganization at the neighborhood level in the same way that it is in the United States. Data on victimization experiences and fear of crime by different population segments were drawn from Statistics Sweden’s (SCB) interview surveys of living conditions. The SCB data included a representative sample of 7,739 Swedish individuals aged 16 to 84 years who were interviewed regarding their living conditions and exposure to threats or violence. Neighborhood-level data were drawn from the Small Areas for Market Statistics (SAMS) neighborhood categorization information for the years 2000 and 2001. The study was restricted to SAMS areas from urban regions, which left a sample of 3,288 neighborhoods. Neighborhood variables under examination included demographic characteristics of its inhabitants, residential stability and immigration, socio-economic conditions, education level of residents, and political resources. Data were analyzed using descriptive statistics and logistic regression models. Tables, figure, references