Purpose: 

Police authorities breached several encrypted chat services in a series of data driven investigations. These services are mostly used by offenders, and the results were thousands of arrests. Limited research has studied the ensuing court cases. In this study, we discuss the strengths and limitations of this data source, and compare the contents of three services: EncroChat, SkyECC, and Anom.

Methods: 

We use a sample of 195 court cases from Sweden between 2019 and 2024, where evidence from the encrypted chats was used. The cases involved 667 individuals and 1519 prosecuted offenses, mostly serious drug offences.

Results: 

Network analysis indicates that serious drug offences are strongly connected to violence and weapons in EncroChat, cross-border drug trafficking in SkyECC, and weapons offences in Anom. The average sentence length did not differ between the platforms. Given the sequential breach of the services, this suggests that offenders continued using them for planning serious offending, despite public knowledge of the police interventions.

Conclusions: 

Information from encrypted chat services is an important supplement for research, policy, and practice concerning criminal groups.

We examine whether hosting the 2024 Eurovision Song Contest affected reported crime in Malmö, Sweden. Eurovision generated large visitor inflows and a substantial police reinforcement, creating a unique disruption to the city's routine activities. Using weekly crime data for 2021–2024 and time-series regression models with log-transformed outcomes and weather controls, we find that non-police-initiated crimes declined by 10.2% and property crimes by 8.6% during the contest week, while vandalism increased by 20.6% and police-initiated detections rose by 8.0%. A formal cumulative three-week displacement test shows that the decline in non-police-initiated and property crimes is fully offset by increases in adjacent weeks, consistent with intertemporal displacement rather than a net reduction. In contrast, vandalism shows a significant net increase over the three-week window.

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.

The lack of research on why certain locations are perceived as unsafe has often been attributed to the scarcity of suitable data on place-based fear of crime. To address this gap, this study explores two spatial data collection methods applied in semi-rural areas of Kristianstad, Sweden. The first method uses a digital sketch map integrated with open-ended questions, accessed via an online web map platform, allowing participants to directly mark unsafe locations and resulting in geographically precise data on perceived fear of crime. The second method involves a random sample survey, employing open-ended questions to identify unsafe locations and circumstances, with responses geocoded into points, lines, or polygons for spatial inquiry. The study identifies similarities and differences in the geography of fear of crime derived from these methods. Strong alignment between the web map and survey is observed in both locations, and Spearman’s rank correlation also reveals a significant link between signal crimes and fear of crime. Kernel Density Estimation and Nearest Neighbour analysis reveal significant clusters of fear of crime in both areas, often overlapping with crime hotspots. The findings suggest that a convenience sample collected via a web application performs comparably to a traditional open-ended survey with a random sample, indicating that simpler and more cost-effective methods may be sufficient for capturing place-based fear of crime. This methodological insight supports pragmatic data collection and informs targeted, place-based fear of crime prevention strategies in both rural and urban contexts.

This anthology brings together researchers and practitioners from the fields of criminology and crime prevention in a shared ambition: to advance knowledge about the geographies of fear and unsafety and to address questions of where, when, and why people feel unsafe. By combining diverse perspectives, the anthology demonstrates that unsafety is not only about fear of crime, but also about how people experience their everyday environments. Feelings of unsafety emerge through the interaction between the individual, the place, and the social context – and vary depending on time, space, and personal experience. The anthology provides new insights into how fear of crime and unsafety is distributed across places, why some environments are perceived as more unsafe than others, and how different risk factors interact. At the same time, it highlights the challenges involved in measuring fear of crime and unsafety, and in designing effective interventions. For anyone working with, researching, or interested in issues of fear of crime and unsafety, this anthology offers a comprehensive knowledgebase for understanding the transition from geographies of unsafety to geographies of safety.

The editors of the anthology are Karl Kronkvist (Malmö University), Manne Gerell (Malmö University), and Nicklas Guldåker (Lund University/Nordregio). The anthology is published in Swedish.

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.

Despite a growing interest in place-based crime prevention, certain locations within the built environment remain overlooked in criminological research and policy. This study examines the effect of place-based crime prevention interventions targeting thefts from basements and attics in public housing “vulnerable” areas in Gothenburg, Sweden. Drawing on principles from situational crime prevention and crime science, we evaluate whether physical security improvements reduced incidents of thefts as a part of a broader social investment program targeting “particularly vulnerable areas”. Using a fixed-effects Difference-in-Differences approach, we analyze address-level crime data between 2019 and 2023 while accounting for time trends and spatial heterogeneity while acknowledging a staggered treatment rate across the studied units.

Our findings suggest that these interventions, and particularly those related to door enhancements, were associated with reductions in reported thefts, compared to control addresses. However, variation in effect size across intervention types underscores the importance of understanding the mechanisms further. This study contributes to the growing body of research on micro-place crime prevention by focusing on neglected within-building spaces and offers implications for both urban safety policy and public housing management.

This study systematically examines how spatial resolution, operationalized as grid cell size, influences the predictive performance of Risk Terrain Modeling (RTM) across 32 Swedish urban areas with varying demographic and criminogenic characteristics. Using performance metrics such as the Predictive Accuracy Index (PAI), Predictive Efficiency Index (PEI*), and the F1-score, the analysis explores how methodological factors (prediction thresholds), demographic and geographic conditions (population density, study area size), and contextual factors (social vulnerability, crime density) mediate or moderate the effect of cell size on model performance. Results show that spatial resolution exerts a strong effect on predictive performance, though the direction of this effect varies by performance metric. Smaller grid cells yield higher PAI values, reflecting greater localized accuracy, but perform poorly on PEI* and F1, indicating limited coverage of crime events. Larger grid cells, by contrast, improve PEI* and F1-scores by capturing more crime incidents, but at the cost of spatial specificity. Taken together, the study demonstrates that no single grid resolution is universally optimal. Instead, the effectiveness and fairness of RTM depend on balancing accuracy, efficiency, and coverage in relation to local conditions and intended applications. These findings provide methodological guidance for crime forecasting and contribute to the broader discussion of spatial scale and The Modifiable Areal Unit Problem in predictive policing.