This study examines the concentration of crime at residential buildings and investigates whether housing ownership has an impact on such a concentration. Using Geographic Information Systems (GIS), police-recorded crime data were geocoded at the building level and combined with socio-demographic and land use variables in Poisson regression models. The results indicate that a small percentage of buildings is responsible for a large proportion of crimes. This pattern persists even after offences are standardised by the number of apartments. Buildings under private housing ownership are generally associated with lower crime levels than those under rental; however, this relationship is not uniform across private housing companies and different types of crime. Findings point to the need for further research into how building-level management practices interact with neighbourhood-wide policing strategies and other micro-level factors, such as a building’s design, location, and accessibility, in shaping crime outcomes.

Using 1281 pedestrian tunnels scattered over Stockholm, Sweden’s capital, we investigate the nature and distribution of graffiti in three types of underpasses (pathways, cycleways, and stairways). Combining crowdsourced data and police records, we compare graffiti levels and geography using fieldwork inspections, regression models, and Geographic Information Systems (GIS). The findings indicate that graffiti recorded to the police is present in only a quarter of the pedestrian tunnels, suggesting that recorded practices are concentrated in specific underpasses. Graffiti is more commonly found in larger tunnels, particularly cycling underpasses. Proximity to metro stations, schools, and the presence of young males in the area are factors linked to graffiti occurrence, although not consistently. These results highlight the complexities of relying on graffiti records for public space management, particularly the challenges professionals face in aligning the maintenance of pedestrian tunnels with urban sustainability goals.

This study evaluates the effectiveness of integrating high-resolution remote sensing (RS) data with machine learning (ML) techniques to identify criminogenic environments in urban areas. We employ an unsupervised ISO clustering method to classify land cover from aerial imagery, thereby capturing fine-scale environmental details that are often overlooked in traditional analyses. These clusters are linked to both crime and non-crime events through a presence/absence (case–control) framework, a methodology adapted from species distribution studies, which enables a micro-environmental examination of crime locations. In addition to RS-derived land-cover predictors, the study incorporates socio-economic and demographic variables, as well as a centrality indicator that proxies the intensity of urban activity. A Random Forest classifier is utilized to model the likelihood of street theft incidents based on these predictors. The model achieves robust performance, with an F1-score of 0.88 ± 0.03 as determined by K-fold cross-validation. To enhance model interpretability, SHapley Additive exPlanations (SHAP) is applied. The findings of this research demonstrate that integrating RS data with ML techniques offers a valuable tool for identifying and mapping criminogenic environments. The resulting risk map of Stockholm highlights key urban areas with elevated street theft risk, offering guidance for targeted crime prevention and urban planning strategies. While our workflow simplifies some technical steps compared to other RS + ML pipelines, it still requires GIS and ML competence to implement effectively. This approach reduces, but does not eliminate, sensitivity to spatial unit choice (MAUP) and spatial data dependencies.

This study evaluates the effectiveness of integrating high-resolution remote sensing (RS) data with machine learning (ML) techniques to identify criminogenic environments in urban areas. We employ an unsupervised ISO clustering method to classify land cover from aerial imagery, thereby capturing fine-scale environmental details that are often overlooked in traditional analyses. These clusters are linked to both crime and non-crime events through a presence/absence (case–control) framework, a methodology adapted from species distribution studies, which enables a micro-environmental examination of crime locations. In addition to RS-derived land-cover predictors, the study incorporates socio-economic and demographic variables, as well as a centrality indicator that proxies the intensity of urban activity. A Random Forest classifier is utilized to model the likelihood of street theft incidents based on these predictors. The model achieves robust performance, with an F1-score of 0.88 ± 0.03 as determined by K-fold cross-validation. To enhance model interpretability, SHapley Additive exPlanations (SHAP) is applied. The findings of this research demonstrate that integrating RS data with ML techniques offers a valuable tool for identifying and mapping criminogenic environments. The resulting risk map of Stockholm highlights key urban areas with elevated street theft risk, offering guidance for targeted crime prevention and urban planning strategies. While our workflow simplifies some technical steps compared to other RS + ML pipelines, it still requires GIS and ML competence to implement effectively. This approach reduces, but does not eliminate, sensitivity to spatial unit choice (MAUP) and spatial data dependencies.

This study explores the balance between connectivity and safety inurban tunnels, analysing their criminogenic characteristics usingGeographical Information Systems (GIS), regression models, andpolice data from Stockholm, Sweden. The findings reveal that 86%of police-recorded incidents in tunnels are concentrated in 2% ofthe tunnels, and these mostly involve vandalism. Inner-city tunnelsand those near metro stations are the most crime-prone, except forcycleway tunnels, while violence is concentrated in tunnels nearsports arenas. Designing short tunnels, encouraging communityparticipation in reporting criminal activities, and reinforcing maintenanceefforts are essential for promoting tunnel safety

By deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis.

Syftet med denna studie är att öka förståelsen för tunnlarnas kriminogena egenskaper i stadsmiljöer ochrekommendera riktlinjer för tryggare tunnlar. Studien bygger på analys av brottsstatistik, crowdsourcad data samtplatsbesök och webenkät med planerare och trygghetsexperter i svenska kommuner med fokus på Stockholmsstad.Vi adresserar frågor om tunnlarnas utseende, brottstyper, socioekonomiska faktorer som påverkar brottsnivåer ochplanerarnas kunskap om tunnelplanering, samt ger rekommendationer för framtida stadsplanering. I Stockholmstadfinns det 1 281 tunnlar: 806 gångtunnlar, 182 trapptunnlar och 293 cykeltunnlar. Gångtunnlarna är avsedda förfotgängare, trapptunnlarna underlättar förflyttning mellan nivåer, och cykeltunnlarna erbjuder säkra rutter förcyklister från bilister. Resultaten visar att 2% av tunnlarna koncentrerar 86% av polisanmälda brott. Dennakoncentration kan dock bero på brottstyp och plats i staden (det finns ofta en högre koncentration av brott iinnerstadsområden där dessa tunnlarna ligger). Den vanligaste brottstypen i tunnlar som registreras hos polisen ärskadegörelse, vilket utgör 81% av alla rapporterade incidenter. Analysen visade att polisen registrerar flerklotterbrott totalt sett, framför allt i centrala områden, medan crowdsourcad data ger bättre täckning utanför centralaområden och registreras ofta där folk bor. Större tunnlar (och/eller med mer trafik) är mer utsatta för brott. Atthantera graffiti i tunnlar kan vara svårt. Sommaren 2023 anmäldes en tunnel i Stockholm för skadegörelse, mendet visade sig vara ett kommunalt ungdomsprojekt för kreativt uttryck. Denna incident i Stockholm är ett exempelpå de tydligt olika uppfattningarna av graffiti i tunnlar. Det som initialt uppfattades som ett klotter och därför ett brottvisade sig vara ett kommunalt finansierat projekt utformat för att främja kreativt uttryck bland ungdomar och bidrapositivt till samhället. Förebyggande arbete för klotter i tunnlar kräver att lokala aktörer samarbetar för att diskuteraproblemet och besluta om lämpliga åtgärder.Närhet till tunnelbanan är starkt kopplat till alla brottstyper, och andelen unga män i närområdet (på DeSo nivå)påverkar också brottsligheten i tunnlar. Trapptunnlar, trots att de är färre till antalet, står för 50% av de totala brotten,inklusive 43% av våldsbrotten, 37% av narkotikabrotten, 34% av stölderna och den högsta andelen skadegörelsepå 53%. Vi har genomfört en enkät med bekvämlighetsurval1 av 29 kommuner som visar att bristande belysning ärdet största problemet för otrygghet i tunnlar, följt av dålig sikt, isolerade lägen och klotter. Vanliga underhållsåtgärderinkluderar städning, belysningsunderhåll och mot klotter. Vid nybyggnation följer de flesta enkätrespondenterTrafikverkets riktlinjer, medan vissa även beaktar andra modeller som fokuserar på siktlinjer och belysning. Baseratpå enkätsvaren rekommenderas stadsplanerare att prioritera finansiering för byggande av tunnlar, vilket 68% avrespondenterna anser vara avgörande. Dessutom skulle beprövade exempel och ökad kunskap bland arkitekteroch planerare vara till hjälp. Tydliga riktlinjer och bättre samarbete mellan aktörer är också viktiga aspekter somnämndes av de som svarade på enkäten. Våra rekommendationer innebär att identifiera de mest problematiskatunnlarna och beakta deras multifunktionalitet, med fokus på områden nära tunnelbanestationer, öka belysningenoch synligheten i och omkring dem, samt involvera användare i hög-riskområden i brottsförebyggande arbete.Dessutom är det viktigt att, innan man påbörjar någon intervention, försöka identifiera om problemet orsakas avbrott, rädsla för brott, eller båda, eftersom en tunnel kan uppfattas som otrygg på grund av dåligt underhåll ochändå vara en tunnel fri från brott.

In this special issue, five articles demonstrate the impact of contemporary Environmental Criminology theory on the understanding of crime and the development of crime prevention practices in both urban and rural contexts. The articles exemplify the development of Environmental Criminology as a field that extends beyond crime in physical spaces to encompass offenses in cyberspace, illustrating the field’s adaptability and relevance in an increasingly digital world. Authored by environmental criminologists from diverse disciplinary backgrounds, these articles offer a range of international perspectives, thereby contributing to the field of security while addressing broader social issues.

Understanding the complex relationship between urban environments and crime is crucial for effective urban planning and crime prevention strategies. Spatial analytical methods have provided valuable knowledge into crime patterns, enabling the detection of crime-concentrated environments and informing law enforcement operations and urban planning interventions. The international literature highlights the increasing use of remote sensing in crime analysis, driven by improved data availability and accuracy. Given the potential of this approach, this thesis investigates the use of spatio-temporal data analyses, particularly the incorporation of remote sensing data along with traditional socio-demographic and land use indicators in understanding the dynamics of crime in urban environments. Four crime categories—cannabis-related crimes, street theft, residential burglaries, and sexual crimes—are investigated using Stockholm City in Sweden as a case study. Remote sensing data, particularly very high-resolution imagery, combined with machine learning algorithms, such as the Random Forest classifier, facilitate the prediction of crime risk areas and the identification of environmental factors associated with crime occurrences. While the thesis reflects upon the advantages and disadvantages of using remote sensing in crime analyses, findings offer practical insights for policymakers, urban planners, and law enforcement agencies, enabling the development of data-informed strategies to foster safer and more resilient urban environments.

Förståelsen av det komplexa sambandet mellan urbana miljöer och brott är avgörande för effektiv stadsplanering och brottsförebyggande strategier. Rumsliga analytiska metoder har gett värdefulla insikter om brottsmönster, vilket möjliggör identifiering av miljöer med hög brottskoncentration och ger underlag för brottsbekämpningsinsatser och stadsplaneringsåtgärder. Den internationella litteraturen lyfter fram den ökande användningen av fjärranalys inom brottsanalys, vilket drivs av förbättrad dataåtkomst och noggrannhet. Med tanke på potentialen i denna metod utforskar denna avhandling användningen av spatio-temporala dataanalyser, särskilt införlivandet av fjärranalysdata tillsammans med konventionella socio-demografiska och markanvändningsindikatorer för att förstå dynamiken kring brott i urbana miljöer. Fyra brottskategorier – cannabisrelaterade brott, fickstölder, bostadsinbrott och sexualbrott – undersöks med Stockholms stad i Sverige som fallstudie. Fjärranalysdata, särskilt bilder med mycket hög upplösning, i kombination med machine learning-algoritmer som Random Forest-klassificeraren, underlättar prediktionen av brottsriskområden och identifieringen av miljöfaktorer som är förknippade med brottsförekomster. Även om avhandlingen reflekterar över fördelar och nackdelar med att använda fjärranalys i brottsanalys, erbjuder våra resultat praktiska insikter för beslutsfattare, stadsplanerare och brottsbekämpande myndigheter, vilket möjliggör utvecklingen av datainformerade strategier för att främja säkrare och mer motståndskraftiga urbana miljöer.

Drawing from environmental criminology principles, this article explores the existing literature to assess the utility of remote sensing data in detecting and analysing features in the urban environment that are associated with crime occurrence. A systematic review of the literature in the English language from 2003 until the first half of 2023 from two major databases, Scopus and Science Direct, is carried out. As many as 910 publications were selected, from which 36 publications satisfied the selection criteria. Findings show that neighborhood's design has a quantifiable imprint that is possible to be observed with very high spatial-resolution imagery. Given its high spatial and temporal resolution, remote sensing data can to different degrees support the identification of criminogenic features in urban environments (streets and roads, property boundaries, housing density, characteristics and density of vegetation as well as luminosity levels), but when it is used for the detection of potentially illegal activities, infringement of people's privacy and methods lacking validation still present serious concerns. The article concludes with a discussion of the opportunities and challenges of using remote sensing data in crime analysis.