Estimating the Effects of Outdoor Crime Prevention Interventions Using a Staggered Difference-in-Differences Model

Estimating the Effects of Outdoor Crime Prevention Interventions Using a Staggered Difference-in-Differences Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimating the Effects of Outdoor Crime Prevention Interventions Using a Staggered Difference-in-Differences Model Jens Widmark This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8940567/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study evaluates outdoor environmental interventions implemented by a municipal public housing provider in police-designated vulnerable residential areas in Gothenburg, Sweden. Using geocoded address-level crime data (2019–2023) and a staggered difference-in-differences design, we estimate the effects of interventions inspired by situational crime prevention (SCP) principles on outdoor-related crime. Across multiple specifications and control groups, we find no statistically significant reductions following intervention exposure. Event-study estimates indicate no differential pre-treatment trends, and robustness checks yield substantively similar results. Descriptive spatial analyses show no consistent evidence of crime displacement. The findings suggest that broad, multi-purpose housing-led improvement strategies do not automatically translate into measurable crime reductions when preventive mechanisms are diffuse or weakly specified. The study highlights the importance of aligning environmental interventions with clearly defined crime problems and contributes to debates on the role of public housing providers in community safety governance. situational crime prevention public housing difference-in-differences community safety governance Figures Figure 1 Figure 2 Figure 3 Introduction Situational crime prevention (SCP) in residential areas has long been promoted as a pragmatic and opportunity-focused approach to reducing crime and improving perceptions of safety (Clarke, 1997; Clarke & Bowers, 2017). Rather than addressing underlying social causes of offending, SCP targets the immediate conditions under which crime occurs by altering situational cues, increasing perceived effort or risk, and reducing accessibility to suitable targets (Clarke, 1997; Shariati & Guerette, 2017). Within the SCP framework, manipulation of environmental conditions is one prevention strategy that aims to reduce opportunities for crime, fear, and offender motivation. Environmental interventions have been described as effective and relatively low-cost tools for improving safety (Welsh & Farrington, 1999). At the same time, empirical evaluations report variable outcomes across settings and intervention types (Eck et al., 2009; Bowers & Johnson, 2016), including substantial reductions and more limited or statistically indistinct changes relative to broader crime trends (e.g., Kondo et al., 2016; Piza et al., 2019; Welsh & Farrington, 2009; Widmark, 2026). This study examines area-specific crime prevention initiatives implemented in residential areas designated as “vulnerable” by the Swedish police in Gothenburg, Sweden (Swedish Police, 2025). The interventions were implemented by the municipal public housing organisation and targeted outdoor residential environments as part of a broader community improvement strategy (Framtiden, 2020). Building on an earlier evaluation conducted within the same strategic programme (Author, 2025), the present study assesses whether outdoor environmental measures associated with SCP principles were followed by changes in outdoor-related crime. This study seeks to contribute to the broader literature on situational crime prevention and community safety. In doing so, it responds to calls for more rigorous evaluations of real-world prevention initiatives implemented by non-policing actors, such as public housing providers, operating within complex social and organisational contexts (Crawford & Evans, 2017). The research question guiding the analysis is: Are the housing company-led outdoor environmental interventions in disadvantaged residential areas associated with reductions in outdoor-related crime, relative to comparable non-treated areas? Situational crime prevention and the intervention logic SCP is based on the premise that crime can be prevented by reducing opportunities and increasing the perceived risks or efforts associated with offending (Clarke, 1997). Related to this notion are opportunity-based theories, including routine activity theory, crime pattern theory, and rational choice perspectives that describe how criminal events arise through the convergence of motivated offenders, suitable targets, and insufficient guardianship and are shaped by everyday movement patterns and environmental design (Brantingham & Brantingham, 2008; Clarke & Bowers, 2017; Cohen & Felson, 1979). Within residential environments, these theoretical perspectives have frequently been operationalised through Crime Prevention Through Environmental Design (CPTED)-inspired interventions. Such interventions are typically focused on the physical environment and aim to enhance natural surveillance, improve access control, strengthen territorial reinforcement, and maintain positive images of place (Schneider, 2005). The principles often overlap, and single interventions may simultaneously address multiple preventive mechanisms (Cozens et al., 2005). Broad or loosely defined interventions may fail to influence the situational mechanisms that give rise to particular crime types, even when implemented with good intentions and substantial resources (Braga, 2008; Taylor, 2002). Importantly, CPTED interventions on environmental changes in residential areas have shown differing efficiency, both in terms of intervention types and for type of crime (Cozens & Love, 2015; Crowe, 2000; Widmark, 2026). From a community safety perspective, SCP has been embedded within broader governance frameworks, where responsibility for crime prevention extends beyond the police to include local authorities, housing providers, and other non-policing actors (Clarke, 1997). An increased focus on local governance and involvement of multi-stakeholder initiatives in place-based environmental crime prevention solutions have emerged within Swedish policy on community safety (Lidskog & Persson, 2012). Thus, the local public housing organisation of Gothenburg has included SCP initiatives in their local governance of housing stock in general, and in vulnerable areas in particular (Framtiden, 2020). This multi-purpose nature of housing-led interventions raises important questions for evaluation. When interventions are not explicitly designed to address specific crime mechanisms, observed outcomes may reflect a mismatch between preventive intentions and criminogenic realities rather than a failure of situational prevention per se. Study Setting The interventions evaluated in this study were implemented in Gothenburg, Sweden’s second-largest city, located on the western coast of the country and home to approximately 600,000 inhabitants. Within the city, the Swedish Police Authority has designated a number of neighbourhoods as vulnerable, a classification used to describe areas characterised by elevated crime levels, reduced trust in public institutions and the presence of organised criminal networks (Swedish Police, 2023). These police-defined vulnerable areas form the broader spatial and institutional context within which the interventions studied here were implemented. The focus on this study is on a subset of these neighbourhoods, identified by the municipal public housing organisation as “development areas.” These areas constitute smaller spatial units nested within vulnerable neighbourhoods and consist primarily of multi-dwelling residential buildings owned and managed by the public housing providers. The designation of development areas reflects an organisational strategy aimed at concentrating resources and interventions within particularly prioritized parts of the housing stock (Framtiden, 2020). Depending on neighbourhood, the municipal housing organisation owns and manages between approximately 50 and 90 per cent of all residential units. Much of the housing stock was constructed during the large-scale national housing programme of the 1960s and 1970s (Hall & Vidén, 2005). As a part of a long-term social investment strategy initiated in 2020, the public housing organisation allocated resources to improving living conditions and safety in development areas over a ten-year period (Framtiden, 2020). While crime prevention constituted one stated objective of this strategy, interventions were also motivated by broader goals related to maintenance, functionality, attractiveness, and residents’ quality of life. Compared to the rest of the city, these areas differ markedly in socio-demographic composition. As shown in Table 1, these areas are characterised by higher levels of economic disadvantage, unemployment, and overcrowding, as well as a substantially larger proportion of residents with foreign backgrounds. Housing tenure is dominated by rental units, and the built environment consists of large shares of multi-dwelling housing. <> The concentration of public housing ownership enables the housing provider to implement environmental interventions at scale but also means that interventions are introduced in areas already facing multiple social and structural challenges. It is an illustration of how environmental crime prevention initiatives can be embedded within broader systems of urban governance and welfare provision. The interventions evaluated in this study should therefore be understood not as isolated crime control measures, but as part of a multi-purpose housing strategy operating in socially and economically disadvantaged residential contexts. Interventions The outdoor interventions examined in this study comprised a broad range of physical and environmental modifications implemented across the designated development areas. For analytical purposes, individual intervention records were harmonised into four overarching classes derived from established CPTED principles: Maintenance Management, Natural Surveillance, Access Control, and Activity Support (see Table 2). <> From an SCP perspective, the interventions display both mechanism-specific and multi-purpose characteristics. While certain measures (e.g., locks, barriers, lighting) correspond closely to clearly defined situational mechanisms, others blend maintenance, aesthetic, and community-oriented objectives. This theoretical heterogeneity is analytically relevant, as it may influence the likelihood of observing measurable crime reductions in aggregate outcome measures. Data and methods This study draws on police-recorded crime data covering offences in Gothenburg between January 2019 and December 2023. Crime records were geocoded and linked to residential addresses managed by the municipal public housing organisation. The unit of analysis is the address-month, enabling the construction of a balanced panel capturing temporal variation in crime and intervention status across locations. The outcome measure consists of reported outdoor-related crimes, including assaults, robberies, thefts of and from bicycles and motor vehicles, and vandalism. Information on outdoor interventions was obtained from the public housing organisation through quarterly check-ins throughout the study period (2021-2023). Each intervention record includes the type of measure, the addresses affected by interventions, and the date of initiation. Given the diversity and scale of interventions, treatment is defined at the address level as the initiation of any documented outdoor intervention. To account for potential uncertainty in intervention documentation, robustness checks were conducted using alternative treatment definitions, including expanded treatment areas intended to capture possible undocumented interventions or broader exposure to the intervention strategy. Identification strategy The causal effect of outdoor interventions is estimated using a difference-in-differences framework designed for staggered treatment adoption across units and time. Unlike conventional two-way fixed effects models, this approach allows for treatment effects to vary across cohorts and avoids bias arising from inappropriate comparisons between early- and late-treated units (Callaway & Sant’Anna, 2021). The identifying assumption is that absent intervention, treated and control addresses would have followed similar trends in outdoor crime. This assumption is assessed through pre-treatment comparisons and event-study estimates. The primary control group consists of public housing addresses owned by the same municipal housing organisation and located in the same broader neighbourhood context as the treated development areas, but not designated for concentrated intervention during the study period. The comparison therefore contrasts addresses exposed to the development-area strategy with other housing units operating under the same ownership and neighbourhood conditions. Because treated and control addresses share the same broader local environment, the estimated effects capture the marginal impact of the concentrated intervention strategy rather than general neighbourhood-level change. As a robustness check, alternative specifications use public housing addresses located outside the vulnerable neighbourhood context as controls. Estimation and inference Treatment effects are reported as average treatment effects on the treated (ATT), estimated across cohorts and time periods. Standard errors are clustered at the address level to account for serial correlation in crime outcomes (Bertrand et al., 2004). Furthermore, the analysis estimates the average effect of exposure to any documented outdoor intervention, rather than intervention-specific effects. To examine potential spatial displacement or diffusion of crime, a supplementary analysis was conducted comparing crime trends in treated areas with those in adjacent non-treated areas. Following prior research on situational crime prevention and spatial spillovers (e.g., Johnson et al., 2014), crime incidents were examined in concentric buffer zones surrounding treated locations. Treated addresses were grouped into spatial clusters using a density-based algorithm and mutually exclusive buffer zones were constructed at distances of 0-200 metres and 200-500 metres from each treated cluster using DBSCAN (Hahsler et al., 2019). Annual counts of outdoor-related crimes were calculated for treated addresses, buffer zones, and control addresses (Appendix Table A2). Because of the staggered timing of interventions and the aggregation of spatial clusters, the displacement analysis is descriptive and intended to identify broad patterns rather than estimate causal spillover effects. Results Table 3 presents the yearly distribution of outdoor-related crimes across development areas, comparison areas within the same broader neighbourhood context, and the rest of the city between 2019 and 2023. Development areas include all public housing addresses designated for intervention, regardless of specific intervention type or timing. Same neighbourhoods (excluding development areas) include public housing addresses located in the same broader neighbourhood context but not designated for intervention. <> Across the study period, development areas exhibit consistently higher crime rates per 1,000 addresses compared to the same neighbourhood comparison areas. The descriptive pattern indicates substantial temporal fluctuation in crime levels across all areas, particularly around 2020-2021, suggesting that broader city-level dynamics affect both treated and comparison areas during the study period. Overall effects of outdoor interventions Figure 1 presents the event-study estimates of group-time average treatment effects for outdoor environmental interventions, using public housing addresses located within the same police-defined vulnerable neighbourhoods but outside the designated development areas as the primary control group. The estimates show no statistically significant reduction in outdoor-related crime following the implementation of interventions. While the post period point estimates do tend to more often be negative, they are close to zero and with confidence intervals consistently overlap the null. No systematic divergence between treated and control addresses is observed prior to intervention, providing no evidence of differential pre-treatment trends. <> To assess the sensitivity to control group selection and potential spillover effects, an alternative specification was estimated using public housing addresses located outside the police-defined vulnerable neighbourhoods as controls. Event-study estimates for this specification are presented in Figure 2. Consistent with the main analysis, no statistically significant post-intervention effects are observed. Estimated treatment effects remain close to zero across post-intervention periods, and confidence intervals largely overlap with those from the primary specification. <> Average treatment effects While the event-study plots provide a dynamic view of treatment effects over time, Table 4 summarises the estimated average treatment effects on the treated (ATT) across post-intervention periods for the main and robustness specifications. Across specifications, average treatment effects are close to zero and statistically indistinguishable from zero (ATT: -.007, 95% CI [-.027, .014]). As the outcome is measured in monthly crimes per address, the estimated ATT corresponds to a reduction of approximately 84 crimes per 1,000 addresses annually. The robustness specification using external controls yields nearly identical estimates. These aggregated estimates reinforce the conclusion drawn from the event-study analyses: outdoor environmental interventions, as implemented within the development areas, were not associated with detectable reductions in reported outdoor-related crime relative to control areas. <> Alternative models based on an intention-to-treat (ITT) framework were estimated using the broader policy exposure definition (Model B), assuming policy-level exposure for all addresses located within development areas and comparing them to public housing addresses not connected to the policy. When defining treatment onset as September 2020, corresponding to the first recorded outdoor intervention, no statistically significant effect is observed (ATT = −0.0043, 95% CI [−0.0257, 0.017]). In contrast, defining policy onset as early 2020, corresponding to the official policy launch, produces a small but statistically significant increase in outdoor crime (ATT = 0.0167, 95% CI [0.0058, 0.0276]). This sensitivity to treatment-policy timing suggests that early 2020 dynamics, rather than intervention exposure, account for the positive estimate under the earlier onset definition. Leave-one-out robustness analyses were conducted for both models by iteratively excluding one treated neighbourhood at a time, yielding substantively similar estimates across all specifications (Appendix Figure A1 and Appendix Table A3). To examine whether aggregate results mask heterogeneous effects across crime categories, additional models were estimated separately for robbery, assault, vehicle-related crime, and outdoor vandalism (Appendix Table A1). For assault, vehicle-related crime, and vandalism, estimated treatment effects were small in magnitude and statistically indistinguishable from zero. A small but significant positive effect was observed for robbery (ATT = 0.0007, p < 0.001). Given the low base rate of robbery and the absence of consistent effects across related crime categories, this isolated estimate should be interpreted with caution. Displacement Additional descriptive analyses examined whether the absence of crime reductions in treated areas could reflect spatial displacement or diffusion of benefits to adjacent areas. Figure 3 presents indexed crime trends (2019 = 100) for treated addresses, buffer zones (0–200 metres and 200–500 metres), and control addresses. General declines are observed in 2022 across all defined zones. Importantly, no sustained divergence between treated and surrounding buffer zones is observed following intervention. In particular, buffer zones do not display consistent post-intervention increases relative to treated areas that would suggest systematic displacement. <> Because the displacement analysis relies on aggregated spatial clusters and staggered intervention timing, it is descriptive rather than causal in nature. Annual raw crime counts underlying the indexed trends are reported in Appendix Table A2. Discussion This study set out to evaluate whether outdoor environmental interventions implemented by a municipal public housing organisation were associated with reductions in outdoor-related crime in designated development areas. Across all specifications, the results consistently indicate that these interventions were not associated with statistically significant changes in reported crime relative to comparable control areas. This finding holds across alternative control groups, treatment definitions, and exploratory displacement analyses. Importantly, the absence of detectable effects should not be interpreted as evidence that situational crime prevention is ineffective per se. Rather, the findings point to the importance of aligning intervention logic with the specific crime mechanisms operating (Taylor, 2002). As emphasised within SCP and realist evaluation perspectives, preventive outcomes depend not only on the presence of interventions, but on how well they target the situational conditions that facilitate particular forms of offending (Clarke, 1997; Tilley, 2000). One key explanation for the observed null results relates to the nature of outdoor environments. Compared to indoor or semi-private spaces, outdoor residential areas are generally more open-access, have fluid movement patterns, and limited control over who enters and exits the space. These features may constrain the potential impact of environmental modifications aimed at increasing guardianship or reducing opportunities (Armitage, 2018). Even when lighting, maintenance, or access control measures are improved locally, offenders may adapt by shifting activities spatially or temporally within or beyond the treated area. Although the descriptive displacement analysis provided no indication that crime increased in nearby locations, the overall patterns suggest that crime trends were largely shaped by broader temporal dynamics. A second explanation concerns the types of crime included in the analysis. Most recorded offences in the outcome measure consist of vandalism-related incidents, alongside smaller numbers of property and violent crimes. From a situational perspective, vandalism such as graffiti presents low barriers to offending and increased target substitutability (Gerell, 2021). Such offences may be less responsive to environmental interventions that are not narrowly targeted or intensively enforced. Similarly, violent crimes occurring in outdoor settings may be driven by interpersonal dynamics or situational conflicts that are only weakly influenced by changes in the physical environment alone (Cozens & Love, 2015). The breadth and multi-purpose nature of the studied intervention regime further complicates interpretation. The outdoor interventions evaluated in this study were implemented as part of a comprehensive housing improvement programme pursuing multiple objectives (Framtiden, 2020). Crime reduction constituted one goal among several, but interventions were not explicitly designed to address specific crime problems or offender behaviours. This may reflect a mismatch between preventive intentions and criminogenic mechanisms rather than an absence of preventive potential. The findings resonate with previous evaluations of situational- and environmental crime prevention in outdoor settings, which frequently report heterogeneous and context-dependent effects (Tilley, 2000; Cozens & Love, 2015). Studies demonstrating positive impacts often focus on narrowly defined interventions implemented at clearly delineated locations and targeting specific crime types (e.g., Piza et al., 2019; Welsh et al., 2022). From a community safety and governance perspective, the results highlight the challenges faced by public housing organisations acting as crime prevention agents. While housing providers are uniquely positioned to influence environmental conditions at scale, they operate within complex organisational and political contexts where crime prevention must be balanced against competing priorities. Limitations and implications Several limitations should be considered when interpreting the findings. First, intervention data are derived from internal documentation, and it is possible that some interventions were implemented but not recorded. To address this concern, robustness checks were conducted using expanded treatment definitions and alternative control groups, yielding substantively similar results. Second, the analysis relies exclusively on police-recorded crime data. While reported crime constitutes a central outcome for evaluating situational prevention, it captures only a subset of potential intervention impacts. Improvements in perceived safety, residents’ use of outdoor spaces, or informal social control may have occurred without translating into measurable changes in recorded crime. Although such outcomes fall outside the scope of the present study, they are relevant for assessing the broader value of crime prevention strategies. Third, the generalisability of the findings is limited by the specific institutional and social context of Gothenburg’s public housing system. The concentration of public housing ownership, the designation of development areas, and the welfare-state context differ from housing arrangements in many other countries. The results should therefore be interpreted as contextually grounded rather than universally generalisable. For practitioners, the findings suggest that broad, multi-purpose environmental improvement programmes should not be assumed to produce measurable crime reductions in outdoor residential environments unless aligned with clearly defined crime problems. For researchers, the results underscore the importance of evaluating real-world prevention initiatives as they are implemented in practice, including those that produce null effects. Conclusion This study evaluated the effects of outdoor environmental interventions implemented by a municipal public housing organisation on outdoor-related crime in residential development areas. Using a staggered difference-in-differences design and multiple robustness checks, the analysis found no statistically significant evidence that these interventions reduced reported crime relative to comparable control areas. Rather than undermining situational crime prevention as a preventive approach, the findings highlight the importance of context. Outdoor residential environments present particular challenges for situational intervention, especially when interventions are broad in scope and pursue multiple objectives beyond crime reduction. By examining a large-scale, real-world intervention strategy implemented by a non-policing actor, this study contributes to the growing literature on community safety governance and environmental crime prevention. It demonstrates both the potential and the limits of housing-led situational prevention and underscores the value of rigorous evaluation in advancing evidence-based crime prevention policy. Declarations Funding declaration The author did not receive funding for this work. Human ethics Consent to Participate Not applicable Ethics approval The research project on which this article is based was reviewed and approved by the Swedish Ethical Review Authority (Etikprövningsnämnden), reference number 2023-06069-01. Author Contribution J.W. conceived and designed the study, collected and curated the data, conducted the statistical analyses, interpreted the results, and wrote the manuscript. J.W. prepared all tables and figures and approved the final version of the manuscript. Acknowledgement I would like to thank Manne Gerell, Charlotta Thodelius and Alberto Chrysoulakis for their helpful comments on earlier versions of this manuscript. I am also grateful to Lisa Persson and to representatives at AB Framtiden for providing access to crime data and intervention records. 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Demographic overview Development areas Mean (min–max) Rest of Gothenburg Low economic standard (%) 34.7 (27.5–44.4) 12.1 Foreign background (%) 81.9 (67.7–90.7) 30.0 Male (%) 51.7 (49.8–52.6) 50.0 Unemployment (%) 21.0 (16.8–24.6) 7.5 Rental housing (%) 79.5 (72.4–91.4) 50.2 Multi-dwelling housing (%) 91.1 (81.0–95.3) 71.6 Overcrowding (%) 32.7 (19.8–44.5) 14.1 Total population 85 954 486 036 Notes. Definitions follow Statistics Sweden. Values are 2020–2023 averages; ranges indicate variation across development areas. Source: Statistics Sweden; author’s calculations. Table 2. Treatment types, contexts and address-intervention rows in dataset. Intervention class Unique interventions (IDs) Address–intervention rows Unique classes Context Maintenance Management 45 1,087 Cleaning & sanitation, Greening & planting, Maintenance & repair, Painting & refurbishment, Seating & outdoor furniture, Staircase, Surface & ground works, Vegetation management, Walls, fences & railings parking, passage, yard Natural Surveillance 43 1,510 Community gardens, Lighting, Vegetation management parking, yard Access Control 16 229 Access barriers & gates, Locks & target hardening, Walls, fences & railings parking, walkway, yard Activity Support 15 406 Community gardens, Outdoor fitness, Playgrounds & recreational areas, Seating & outdoor furniture, Waste infrastructure yard Note. Intervention labels were harmonised into canonical categories for analytical clarity. Address–intervention rows denote address × intervention records. Table 3. Descriptive distribution of outdoor crime by type and area (2019–2023) Area Year Addresses (included) Total (outdoor) Robbery Assault Vehicle-related Outdoor vandalism Rate / 1,000 addr. Development areas 2019 2 041 366 11 46 171 77 179.3 2020 611 6 45 188 283 299.4 2021 677 6 49 164 346 331.7 2022 644 9 47 165 352 315.5 2023 491 6 35 136 244 240.6 Same neighbourhoods (excl. dev) 2019 5 394 504 10 47 251 101 93.4 2020 672 13 55 260 196 124.6 2021 682 13 49 283 219 126.4 2022 494 9 63 206 157 91.6 2023 546 7 51 220 183 101.2 Rest of city 2019 101 868 21 913 808 1 931 6 893 8 496 215.1 2020 25 833 746 1 827 6 419 12 017 253.6 2021 21 698 591 1 739 5 882 9 447 213.0 2022 19 113 510 1 947 4 923 10 179 187.6 2023 24 075 548 1 891 6 252 11 090 236.3 Notes. Values show yearly outdoor crime totals and rates per 1,000 addresses. Development areas refer to designated treatment areas; Same neighbourhoods (excl. dev) are primary areas containing development areas but excluding treated addresses. Address counts represent the number of unique addresses included in each group. Data cover 2019–2023 and are compiled by the author from police-recorded crime data Table 4. Main models Model N addresses N addr-months ATT Std. Error 95% CI Model A: Within development areas 2,041 122,460 -0.007 0.011 [-0.027, 0.014] Model B: External controls 10,301 618,060 -0.007 0.010 [-0.027, 0.013] Notes: Group-aggregated ATT estimates from a staggered difference-in-differences design. Address-level panel data (2019–2023). Standard errors clustered at the address level; 95% confidence intervals based on normal approximation. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx FigureA1LeaveOneOut.jpg Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 11 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 22 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8940567","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604660639,"identity":"9c9f8c1b-8cf8-48de-9a06-b48627729ca8","order_by":0,"name":"Jens Widmark","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie2QsQrCMBCGEw6cCq63FF9BCYgO+iyGQlzqEwiaqV0srt18AtebhQwu1a6OguDkkNVFjJ2com4O+YYchPty/4WxQOAP4bop+KrA7M8KL3+dBhB90wp5YSzPBvE2P1zEKB1L3TY7/+uro0KeoaBqKpIZJVKjmviVMu0zTihpp8DMCJwSdf3K5iZso9RXMENaumC1/+N4GXWxUU4KEk5GapZ6DbdL2kf5cLucrtAraC8yVP5gvbwS1laLmGoFeKd5vG6bs1/R7pi837T8sRjrfGoIBAKBAHsChWZGfFJhKAAAAAAASUVORK5CYII=","orcid":"","institution":"Malmö University","correspondingAuthor":true,"prefix":"","firstName":"Jens","middleName":"","lastName":"Widmark","suffix":""}],"badges":[],"createdAt":"2026-02-22 17:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8940567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8940567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104578721,"identity":"7ad4a8e7-fa20-4739-a200-08d49fb07c89","added_by":"auto","created_at":"2026-03-13 14:35:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68366,"visible":true,"origin":"","legend":"\u003cp\u003eGroup-time effects of outdoor interventions using remainder of treatment neighbourhoods as control group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8940567/v1/e23bf4a4eb8f23f5b59e0116.png"},{"id":104578724,"identity":"fbfdea61-6e0a-4a59-8d38-653623a6a3e1","added_by":"auto","created_at":"2026-03-13 14:35:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62867,"visible":true,"origin":"","legend":"\u003cp\u003eGroup-time effect measures of outdoor interventions using separated public housing neighbourhoods as control group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8940567/v1/31300f41dfc2577bbaa63a86.png"},{"id":104578723,"identity":"89f462b7-20ea-4933-98e8-68d214279a78","added_by":"auto","created_at":"2026-03-13 14:35:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110400,"visible":true,"origin":"","legend":"\u003cp\u003eIndexed outdoor crime trends (2019 = 100) for treated addresses, buffer zones (0–200 m and 200–500 m), and control addresses, 2019–2023.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8940567/v1/a70067705fa8f7b6f6e6ded9.png"},{"id":104835201,"identity":"73c82de9-ee4f-40cb-a811-2b619abbfe47","added_by":"auto","created_at":"2026-03-17 17:42:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8940567/v1/47617492-7aee-42d1-b45c-75f45368b208.pdf"},{"id":104578722,"identity":"ccd8a9b1-d386-4792-83a9-0caead624cb6","added_by":"auto","created_at":"2026-03-13 14:35:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16968,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8940567/v1/fda4eba912bed8859303f63d.docx"},{"id":104578725,"identity":"7a6bd16f-c453-4a6e-9106-8d2ac4aa9049","added_by":"auto","created_at":"2026-03-13 14:35:00","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":539401,"visible":true,"origin":"","legend":"","description":"","filename":"FigureA1LeaveOneOut.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8940567/v1/ac6c994757cc7d11dc1ae4cd.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating the Effects of Outdoor Crime Prevention Interventions Using a Staggered Difference-in-Differences Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSituational crime prevention (SCP) in residential areas has long been promoted as a pragmatic and opportunity-focused approach to reducing crime and improving perceptions of safety (Clarke, 1997; Clarke \u0026amp; Bowers, 2017). Rather than addressing underlying social causes of offending, SCP targets the immediate conditions under which crime occurs by altering situational cues, increasing perceived effort or risk, and reducing accessibility to suitable targets (Clarke, 1997; Shariati \u0026amp; Guerette, 2017). Within the SCP framework, manipulation of environmental conditions is one prevention strategy that aims to reduce opportunities for crime, fear, and offender motivation. Environmental interventions have been described as effective and relatively low-cost tools for improving safety (Welsh \u0026amp; Farrington, 1999). At the same time, empirical evaluations report variable outcomes across settings and intervention types (Eck et al., 2009; Bowers \u0026amp; Johnson, 2016), including substantial reductions and more limited or statistically indistinct changes relative to broader crime trends (e.g., Kondo et al., 2016; Piza et al., 2019; Welsh \u0026amp; Farrington, 2009; Widmark, 2026).\u003c/p\u003e\n\u003cp\u003eThis study examines area-specific crime prevention initiatives implemented in residential areas designated as \u0026ldquo;vulnerable\u0026rdquo; by the Swedish police in Gothenburg, Sweden (Swedish Police, 2025). The interventions were implemented by the municipal public housing organisation and targeted outdoor residential environments as part of a broader community improvement strategy (Framtiden, 2020). Building on an earlier evaluation conducted within the same strategic programme (Author, 2025), the present study assesses whether outdoor environmental measures associated with SCP principles were followed by changes in outdoor-related crime.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study seeks to contribute to the broader literature on situational crime prevention and community safety. In doing so, it responds to calls for more rigorous evaluations of real-world prevention initiatives implemented by non-policing actors, such as public housing providers, operating within complex social and organisational contexts (Crawford \u0026amp; Evans, 2017). The research question guiding the analysis is: Are the housing company-led outdoor environmental interventions in disadvantaged residential areas associated with reductions in outdoor-related crime, relative to comparable non-treated areas?\u003c/p\u003e\n\u003ch3\u003eSituational crime prevention and the intervention logic\u003c/h3\u003e\n\u003cp\u003eSCP is based on the premise that crime can be prevented by reducing opportunities and increasing the perceived risks or efforts associated with offending (Clarke, 1997). Related to this notion are opportunity-based theories, including routine activity theory, crime pattern theory, and rational choice perspectives that describe how criminal events arise through the convergence of motivated offenders, suitable targets, and insufficient guardianship and are shaped by everyday movement patterns and environmental design (Brantingham \u0026amp; Brantingham, 2008; Clarke \u0026amp; Bowers, 2017; Cohen \u0026amp; Felson, 1979). Within residential environments, these theoretical perspectives have frequently been operationalised through Crime Prevention Through Environmental Design (CPTED)-inspired interventions. Such interventions are typically focused on the physical environment and aim to enhance natural surveillance, improve access control, strengthen territorial reinforcement, and maintain positive images of place (Schneider, 2005). The principles often overlap, and single interventions may simultaneously address multiple preventive mechanisms\u0026nbsp;(Cozens et al., 2005). Broad or loosely defined interventions may fail to influence the situational mechanisms that give rise to particular crime types, even when implemented with good intentions and substantial resources (Braga, 2008; Taylor, 2002). Importantly, CPTED interventions on environmental changes in residential areas have shown differing efficiency, both in terms of intervention types and for type of crime (Cozens \u0026amp; Love, 2015; Crowe, 2000; Widmark, 2026).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom a community safety perspective, SCP has been embedded within broader governance frameworks, where responsibility for crime prevention extends beyond the police to include local authorities, housing providers, and other non-policing actors (Clarke, 1997). An increased focus on local governance and involvement of multi-stakeholder initiatives in place-based environmental crime prevention solutions have emerged within Swedish policy on community safety (Lidskog \u0026amp; Persson, 2012). Thus, the local public housing organisation of Gothenburg has included SCP initiatives in their local governance of housing stock in general, and in vulnerable areas in particular (Framtiden, 2020). This multi-purpose nature of housing-led interventions raises important questions for evaluation. When interventions are not explicitly designed to address specific crime mechanisms, observed outcomes may reflect a mismatch between preventive intentions and criminogenic realities rather than a failure of situational prevention per se.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStudy Setting\u003c/h3\u003e\n\u003cp\u003eThe interventions evaluated in this study were implemented in Gothenburg, Sweden\u0026rsquo;s second-largest city, located on the western coast of the country and home to approximately 600,000 inhabitants. Within the city, the Swedish Police Authority has designated a number of neighbourhoods as vulnerable, a classification used to describe areas characterised by elevated crime levels, reduced trust in public institutions and the presence of organised criminal networks (Swedish Police, 2023). These police-defined vulnerable areas form the broader spatial and institutional context within which the interventions studied here were implemented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe focus on this study is on a subset of these neighbourhoods, identified by the municipal public housing organisation as \u0026ldquo;development areas.\u0026rdquo; These areas constitute smaller spatial units nested within vulnerable neighbourhoods and consist primarily of multi-dwelling residential buildings owned and managed by the public housing providers. The designation of development areas reflects an organisational strategy aimed at concentrating resources and interventions within particularly prioritized parts of the housing stock (Framtiden, 2020). Depending on neighbourhood, the municipal housing organisation owns and manages between approximately 50 and 90 per cent of all residential units. Much of the housing stock was constructed during the large-scale national housing programme of the 1960s and 1970s (Hall \u0026amp; Vid\u0026eacute;n, 2005).\u003c/p\u003e\n\u003cp\u003eAs a part of a long-term social investment strategy initiated in 2020, the public housing organisation allocated resources to improving living conditions and safety in development areas over a ten-year period (Framtiden, 2020). While crime prevention constituted one stated objective of this strategy, interventions were also motivated by broader goals related to maintenance, functionality, attractiveness, and residents\u0026rsquo; quality of life. Compared to the rest of the city, these areas differ markedly in socio-demographic composition. As shown in Table 1, these areas are characterised by higher levels of economic disadvantage, unemployment, and overcrowding, as well as a substantially larger proportion of residents with foreign backgrounds. Housing tenure is dominated by rental units, and the built environment consists of large shares of multi-dwelling housing.\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Table 1 about here \u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eThe concentration of public housing ownership enables the housing provider to implement environmental interventions at scale but also means that interventions are introduced in areas already facing multiple social and structural challenges. It is an illustration of how environmental crime prevention initiatives can be embedded within broader systems of urban governance and welfare provision. The interventions evaluated in this study should therefore be understood not as isolated crime control measures, but as part of a multi-purpose housing strategy operating in socially and economically disadvantaged residential contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outdoor interventions examined in this study comprised a broad range of physical and environmental modifications implemented across the designated development areas. For analytical purposes, individual intervention records were harmonised into four overarching classes derived from established CPTED principles: Maintenance Management, Natural Surveillance, Access Control, and Activity Support (see Table 2).\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Table 2 about here \u0026gt;\u0026gt;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom an SCP perspective, the interventions display both mechanism-specific and multi-purpose characteristics. While certain measures (e.g., locks, barriers, lighting) correspond closely to clearly defined situational mechanisms, others blend maintenance, aesthetic, and community-oriented objectives. This theoretical heterogeneity is analytically relevant, as it may influence the likelihood of observing measurable crime reductions in aggregate outcome measures.\u0026nbsp;\u003c/p\u003e"},{"header":"Data and methods","content":"\u003cp\u003eThis study draws on police-recorded crime data covering offences in Gothenburg between January 2019 and December 2023. Crime records were geocoded and linked to residential addresses managed by the municipal public housing organisation. The unit of analysis is the address-month, enabling the construction of a balanced panel capturing temporal variation in crime and intervention status across locations. The outcome measure consists of reported outdoor-related crimes, including assaults, robberies, thefts of and from bicycles and motor vehicles, and vandalism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformation on outdoor interventions was obtained from the public housing organisation through quarterly check-ins throughout the study period (2021-2023). Each intervention record includes the type of measure, the addresses affected by interventions, and the date of initiation. Given the diversity and scale of interventions, treatment is defined at the address level as the initiation of any documented outdoor intervention. To account for potential uncertainty in intervention documentation, robustness checks were conducted using alternative treatment definitions, including expanded treatment areas intended to capture possible undocumented interventions or broader exposure to the intervention strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe causal effect of outdoor interventions is estimated using a difference-in-differences framework designed for staggered treatment adoption across units and time. Unlike conventional two-way fixed effects models, this approach allows for treatment effects to vary across cohorts and avoids bias arising from inappropriate comparisons between early- and late-treated units (Callaway \u0026amp; Sant\u0026rsquo;Anna, 2021). The identifying assumption is that absent intervention, treated and control addresses would have followed similar trends in outdoor crime. This assumption is assessed through pre-treatment comparisons and event-study estimates.\u003c/p\u003e\n\u003cp\u003eThe primary control group consists of public housing addresses owned by the same municipal housing organisation and located in the same broader neighbourhood context as the treated development areas, but not designated for concentrated intervention during the study period. The comparison therefore contrasts addresses exposed to the development-area strategy with other housing units operating under the same ownership and neighbourhood conditions. Because treated and control addresses share the same broader local environment, the estimated effects capture the marginal impact of the concentrated intervention strategy rather than general neighbourhood-level change. As a robustness check, alternative specifications use public housing addresses located outside the vulnerable neighbourhood context as controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation and inference\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTreatment effects are reported as average treatment effects on the treated (ATT), estimated across cohorts and time periods. Standard errors are clustered at the address level to account for serial correlation in crime outcomes (Bertrand et al., 2004). Furthermore, the analysis estimates the average effect of exposure to any documented outdoor intervention, rather than intervention-specific effects.\u003c/p\u003e\n\u003cp\u003eTo examine potential spatial displacement or diffusion of crime, a supplementary analysis was conducted comparing crime trends in treated areas with those in adjacent non-treated areas. Following prior research on situational crime prevention and spatial spillovers (e.g., Johnson et al., 2014), crime incidents were examined in concentric buffer zones surrounding treated locations. Treated addresses were grouped into spatial clusters using a density-based algorithm and mutually exclusive buffer zones were constructed at distances of 0-200 metres and 200-500 metres from each treated cluster using DBSCAN (Hahsler et al., 2019). Annual counts of outdoor-related crimes were calculated for treated addresses, buffer zones, and control addresses (Appendix Table A2). Because of the staggered timing of interventions and the aggregation of spatial clusters, the displacement analysis is descriptive and intended to identify broad patterns rather than estimate causal spillover effects.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 3 presents the yearly distribution of outdoor-related crimes across development areas, comparison areas within the same broader neighbourhood context, and the rest of the city between 2019 and 2023. Development areas include all public housing addresses designated for intervention, regardless of specific intervention type or timing. Same neighbourhoods (excluding development areas) include public housing addresses located in the same broader neighbourhood context but not designated for intervention.\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Table 3 about here \u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eAcross the study period, development areas exhibit consistently higher crime rates per 1,000 addresses compared to the same neighbourhood comparison areas. The descriptive pattern indicates substantial temporal fluctuation in crime levels across all areas, particularly around 2020-2021, suggesting that broader city-level dynamics affect both treated and comparison areas during the study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall effects of outdoor interventions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 presents the event-study estimates of group-time average treatment effects for outdoor environmental interventions, using public housing addresses located within the same police-defined vulnerable neighbourhoods but outside the designated development areas as the primary control group. The estimates show no statistically significant reduction in outdoor-related crime following the implementation of interventions. While the post period point estimates do tend to more often be negative, they are close to zero and with confidence intervals consistently overlap the null. No systematic divergence between treated and control addresses is observed prior to intervention, providing no evidence of differential pre-treatment trends.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Figure 1 about here \u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eTo assess the sensitivity to control group selection and potential spillover effects, an alternative specification was estimated using public housing addresses located outside the police-defined vulnerable neighbourhoods as controls. Event-study estimates for this specification are presented in Figure 2. Consistent with the main analysis, no statistically significant post-intervention effects are observed. Estimated treatment effects remain close to zero across post-intervention periods, and confidence intervals largely overlap with those from the primary specification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Figure 2 about here \u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAverage treatment effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the event-study plots provide a dynamic view of treatment effects over time, Table 4 summarises the estimated average treatment effects on the treated (ATT) across post-intervention periods for the main and robustness specifications. Across specifications, average treatment effects are close to zero and statistically indistinguishable from zero (ATT: -.007, 95% CI [-.027, .014]). As the outcome is measured in monthly crimes per address, the estimated ATT corresponds to a reduction of approximately 84 crimes per 1,000 addresses annually. The robustness specification using external controls yields nearly identical estimates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese aggregated estimates reinforce the conclusion drawn from the event-study analyses: outdoor environmental interventions, as implemented within the development areas, were not associated with detectable reductions in reported outdoor-related crime relative to control areas.\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Table 4 about here \u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eAlternative models based on an intention-to-treat (ITT) framework were estimated using the broader policy exposure definition (Model B), assuming policy-level exposure for all addresses located within development areas and comparing them to public housing addresses not connected to the policy. When defining treatment onset as September 2020, corresponding to the first recorded outdoor intervention, no statistically significant effect is observed (ATT = \u0026minus;0.0043, 95% CI [\u0026minus;0.0257, 0.017]). In contrast, defining policy onset as early 2020, corresponding to the official policy launch, produces a small but statistically significant \u003cem\u003eincrease\u003c/em\u003e in outdoor crime (ATT = 0.0167, 95% CI [0.0058, 0.0276]). This sensitivity to treatment-policy timing suggests that early 2020 dynamics, rather than intervention exposure, account for the positive estimate under the earlier onset definition. Leave-one-out robustness analyses were conducted for both models by iteratively excluding one treated neighbourhood at a time, yielding substantively similar estimates across all specifications (Appendix Figure A1 and Appendix Table A3).\u003c/p\u003e\n\u003cp\u003eTo examine whether aggregate results mask heterogeneous effects across crime categories, additional models were estimated separately for robbery, assault, vehicle-related crime, and outdoor vandalism (Appendix Table A1). For assault, vehicle-related crime, and vandalism, estimated treatment effects were small in magnitude and statistically indistinguishable from zero. A small but significant positive effect was observed for robbery (ATT = 0.0007, p \u0026lt; 0.001). Given the low base rate of robbery and the absence of consistent effects across related crime categories, this isolated estimate should be interpreted with caution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisplacement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional descriptive analyses examined whether the absence of crime reductions in treated areas could reflect spatial displacement or diffusion of benefits to adjacent areas. Figure 3 presents indexed crime trends (2019 = 100) for treated addresses, buffer zones (0\u0026ndash;200 metres and 200\u0026ndash;500 metres), and control addresses. General declines are observed in 2022 across all defined zones. Importantly, no sustained divergence between treated and surrounding buffer zones is observed following intervention. In particular, buffer zones do not display consistent post-intervention increases relative to treated areas that would suggest systematic displacement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt; Figure 3 about here \u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eBecause the displacement analysis relies on aggregated spatial clusters and staggered intervention timing, it is descriptive rather than causal in nature. Annual raw crime counts underlying the indexed trends are reported in Appendix Table A2.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study set out to evaluate whether outdoor environmental interventions implemented by a municipal public housing organisation were associated with reductions in outdoor-related crime in designated development areas. Across all specifications, the results consistently indicate that these interventions were not associated with statistically significant changes in reported crime relative to comparable control areas. This finding holds across alternative control groups, treatment definitions, and exploratory displacement analyses.\u003c/p\u003e\n\u003cp\u003eImportantly, the absence of detectable effects should not be interpreted as evidence that situational crime prevention is ineffective per se. Rather, the findings point to the importance of aligning intervention logic with the specific crime mechanisms operating (Taylor, 2002). As emphasised within SCP and realist evaluation perspectives, preventive outcomes depend not only on the presence of interventions, but on how well they target the situational conditions that facilitate particular forms of offending (Clarke, 1997; Tilley, 2000).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne key explanation for the observed null results relates to the nature of outdoor environments. Compared to indoor or semi-private spaces, outdoor residential areas are generally more open-access, have fluid movement patterns, and limited control over who enters and exits the space. These features may constrain the potential impact of environmental modifications aimed at increasing guardianship or reducing opportunities (Armitage, 2018). Even when lighting, maintenance, or access control measures are improved locally, offenders may adapt by shifting activities spatially or temporally within or beyond the treated area. Although the descriptive displacement analysis provided no indication that crime increased in nearby locations, the overall patterns suggest that crime trends were largely shaped by broader temporal dynamics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA second explanation concerns the types of crime included in the analysis. Most recorded offences in the outcome measure consist of vandalism-related incidents, alongside smaller numbers of property and violent crimes. From a situational perspective, vandalism such as graffiti presents low barriers to offending and increased target substitutability (Gerell, 2021). Such offences may be less responsive to environmental interventions that are not narrowly targeted or intensively enforced. Similarly, violent crimes occurring in outdoor settings may be driven by interpersonal dynamics or situational conflicts that are only weakly influenced by changes in the physical environment alone (Cozens \u0026amp; Love, 2015).\u003c/p\u003e\n\u003cp\u003eThe breadth and multi-purpose nature of the studied intervention regime further complicates interpretation. The outdoor interventions evaluated in this study were implemented as part of a comprehensive housing improvement programme pursuing multiple objectives (Framtiden, 2020). Crime reduction constituted one goal among several, but interventions were not explicitly designed to address specific crime problems or offender behaviours. This may reflect a mismatch between preventive intentions and criminogenic mechanisms rather than an absence of preventive potential.\u003c/p\u003e\n\u003cp\u003eThe findings resonate with previous evaluations of situational- and environmental crime prevention in outdoor settings, which frequently report heterogeneous and context-dependent effects (Tilley, 2000; Cozens \u0026amp; Love, 2015). Studies demonstrating positive impacts often focus on narrowly defined interventions implemented at clearly delineated locations and targeting specific crime types (e.g., Piza et al., 2019; Welsh et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom a community safety and governance perspective, the results highlight the challenges faced by public housing organisations acting as crime prevention agents. While housing providers are uniquely positioned to influence environmental conditions at scale, they operate within complex organisational and political contexts where crime prevention must be balanced against competing priorities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting the findings. First, intervention data are derived from internal documentation, and it is possible that some interventions were implemented but not recorded. To address this concern, robustness checks were conducted using expanded treatment definitions and alternative control groups, yielding substantively similar results. Second, the analysis relies exclusively on police-recorded crime data. While reported crime constitutes a central outcome for evaluating situational prevention, it captures only a subset of potential intervention impacts. Improvements in perceived safety, residents\u0026rsquo; use of outdoor spaces, or informal social control may have occurred without translating into measurable changes in recorded crime. Although such outcomes fall outside the scope of the present study, they are relevant for assessing the broader value of crime prevention strategies. Third, the generalisability of the findings is limited by the specific institutional and social context of Gothenburg\u0026rsquo;s public housing system. The concentration of public housing ownership, the designation of development areas, and the welfare-state context differ from housing arrangements in many other countries. The results should therefore be interpreted as contextually grounded rather than universally generalisable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor practitioners, the findings suggest that broad, multi-purpose environmental improvement programmes should not be assumed to produce measurable crime reductions in outdoor residential environments unless aligned with clearly defined crime problems. For researchers, the results underscore the importance of evaluating real-world prevention initiatives as they are implemented in practice, including those that produce null effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study evaluated the effects of outdoor environmental interventions implemented by a municipal public housing organisation on outdoor-related crime in residential development areas. Using a staggered difference-in-differences design and multiple robustness checks, the analysis found no statistically significant evidence that these interventions reduced reported crime relative to comparable control areas.\u003c/p\u003e\n\u003cp\u003eRather than undermining situational crime prevention as a preventive approach, the findings highlight the importance of context. Outdoor residential environments present particular challenges for situational intervention, especially when interventions are broad in scope and pursue multiple objectives beyond crime reduction. By examining a large-scale, real-world intervention strategy implemented by a non-policing actor, this study contributes to the growing literature on community safety governance and environmental crime prevention. It demonstrates both the potential and the limits of housing-led situational prevention and underscores the value of rigorous evaluation in advancing evidence-based crime prevention policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding declaration\u003c/h2\u003e\n\u003cp\u003eThe author did not receive funding for this work.\u003c/p\u003e\n\u003ch2\u003eHuman ethics Consent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThe research project on which this article is based was reviewed and approved by the Swedish Ethical Review Authority (Etikpr\u0026ouml;vningsn\u0026auml;mnden), reference number 2023-06069-01.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJ.W. conceived and designed the study, collected and curated the data, conducted the statistical analyses, interpreted the results, and wrote the manuscript. J.W. prepared all tables and figures and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eI would like to thank Manne Gerell, Charlotta Thodelius and Alberto Chrysoulakis for their helpful comments on earlier versions of this manuscript. I am also grateful to Lisa Persson and to representatives at AB Framtiden for providing access to crime data and intervention records.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available due to legal and ethical restrictions but may be available from the author upon reasonable request and with permission from the data providers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAuthor (2025). [Details omitted for blind review].\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArmitage, R. (2018). Burglars\u0026apos; take on crime prevention through environmental design (CPTED): reconsidering the relevance from an offender perspective. \u003cem\u003eSecurity Journal\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e31, No. 1, pp 285\u0026ndash;304.\u003c/li\u003e\n \u003cli\u003eBertrand, M., Duflo, E., \u0026amp; Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? \u003cem\u003eThe Quarterly Journal of Economics\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e119, No. 1, pp 249\u0026ndash;275.\u003c/li\u003e\n \u003cli\u003eBowers, K. J., \u0026amp; Johnson, S. D. (2016). Situational Prevention. In D. Weisburd, D. Farrington, \u0026amp; C. Gill, \u003cem\u003eWhat Works in Crime Prevention and Rehabilitation\u003c/em\u003e (pp. 111\u0026ndash;135). New York: Springer.\u003c/li\u003e\n \u003cli\u003eBraga, A. A. (2008). \u003cem\u003eProblem-oriented policing and crime prevention\u003c/em\u003e (Vol. 2). 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Sidebottom, \u003cem\u003eHandbook of crime prevention and community safety\u003c/em\u003e (2nd ed., pp. 109\u0026ndash;142). London: Routledge.\u003c/li\u003e\n \u003cli\u003eCohen, L. E., \u0026amp; Felson, M. (1979). Social change and crime rate trends: a routine activity. \u003cem\u003eAmerican Sociological Review\u003c/em\u003e. Vol. 44, pp 588\u0026ndash;608.\u003c/li\u003e\n \u003cli\u003eCozens, P. M., Saville, G., \u0026amp; Hillier, D. (2005). Crime Prevention Through Environmental Design (CPTED): A Review and Modern Bibliography. \u003cem\u003eJournal of Property Management\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e23, No. 5, pp 328\u0026ndash;356.\u003c/li\u003e\n \u003cli\u003eCozens, P., \u0026amp; Love, T. (2015). A Review and Current Status of Crime Prevention through Environmental Design (CPTED). \u003cem\u003eJournal of Planning Literature\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e30, No. 4, pp 393\u0026ndash;412.\u003c/li\u003e\n \u003cli\u003eCrawford, A., \u0026amp; Evans, K. (2017). Crime Prevention and Community Safety. In A. Liebling, \u003cem\u003eThe Oxford Handbook of Criminology\u003c/em\u003e (pp. 797\u0026ndash;824). Oxford University Press.\u003c/li\u003e\n \u003cli\u003eCrowe, T. (2000). \u003cem\u003eCrime Prevention Through Environmental Design: Applications of Architectural Design and Space Management Concepts\u003c/em\u003e (2nd ed.). Oxford: Butterworth-Heinemann.\u003c/li\u003e\n \u003cli\u003eEck, J. E., Madensen, T., Payne, T., Wilcox, P., Fischer, B. S., \u0026amp; Scherer, H. (2009). \u003cem\u003eSituational crime prevention at specific locations in community context: Place and neighborhood effects.\u003c/em\u003e US Department of Justice.\u003c/li\u003e\n \u003cli\u003eFramtiden. (2020). \u003cem\u003eFramtidskoncernes strategi f\u0026ouml;r utvecklingsomr\u0026aring;den 2020-2030.\u003c/em\u003e G\u0026ouml;teborg: AB Framtiden.\u003c/li\u003e\n \u003cli\u003eGerell, M. (2021). Does the Association Between Flows of People and Crime Differ Across Crime Types in Sweden? \u003cem\u003eEuropean Journal on Criminal Policy and Research.\u003c/em\u003e Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e27, pp 433\u0026ndash;449.\u003c/li\u003e\n \u003cli\u003eHahsler, M., Piekenbrock, M., \u0026amp; Doran, D. (2019). dbscan: Fast Density-Based Clustering with R. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e91, No. 1, pp 1\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eHall, T., \u0026amp; Vid\u0026eacute;n, S. (2005). The Million Homes Programme: a review of the great Swedish planning project. \u003cem\u003ePlanning Perspectives\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e20, No. 3, pp 301\u0026ndash;328.\u003c/li\u003e\n \u003cli\u003eJohnson, S. D., Guerette, R. T., \u0026amp; Bowers, K. (2014). Crime displacement: What we know, what we don\u0026apos;t know, and what it means for crime reduction. \u003cem\u003eJournal of Experimental Criminology\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e10, No. 4, pp 549\u0026ndash;571.\u003c/li\u003e\n \u003cli\u003eKondo, M., Hohl, B., Han, S., \u0026amp; Branas, C. (2016). Effects of greening and community reuse of vacant lots on crime. \u003cem\u003eUrban Studies\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e53, No. 15, pp 3279\u0026ndash;3295.\u003c/li\u003e\n \u003cli\u003eLidskog, R., \u0026amp; Persson, M. (2012). Community Safety Policies in Sweden. A Policy Change in Crime Control Strategies? \u003cem\u003eInternational Journal of Public Administration\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e35, No. 5, pp 293\u0026ndash;302.\u003c/li\u003e\n \u003cli\u003ePiza, E. L., Welsh, B. C., Farrington, D. P., \u0026amp; Thomas, A. L. (2019). CCTV Surveillance For Crime Prevention. \u003cem\u003eCriminology \u0026amp; Public Policy\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e18, No. 1, pp 135\u0026ndash;159.\u003c/li\u003e\n \u003cli\u003eSchneider, R. H. (2005). Introduction: Crime Prevention Through Environmental Design (CPTED): Themes, Theories, Practice, and Conflict. \u003cem\u003eJournal of Architectural and Planning Research\u003c/em\u003e. Vol. \u003cem\u003e\u0026nbsp;\u003c/em\u003e22, No. 4, pp 271\u0026ndash;283.\u003c/li\u003e\n \u003cli\u003eShariati, A., \u0026amp; Guerette, R. T. (2017). Situational crime prevention. In B. Teasdale, \u0026amp; M. Bradley, \u003cem\u003ePreventing Crime and Violence: Advances in Prevention Science\u003c/em\u003e (pp. 261\u0026ndash;268). Cham: Springer.\u003c/li\u003e\n \u003cli\u003eSwedish Police. (2023). \u003cem\u003eL\u0026auml;gesbild \u0026ouml;ver utsatta omr\u0026aring;den 2023.\u003c/em\u003e https://polisen.se/om-polisen/polisens-arbete/utsatta-omraden/\u003c/li\u003e\n \u003cli\u003eSwedish Police. (2025). \u003cem\u003eL\u0026auml;gesbild \u0026ouml;ver utsatta omr\u0026aring;den.\u003c/em\u003e Stockholm: Polismyndigheten, Nationella operativa avdelningen.\u003c/li\u003e\n \u003cli\u003eTaylor, R. B. (2002). Crime Prevention Through Environmental Design (CPTED): Yes, no, maybe, unknowable, and all of the above. In R. B. Bechtel, \u003cem\u003eHandbook of environmental psychology\u003c/em\u003e (pp. 413\u0026ndash;426). New York: John Wiley.\u003c/li\u003e\n \u003cli\u003eTilley, N. (2000). Realistic evaluation: an overview. \u003cem\u003eFounding Conference of the Danish Evaluation Society.\u003c/em\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWelsh, B. C., \u0026amp; Farrington, D. P. (1999). Value for money? A review of the costs and benefits of situational crime prevention. \u003cem\u003eBritish Journal of Criminology\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e39, No. 3, pp 345\u0026ndash;368.\u003c/li\u003e\n \u003cli\u003eWelsh, B. C., \u0026amp; Farrington, D. P. (2009). Public Area CCTV and Crime Prevention: An Updated Systematic Review and Meta-Analysis. \u003cem\u003eJustice Quarterly\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e26, No. 4, pp 716\u0026ndash;745.\u003c/li\u003e\n \u003cli\u003eWelsh, B. C., Farrington, D. P., \u0026amp; Douglas, S. (2022). The impact and policy relevance of street lighting for crime prevention: A systematic review based on a half-century of evaluation research. \u003cem\u003eCriminology \u0026amp; Public Policy\u003c/em\u003e. Vol.\u003cem\u003e\u0026nbsp;\u003c/em\u003e21, pp 739\u0026ndash;765.\u003c/li\u003e\n \u003cli\u003eWidmark, J. (2026). Crime Prevention in Residential Areas: A Systematic Review and Meta-Analysis of Environmental Design Approaches. \u003cem\u003eEuropean Journal on Criminal Policy and Research\u003c/em\u003e, pp 1\u0026ndash;29.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Demographic overview\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevelopment areas\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eMean (min\u0026ndash;max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRest of Gothenburg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eLow economic standard (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e34.7 (27.5\u0026ndash;44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eForeign background (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e81.9 (67.7\u0026ndash;90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e51.7 (49.8\u0026ndash;52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eUnemployment (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e21.0 (16.8\u0026ndash;24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eRental housing (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e79.5 (72.4\u0026ndash;91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eMulti-dwelling housing (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e91.1 (81.0\u0026ndash;95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e71.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eOvercrowding (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e32.7 (19.8\u0026ndash;44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eTotal population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e85 954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e486 036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNotes. Definitions follow Statistics Sweden. Values are 2020\u0026ndash;2023 averages; ranges indicate variation across development areas. Source: Statistics Sweden; author\u0026rsquo;s calculations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Treatment types, contexts and address-intervention rows in dataset.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique interventions (IDs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAddress\u0026ndash;intervention rows\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnique classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContext\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMaintenance Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1,087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eCleaning \u0026amp; sanitation, Greening \u0026amp; planting, Maintenance \u0026amp; repair, Painting \u0026amp; refurbishment, Seating \u0026amp; outdoor furniture, Staircase, Surface \u0026amp; ground works, Vegetation management, Walls, fences \u0026amp; railings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eparking, passage, yard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eNatural Surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1,510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eCommunity gardens, Lighting, Vegetation management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eparking, yard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAccess Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eAccess barriers \u0026amp; gates, Locks \u0026amp; target hardening, Walls, fences \u0026amp; railings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eparking, walkway, yard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eActivity Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eCommunity gardens, Outdoor fitness, Playgrounds \u0026amp; recreational areas, Seating \u0026amp; outdoor furniture, Waste infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eyard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003eNote. Intervention labels were harmonised into canonical categories for analytical clarity. Address\u0026ndash;intervention rows denote address \u0026times; intervention records.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Descriptive distribution of outdoor crime by type and area (2019\u0026ndash;2023)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAddresses (included)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (outdoor)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRobbery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAssault\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVehicle-related\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOutdoor vandalism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRate / 1,000 addr.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eDevelopment areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e2 041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e299.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e315.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e240.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eSame neighbourhoods (excl. dev)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e5 394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eRest of city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e101 868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e215.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e253.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e213.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19 113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24 075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e236.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003eNotes. Values show yearly outdoor crime totals and rates per 1,000 addresses. Development areas refer to designated treatment areas; Same neighbourhoods (excl. dev) are primary areas containing development areas but excluding treated addresses. \u0026nbsp;Address counts represent the number of unique addresses included in each group. \u0026nbsp;Data cover 2019\u0026ndash;2023 and are compiled by the author from police-recorded crime data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Main models\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN addresses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN addr-months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eATT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel A: Within development areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.027, 0.014]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel B: External controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10,301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e618,060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[-0.027, 0.013]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 548px;\"\u003e\n \u003cp\u003eNotes: Group-aggregated ATT estimates from a staggered difference-in-differences design. Address-level panel data (2019\u0026ndash;2023). Standard errors clustered at the address level; 95% confidence intervals based on normal approximation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"crime-prevention-and-community-safety","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Crime Prevention and Community Safety](https://www.palgrave.com/gp/journal/41300)","snPcode":"41300","submissionUrl":"https://submission.springernature.com/new-submission/41300/3?","title":"Crime Prevention and Community Safety","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer SNAPPs","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"situational crime prevention, public housing, difference-in-differences, community safety, governance","lastPublishedDoi":"10.21203/rs.3.rs-8940567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8940567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluates outdoor environmental interventions implemented by a municipal public housing provider in police-designated vulnerable residential areas in Gothenburg, Sweden. Using geocoded address-level crime data (2019\u0026ndash;2023) and a staggered difference-in-differences design, we estimate the effects of interventions inspired by situational crime prevention (SCP) principles on outdoor-related crime. Across multiple specifications and control groups, we find no statistically significant reductions following intervention exposure. Event-study estimates indicate no differential pre-treatment trends, and robustness checks yield substantively similar results. Descriptive spatial analyses show no consistent evidence of crime displacement. The findings suggest that broad, multi-purpose housing-led improvement strategies do not automatically translate into measurable crime reductions when preventive mechanisms are diffuse or weakly specified. The study highlights the importance of aligning environmental interventions with clearly defined crime problems and contributes to debates on the role of public housing providers in community safety governance.\u003c/p\u003e","manuscriptTitle":"Estimating the Effects of Outdoor Crime Prevention Interventions Using a Staggered Difference-in-Differences Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 14:34:51","doi":"10.21203/rs.3.rs-8940567/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-18T09:03:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T20:56:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T14:19:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233043110663326690212640809272271474245","date":"2026-03-18T23:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36053446888120113048271158442033333743","date":"2026-03-18T08:59:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48497441827003323261804357397275335345","date":"2026-03-11T07:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-11T06:21:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T23:30:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T23:29:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Crime Prevention and Community Safety","date":"2026-02-22T16:53:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"crime-prevention-and-community-safety","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Crime Prevention and Community Safety](https://www.palgrave.com/gp/journal/41300)","snPcode":"41300","submissionUrl":"https://submission.springernature.com/new-submission/41300/3?","title":"Crime Prevention and Community Safety","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer SNAPPs","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e963a06e-bf6a-46d4-964f-635a70fdc79d","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T17:09:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 14:34:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8940567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8940567","identity":"rs-8940567","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}