THE GRID OF SWEDEN A MICRO-UNIT ANALYSIS OF VULNERABLE NEIGHBORHOODS MIA PUUR Degree Project in Criminology Malmö University 90-120 credits Two-year master Faculty of Health and Society Criminology Masters’ programme 205 06 Malmö May 2020 THE GRID OF SWEDEN A MICRO-UNIT ANALYSIS OF VULNERABLE NEIGHBORHOODS MIA PUUR Puur, M. The Grid of Sweden. A Micro-unit Analysis of Vulnerable Neighborhoods. Degree project in criminology 15 Credits. Malmö University: Faculty of Health and Society, Department of Criminology, 2020. 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. Keywords: crime, geography, micro-places, police authorities, spatial analysis, socio-demographic, vulnerable neighborhoods 2 ACKNOWLEDGEMENTS I would like to begin by thanking Manne Gerell, my supervisor, who gave me the opportunity to write this paper and therefore made this study possible. With quick responses and helpful suggestions, I thank you for your encouragement and support throughout this whole process. I want to express a special gratitude to my family, partner and our three beautiful children for hanging in there by my side and supporting me all the way. 3 CONTENT ACKNOWLEDGEMENTS ..................................................................................... 3 CONTENT ............................................................................................................... 4 INTRODUCTION ................................................................................................... 5 Aim and research questions ................................................................................. 5 BACKGROUND ..................................................................................................... 6 Theoretical frameworks ....................................................................................... 6 Social disorganisation theory and collective efficacy ...................................... 6 Broken windows .............................................................................................. 7 Strain ................................................................................................................ 7 The labelling perspective ................................................................................. 8 Previous research ................................................................................................. 8 Fear of crime and organised crime .................................................................. 9 The national collection ......................................................................................... 9 METHOD .............................................................................................................. 10 Units of analysis ................................................................................................. 10 Ethical reflections .............................................................................................. 11 Data .................................................................................................................... 11 Material .......................................................................................................... 12 Variables ........................................................................................................ 13 Considerations ............................................................................................... 14 Analytical strategy ............................................................................................. 14 RESULT ................................................................................................................ 14 Characteristics .................................................................................................... 14 A statistical model in progress ........................................................................... 15 Using a vulnerable prediction estimate .............................................................. 17 Category A ..................................................................................................... 18 Category B ..................................................................................................... 18 Category C ..................................................................................................... 19 Category D ..................................................................................................... 20 DISCUSSION ........................................................................................................ 20 The modifiable areal unit problem ..................................................................... 21 Study limitations ................................................................................................ 22 CONCLUSION ...................................................................................................... 23 REFERENCES ...................................................................................................... 24 APPENDIX ............................................................................................................ 28 4 INTRODUCTION Structural societal changes have created many challenges in contemporary Sweden, where social exclusion and marginalisation are a growing issue in and around many urban areas of the country (Sjöberg & Turunen 2018). Low socioeconomic status and living segregation is a common reality for a relatively extensive group of individuals who has not been integrated in the overall society (ibid.). The National Council of Crime Prevention in Sweden sometimes refer to areas or neighborhoods where these structures are common as socially vulnerable (NCCP 2015) and these areas also tend to show higher levels of criminal activity which have an impact on the local community with signs of, among other things, feelings of unsafety (NCCP 2018). The Swedish Police also describe these neighborhoods as vulnerable and they are nowadays seen as a growing societal issue (Police 2017; Police 2019). The Swedish Police further divide these neighborhoods into geographical units and classifies them as vulnerable-, at risk- or particularly vulnerable neighborhoods and they mostly occur in bigger urban areas, but also in smaller cities of Sweden (NCCP 2016). Levels of fear of crime in socially vulnerable areas are significantly higher than in other urban areas and the causes are related to visible disorder and criminal behaviour that affect residents, regardless of whether they are exposed to the crime itself or not (NCCP 2018). Both social and physical disorders, such as littering, car fires and vandalism are known to have a negative impact on residents’ feelings of safety, along with nuisance from groups of young people, joyriding and open drug sales (ibid.). In a British context, Brunton-Smith and Sturgis (2011) found similar patterns where fear of crime was influenced by visible signs of low-level disorder and weak social, economic and structural characteristics. Another phenomenon, that got a lot of media attention in Sweden the last few years and which have been found to have a major impact on resident’s feelings of fear, are shootings (NCCP 2018). The above issues can be associated with criminal gangs or groups of young boys and men, who’s behavior is perceived as threatening (ibid.). Often these problems are clustered, and tend to occur on specific courtyards, streets or squares, rather than in entire residential neighborhoods (ibid.). The Swedish police has since 2015 published three reports regarding vulnerable