a b s t r a c tSince the end of the 1990s, the number of fires has increased dramatically in Malmö, a city in the southernmost part of Sweden. Between 1998 and 2009, the increase was 215%, and a large number of the fires were intentional. The aim of this paper is to deepen our understanding of the underlying causes of the spatial and spatio-temporal distribution of intentional fires in Malmö, and to analyse how different living conditions in Malmö sub-areas may determine the frequency of intentional fires. This paper's main contributions to the field is to operationalize theories of social stress into measurable variables and an index of living conditions (ILC), and to statistically and spatially analyse the underlying relationship between living conditions and intentional fires. One key conclusion is that the spatio-temporal patterns of intentional fires can be determined by different living conditions and different levels of exposure to socio-economic stressors. Another important finding is to emphasize the importance of analysing specific and local patterns of fire incidents and living conditions in order to utilize them in locallyadapted fire safety policy formulations and in implementing preventative measures.

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Swedish emergency services still have relatively limited resources and time for proactive fire prevention. As a result of this, there is an extensive need for strategic working methods and knowledge to take advantage of spatial analyses. In addition, decision-making based on visualizations and analyses of their own collected data has the potential to increase the validity of strategic decisions. The objective of this paper is to critically examine how some different geovisualization techniques—point data, kernel density and choropleth mapping—actively can complement each other and be applied in fire preventive work. The results show that each technique itself has limitations, but that, in combination, they increase the scope for interpretation and the possibilities of targeting different forms of preventive measures. The investigated geovisualization techniques facilitate various forms of fire prevention such as identifying which areas to prioritize for outreach, home visits, identification and targeting of different risk groups and customized information campaigns about certain types of fires in risk-prone areas. Furthermore, fairly simple mapping techniques can be utilized directly to evaluate incident reports and increase the quality of geocoded fire incidents. The study also shows how some of these techniques can be applied when analyzing residential fire incidents and their relation to underlying structural and socio-economic factors as well as spatio-temporal dimensions of fire incident data. The spatial analyses and supporting maps can help find and predict risk areas for residential fires or be used directly to formulate hypotheses on fire patterns. The generic functionality of the visualization methods makes them also useful for visual analysis of other types of incidents, such as reported crimes and accidents. Finally, the results are applicable to a work process adapted to the Swedish legislation on confidential data.

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Deprived neighborhoods where criminal networks have a negative impact on local residents are in Sweden labeled as vulnerable neighborhoods by the police. The method used by the police to classify such neighborhoods is largely based on police perceptions, which raises issues around subjectivity and potential biases. The present study explores the characteristics of such neighborhoods based on registry data over sociodemographics and crime. The data used is a grid (N=116 660) of 250x250 meter vector grids with data on population, foreign background, employment, age characteristics, household types and eight types of crime. Generalized mixed effect 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 share of population that is foreign born and share with parents foreign born 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 or not. The model re-classifies 39.8% of the vector grids, identifying locations that statistically resemble vulnerable neighborhoods but are not classified as such and vice versa.