The objective was to assess the effectiveness of psychosocial interventions in preventing gang membership and gang-related crime among children and young adults under the age of 30. We performed a systematic review and synthesized interventions targeting universal, selective, and indicated populations published between January 2000 and April 2023. We included 42 (seven randomized, 12 nonrandomized, 23 controlled interrupted time series) studies evaluating 33 unique psychosocial interventions. Synthesis without meta-analysis found a preventive effect of psychosocial interventions in middle schools on gang membership. Furthermore, meta-analysis found that focused deterrence strategies prevented gang-involved violence, and that psychosocial support during probation decreased crime recidivism. This systematic review found significant effects of four psychosocial interventions compared to control in reducing future criminality, especially gun violence, among children and young adults. The findings are discussed regarding policy implications and ethical considerations.

Introduction: Drug market related violence has plagued Sweden in the last decade and has geographically been linked to open drug scenes, ODSs. Surveys have been used to identify ODSs in local police districts in Stockholm County since 2017. The present study analyzes how ODSs and hot spots of police-reported crime relate to geographical indicators of systemic drug market violence.

Method: Drug hot spots are identified using clustering methods. Drug hot spots and ODSs are then both geographically matched with markers for organized crime to analyze the extent to which they can serve as geographical markers for systemic drug market related violence. The markers for organized crime employed are shootings and organized crime related activities described in court cases that include encrypted phone texts.

Results: Our findings suggest that drug hot spots are a rather weak predictor of systemic drug market related violence, whereas ODSs function reasonably well, particularly in the case of ODSs in areas with an established presence of criminal networks.

Conclusion: Measuring drug market locations is complex, but drug market related violence can be understood geographically by linking typical gang-related crimes to places with open drug scenes.

This study evaluates the impact of the LOV3 policy—which allows the police to mandate security guards to patrol and maintain order in public environments—on local crime rates in three locations within Malmö and Helsingborg, the two largest cities in southern Sweden, using data from March 2020 to November 2022. We use interrupted time series analyses on daily crime data to assess the policy’s effects on reported crime rates. Our findings, which reveal no significant impact of the LOV3 policy on reported crimes in the examined locations, underscore the need for further research and refinement of crime prevention strategies.

Objectives: Assessing the predictive accuracy of using prior crime, place attributes, ambient population, community structural, and social characteristics, in isolation and combined when forecasting different violent and property crimes. Method: Using multilevel negative binomial regression, crime is forecasted into the subsequent year, in 50-m grid-cells. Incidence rate ratio (IRR), Prediction Accuracy Index (PAI), and Prediction Efficacy Index (PEI*) are interpreted for all combined crime generators and community characteristics. This study is partially a test of a crude version of the Risk Terrain Modeling technique. Results: Where crime has been in the past, the risk for future crime is higher. Where characteristics conducive to crime congregate, the risk for crime is higher. Community structural characteristics and ambient population are important for some crime types. Combining variables increases the accuracy for most crime types, looking at the IRR. Taking the geographical area into account, crime history in combination with both place- and neighborhood characteristics reaches similar accuracy as crime history alone for most crime types and most hotspot cutoffs. Conclusions: Crime history, place-, and neighborhood-level attributes are all important when trying to accurately forecast crime, long-term at the micro-place. Only counting past crimes, however, still does a really good job.

The current study explores the association between place features (e.g., schools, public transportation nodes, bars and restaurants) and perceived unsafe locations using data from two open-ended items in a cross sectional random sample community survey in Malmö, Sweden. Perceived unsafe locations in respondents’ own neighborhood and in other parts of the city are geocoded and merged with a 200 by 200-m grid-cell network. The data are analyzed using logistic regressions inspired by the Risk Terrain Modeling approach, where geographical representations of place features are used as predictors and geographical representations of unsafe locations used as outcomes. The results show that several place features are associated with unsafe locations, but the importance of different features seem to vary between outcomes. While grid-cells being near an elementary school, convenience store or park demonstrates significant associations with unsafety when respondent’s report locations in their own neighborhood, these patterns are weaker when considering unsafe locations in other parts of the city, where the most important variable is whether a grid-cell is located within a neighborhood defined as vulnerable by the national police agency. This indicates a qualitative difference in what types of locations are being reported as unsafe when respondents are asked about locations in their own neighborhood versus other parts of the city. These results contribute to the current body of research on place-based fear of crime, and the findings may offer guidance in better understanding why some places are perceived as unsafe and how public perceptions of safety may be improved.

Objectives: This study introduces and evaluates GraphTrace, a graph-based method for identifying crime hotspots suitable for CCTV placement. The method addresses key limitations in traditional spatial crime analysis techniques, such as rigid spatial divisions and reliance on heuristics, by dynamically modeling crime clusters with guaranteed distance constraints. Methods: We evaluate GraphTrace using five years of official crime data (N = 125,512) from Malmö, Sweden, and compare its performance against four established spatial methods: Grid+KDE, K-Means, HDBScan, and Greedy PAI Maximization. Each method uses crime data from one year to identify high-crime locations used as suggested CCTV camera placements, which are then evaluated based on their ability to capture crimes occurring within a specified radius in the following year. For example, hotspots identified from 2019 data are assessed against 2020 crime data by counting how many crimes that fall within the radius of each location. Performance is measured using total crime counts and the Predictive Accuracy Index (PAI). Results: GraphTrace significantly outperforms all comparison methods (p<0.05) in terms of both crime capture and PAI. Effect sizes using Cohen’s d range from 0.14 to 1.98, demonstrating up to very large improvements in PAI. Despite its performance, GraphTrace maintains feasible runtimes and scales well. Conclusions: GraphTrace balances precision and computational efficiency by avoiding exhaustive pairwise comparisons while preserving spatial flexibility. Unlike grid-based methods, it does not segment the study area arbitrarily, and unlike many clustering heuristics, it enforces strict distance constraints. This study presents an initial evaluation and open-source implementation of GraphTrace for hotspot detection and CCTV placement, showing strong promise for spatial crime analysis.

Aims: Open drug scenes (ODS) have increasingly drawn the attention of the police and municipalities in Sweden. These locations, where illicit drugs are sold and/or consumed, are often associated with various forms of disorder and crime. While ODS are typically depicted as a phenomenon predominantly found in larger cities, their prevalence and characteristics in smaller cities remain underexplored. This study aims to analyse the patterns and characteristics of ODS, as identified by the police and municipalities, across a range of cities in southern Sweden. Methods: By utilising spatial and temporal analyses of police-reported crimes and demographic statistics, this research examines the characteristics of identified ODS and their connections to socioeconomic disadvantage. Results: The findings suggest that the identified ODS in smaller cities share similar patterns to those found in prior research and in larger urban areas, characterised by lower socioeconomic status and elevated crime rates. Conclusions: Police and municipalities in smaller cities identify places in their communities that closely resemble, although are not necessarily equivalent to, an ODS. Nevertheless, these places are disproportionately burdened by social problems and require targeted assistance.

Since Sweden won the Eurovision song contest 2023 the event was organized in Malmö, Sweden, in 2024. Due to the tense situation and terror threat, this led to a massive police presence in Malmö. Media have reported that this was associated with a substantially reduced crime level, but whether this holds up for academic scrutiny is unclear. Furthermore, one could expect some crime types to go up due to increased police presence, whereas other crime types should go down. Police initiated crimes such as traffic or drug crime could be expected to increase with more police. The massive influx of visitors to the city could be expected to drive up non police-initiated crime, whereas the police presence should reduce it. In the present study interrupted time series analysis is used to identify whether crime differed substantially during Eurovision, and how that varies by crime type.

The aim of the current study is to estimate whether spatial data on place features from OpenStreetMap (OSM) produce results similar to those when employing register data to predict future violent crime in public across three Swedish cities of varying sizes. Using violent crime in public as an outcome, four models for each city are produced using a Risk Terrain Modeling approach. One using spatial data on place features from register data and one from OSM, one model with prior violent crime excluded and one with prior crime included. The results show that several place features are significantly associated with violent crime in public independent of using register or OSM data as input. While models using register data seem to produce more accurate and efficient predictions than OSM data for the two smaller cities, the difference for the largest city is negligible indicating that the models provide similar results. As such, OSM place feature data may be of value when predicting the spatial distribution of future violent crime in public and provide results similar to those when using register data, at least when employed in larger compared to smaller cities. Possibilities, limitations, and avenues for future research when using OSM data in place-based criminological research are discussed.

Do neighborhoods generate fear of crime? In their renowned study published in Criminology, Brunton-Smith and Sturgis (2011) examined this research question using data from three waves of the British Crime Survey (now the Crime Survey for England and Wales). The authors conclude that both neighborhood structural characteristics, signs of disorder, and reported crime rates independently affect individual-level fear of criminal victimization. By utilizing data from six waves of the Swedish Crime Survey, spanning 2018 and 2023, the current study partially replicates the Brunton-Smith and Sturgis study. Utilizing multi-level modelling of roughly 400,00 respondents nested in about 6,000 neighborhoods, the current study examines whether the original findings hold with more recent data and across national contexts.