From Barriers to Breakthroughs: A Mixed-Methods Evaluation of Stroke Education in an Under-Resourced Urban Population.

There is substantial evidence that positive intergroup contact can reduce prejudice. Most everyday interactions, however, are not deliberately structured to be positive, and individuals do not always engage in intergroup contact even when there is opportunity. The present research adopts a qualitative approach to understand how youth negotiate everyday contact with outgroup friends and acquaintances in the ethnically diverse city of Bradford, England. We explore how youth intergroup interactions manifest in everyday life, how urban spaces facilitate or inhibit them, and the psychological processes involved. A total of 33 youth aged 16–18 (16 Asian, 14 White, 1 Black, 1 Arab, 1 mixed race) took part in a photography project and focus group sessions, and nine of those youth (4 Asian, 3 White, 1 Black, 1 Arab) took part in follow‐up walking interviews. Data were analysed using reflexive thematic analysis. Findings demonstrated the habitual nature of everyday intergroup contact and the complex negotiations youth engage in to socialise with outgroup friends. They also highlight how space perceptions influence the maintenance of cross‐ethnic friendships and are shaped by past experiences and memories. Our research has implications for understanding everyday unstructured interactions and the spatial and temporal factors that influence youth intergroup contact.

The electrification of minibus taxis in Africa is envisioned to play an important role in decarbonising transportation, reducing urban air pollution, and generating economic savings. However, comprehensive quantifications of these implications are scarce. This study introduces a modelling approach based on readily available data to simulate minibus operation and evaluate the key energy, environmental, and economic implications of such an electrification. Applying our model to nine diverse African cities, we find that electrifying minibuses could prevent the emission of 4.3 to 19.2tCO[Formula: see text] annually per vehicle, based on current electricity mixes, while saving minibus owner-operators US$1.2k to US$14.0k in annual fuel costs. Using a relative exposure index for minibus-related air pollution, we also identify that approximately 23million people across these cities could benefit from improved air quality. Nevertheless, substantial variations in the charging demand-both per vehicle and aggregated per city-are observed, emphasising the critical importance of energy planning and tailored electrification strategies. This research provides insights for policymakers and planners, and offers a transparent and replicable framework for assessing the impacts of public transport electrification across diverse locations.

ABSTRACT The concept of scenes offers a lens for understanding how urbanites interact with and assign meaning to neighborhoods in culturally diverse cities. Building on scenes theory, this paper examines how scenes—defined as combinations of people, place, shared values, and amenities—emerge, coexist, and operate at different scales. Drawing on 56 walking interviews and ethnographic fieldwork, I identify four scenes in Lower-Molenbeek (“Feels like Morocco,” “Flemish Diversity-seekers,” “Artist-activist,” and “Old-school Bruxellois.e.s”) and four in Matonge (“Feels like Africa/Congo,” “Diversity-seekers,” “Postcolonial,” and “Expat”). Findings show that aesthetic repertoires, the interplay of sensory cues and values, are central to understanding coexistence. Scenes are interdependent, often drawing on each other’s presence and aesthetic cues. Overall, neighborhoods display convivial coexistence, yet interactions remain rare and occur only under specific conditions. These findings suggest that vivre ensemble in superdiverse neighborhoods is fragile: symbolic appreciation rarely translates into interaction. Cohesion depends on organizations and activities that actively bridge scenes, rather than spatial proximity alone. While some scenes shape local atmospheres, others operate citywide and influence development agendas. Analyzing aesthetic repertoires alongside spatial practices provides a conceptual and methodological toolkit for understanding coexistence in diverse cities.

Accurately modeling human mobility flows between urban regions is crucial for urban sustainability. However, estimating such flows in cities lacking any observed mobility data remains a fundamental challenge. Existing models typically rely on transferring city-specific mobility patterns to target cities, limiting their generalizability. We propose a novel cross-city mobility modeling framework that departs from conventional approaches: rather than learning city-specific mobility patterns, we learn transferable spatial priors that drive mobility formation from readily available geographic data. Specifically, we first construct a cross-city graph neural network (GNN) and pre-train it on large-scale urban population and location data from diverse cities, without using mobility data, to learn a mobility prior representation that encodes how population distribution shapes the potential for human mobility. We then introduce a lightweight graph prompt mechanism to adapt this generic prior to the unique mobility patterns of a target city by fine-tuning the pre-trained GNN. Flow generation is treated as a downstream task of the adapted model. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods in both accuracy and cross-city generalization.

This study uses statistical and machine learning techniques to categorize and rank 99 high development cities according to multidimensional Quality of Life factors (QoL). We categorize cities into three unique clusters using hierarchical Ward.D2 clustering and Principal Component Analysis for dimension reduction. We discover clusters that bring together economically developed cities with strong social safety nets, high-income cities lacking in public amenities, rising cities of the future located in peripheral regions, and major population centers in the developing world, finding considerable structural similarities that lead to comparable outcomes across culturally and geographically diverse cities. Components of QoL are grouped into three Principal Components that reveal dynamic interactions between different variables influencing economic and experimental aspects of QoL. Clusters are assessed relative to one another using the three PCs, and crucial factors for classifying cities into different clusters are identified by a Decision tree to allow for tailored policy recommendations for cities of different clusters. Our findings give policymakers a framework to prioritize holistic urban development over GDP-centric models, highlighting the importance of striking a balance between objective standards of human development and subjective, experiential indicators of individual wellbeing.

ABSTRACT The proliferation of AI‐generated building footprints in OpenStreetMap (OSM) has transformed crowdsourced mapping, yet the geometric characteristics associated with different digitization methods remain poorly understood. This study presents a comprehensive morphometric analysis of more than 9 million building footprints across 15 geographically diverse cities spanning six continents. To investigate whether buildings labeled with AI source tags exhibit distinct geometric patterns, machine learning classifiers were trained on a regionally balanced dataset of approximately 1 million buildings. Thirty‐two shape‐based features encompassing size metrics, shape regularity, complexity measures, and anomaly detection indicators were extracted, and three gradient boosting classifiers were evaluated to distinguish these contributions. The best‐performing model (LightGBM with class weighting) was used to achieve 74.5% balanced accuracy, 82.9% recall, and 0.819 AUC, providing evidence that buildings labeled with AI source tags exhibit geometric patterns that differ systematically from other buildings in the dataset. Feature importance analysis revealed that orthogonality measurements, vertex density patterns, and shape regularity indices were the strongest discriminators, with orthogonality z ‐scores alone accounting for 18.2% of model importance. AI‐generated buildings showed significantly higher orthogonality (angles within 5° of 90°), greater rectangularity, and more consistent vertex spacing compared to human‐digitized footprints. Geographic analysis revealed substantial variation in both AI adoption (0.15% in Berlin to 15.7% in Cairo) and model performance across regions, with balanced accuracy ranging from 67.5% (Asia) to 79.9% (Oceania). However, 34% of human‐mapped buildings exhibited AI‐like geometric patterns: an overlap reflecting multiple competing factors including methodological convergence, label uncertainty from hybrid workflows, OSM's version‐history limitations, and natural variation in human digitization practices, none of which can be definitively separated without independent ground truth validation. These findings provide exploratory evidence that geometric analysis might complement other approaches to understanding data provenance in crowdsourced platforms, though substantial overlap between categories limits the utility of morphometric features for definitive source attribution.

Access to healthcare services remains a persistent challenge, disproportionately affecting vulnerable populations. Limited access results in lower service utilisation and worse health outcomes, hindering progress toward inclusive and sustainable cities. This study developed a novel methodological framework integrating high-resolution (1-km2) socio-demographic data from the 2021 Canadian Census with an advanced multimodal transport routing engine (R5) to assess healthcare access via public transit in Surrey, British Columbia - a fast-growing, diverse city. Using a 30-min travel time threshold, we computed destination-oriented ('passive') and origin-oriented ('active') accessibility to walk-in clinics, urgent care centres, and hospitals. Eco-intersectional multilevel modelling was applied to examine accessibility inequalities across intersectional strata, defined as areas with a high concentration of vulnerable populations based on age, sex, race/ethnicity, education, income, and urbanicity (>vs. ≤ 400 people/km2). Overall, 319,402 (56.2%) residents could reach at least one healthcare facility within 30 min via public transit. Walk-in clinics were the most accessible, followed by urgent care centres and hospitals. Many vulnerable populations were concentrated near major urban centres, which generally had access to more facilities than the city's periphery and outer suburbs. Strata with a high concentration of females had higher odds of accessibility, while seniors and non-urban areas had lower odds. Access inequalities were most pronounced among senior visible minority communities living in non-urban areas. Equity-oriented planning and investments in sustainable transportation and healthcare infrastructure are required to close accessibility gaps. This scalable, open-data framework can inform inclusive urban policy and improve access to essential services for underserved communities.

Towards scalable urban monitoring: Semi-supervised building change detection and the ICCD dataset for Indian cities

Association between rainfall and pre-hospital traffic injury characteristics in Southern China: A multi-city time-series analysis in an E-bike dominated region

Purpose As subway networks expand and cities grow in China, mobile payment and smart access are central to operations, yet passenger satisfaction with these phygital (physical–digital) encounters is underexplored. This study aims to examine how subway app interface quality shapes overall passenger satisfaction across diverse Chinese cities. Design/methodology/approach Grounded in the stimulus–organism–response (S-O-R) framework, we analyze 18,487 App Store reviews of subway mobile apps from 10 representative Chinese cities. We apply machine learning to unstructured user-generated content (UGC), using PERT for feature extraction, latent Dirichlet allocation for topic modeling, and bidirectional encoder representations from transformers with whole word masking extension for fine-grained sentiment analysis. Findings Digital reliability is a critical hygiene factor for satisfaction. Drivers vary by city: high-density metropolises prioritize payment efficiency to ease time pressure, whereas developing cities are constrained by onboarding frictions (e.g. registration and verification). We also observe a spillover effect whereby app instability worsens perceptions of the physical ride experience. Originality/value We extend E-S-QUAL to smart mobility and validate the integration of digital and physical service quality within S-O-R using large-scale UGC. The findings offer actionable guidance for city managers and transit operators to improve resilient and inclusive service delivery and passenger satisfaction in the digital era.

BackgroundFew studies have focused on factors contributing to decreased rates of bystander cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use after out‐of‐hospital cardiac arrest in underserved and underrepresented communities, particularly those with Asian populations. We identified factors related to CPR and AED use in 2 cities with low socioeconomic status, majority underrepresented populations, and historically low CPR and AED use rates.MethodsWe conducted semistructured focus groups in English, Spanish, Japanese, and Korean between September 2024 and May 2025. We used purposive and snowball sampling. We developed a grounded theory model to explain the factors influencing learning and performing CPR and AED use.ResultsOf 124 participants, 44% identified as Hispanic, 29% as Asian, and 19% as Black. We grouped our findings into 4 main themes: (1) barriers to obtaining CPR/AED education and training, (2) facilitators to obtaining CPR/AED education and training, (3) barriers to providing bystander care, and (4) facilitators to providing bystander care. Barriers to learning included limited public information, low prioritization, training access, and institutional distrust. Barriers to providing care included low confidence, qualification misconceptions, legal and personal welfare concerns, social dynamics, and AED access. Facilitators to learning included self‐ and family preparedness, training incentives, and training for social roles. Facilitators to providing care included willingness to perform interventions after training and 911 dispatcher instructions.ConclusionsA complex array of factors influence learning and performing CPR and AED use in underserved and underrepresented communities. These findings should inform the development of community‐specific CPR and AED initiatives.

ABSTRACT Perception-based studies of urban neighborhoods and physical activity often focus narrowly on walkability, overlooking broader social, design, and environmental domains. This study developed and validated the Comprehensive Neighborhood Environment Physical Activity Questionnaire (CNEPAQ), comprising 82 items across 11 factors (destination proximity, destination accessibility, residential density, street connectivity, infrastructure for walking/cycling, safety, urban design qualities, social environment, recreational facilities, fitness and sports facilities, ambient environment). Content validity was evaluated through expert review and cognitive testing in Nagpur, India. A cross-sectional survey (N = 425 across four neighborhoods) assessed construct validity using exploratory and confirmatory factor analyses; known-group differences were tested using ANCOVA. The content validity of CNEPAQ was high (item CVR ≥0.60). The 11-factor CFA demonstrated excellent fit (χ2 [3184] = 4869.51, p < .001; CFI = 0.989; TLI = 0.989; AGFI = 0.969; RMSEA = 0.05; CMIN/DF = 1.53). ANCOVA indicated significant neighborhood differences (p < .001). Test-retest reliability on a subsample (N = 121) was almost perfect at the factor level and substantial to almost perfect at the item level. CNEPAQ is a valid and reliable instrument; further research is needed to evaluate its applicability across diverse neighborhoods and cities in India, and other low- and middle-income countries.

The transition to carbon neutrality in European cities is a cornerstone of the European Commission’s new Green Deal, with the ambitious target of delivering 100 “Climate-neutral and smart cities” by 2030. Although cities are increasingly recognised as pivotal actors in environmental governance, a persistent gap, largely acknowledged in the literature, remains between policy ambitions and implementation outcomes. This paper presents a comprehensive analysis of the structural, financial, organizational, political, and technical barriers that hinder the effective implementation of carbon-neutral strategies at the city level. Drawing on case study analysis from 10 diverse European cities, the study applies a qualitative methodology integrating insights from 30 collaborative workshops held between 2023 and 2024 and 12 follow-up interviews with city practitioners. The first round of findings confirms many of the key barriers identified in the literature. More critically, insights from this study reveals that these barriers and their underlying drivers operate through complex interdependent networks of causes and effects - what we conceptualise as “vicious circles of barriers.” Identifying these vicious circles provides a deeper understanding of the self-reinforcing carbon lock-in dynamics that constrain urban transformation and highlights that many underlying drivers extend beyond the institutional capacities of the cities themselves. Therefore, this paper calls for a renewed examination of the role of cities within broader climate governance.

Intercity coopetition—the dynamic interplay between cooperation and competition—is central to regional innovation, enabling knowledge exchange across cities. However, the spatial organization of these dynamics among functionally diverse cities and their implications for regional innovation outcomes remain underexplored. This study examines how urban polycentricity, captured through the balance of intercity coopetition relations, affects regional innovation performance. Methodologically, we develop an intercity coopetition index that incorporates both geographical overlap in collaboration networks and technological proximity. Empirically, we draw on longitudinal patent co-application data from nineteen Chinese urban regions between 1990 and 2022. Using fixed effect and instrumental variables models, we find that a balance in cooperation alone has a negative effect on regional innovation. In contrast, regional innovation performance is driven by the interaction between the strength of competitive ties and the balance of competition across cities. We conclude by discussing the policy implications of these findings for fostering spatially coordinated innovation systems.

Accurate assessment of personal PM2.5 exposure is essential but challenging in large-scale epidemiology, as conventional residential ambient data often lead to exposure misclassification. This study aimed to quantify errors in ambient data proxies and develop a scalable modeling framework for personal exposure prediction using readily available data. We conducted a panel study with 12 adults from three diverse Chinese cities, collecting 4571 person-hours of personal PM2.5 measurements. These were compared against three ambient data sources to quantify relative errors. We developed a modeling framework integrating ambient concentrations, meteorological variables, and basic personal characteristics, incorporating systematic preprocessing, feature engineering, variable selection, and multialgorithm comparison optimized through hyperparameter tuning and cross-validation. Results showed substantial personal-ambient exposure discrepancies, with relative errors of 39-48% for the daily average level. The framework successfully predicted personal exposure, with a Random Forest model using daily monitoring-station data achieving the highest performance (R2 = 0.87). SHAP analysis identified ambient PM2.5 as the dominant predictor, with personal traits and meteorology also contributing significantly. This work provides a validated, end-to-end modeling framework that moves beyond ambient proxies, offering a standardized workflow to refine personal exposure estimates in large cohorts and enhance the validity of air pollution health studies.

Shade provision is the most effective strategy for mitigating heat in cities; yet its distribution remains highly uneven. Using high-resolution simulations of shade casting from buildings and trees on pedestrian areas, combined with socioeconomic data at the neighbourhood level, we assess shade availability across nine climatically and geographically diverse cities: Amsterdam, Barcelona, Belém, Boston, Hong Kong, Milan, Rio de Janeiro, Stockholm, and Sydney. Our results reveal a consistent pattern of spatial and socioeconomic inequality: lower-income and peripheral neighbourhoods tend to receive significantly less shade on sidewalks, despite facing greater heat vulnerability. Notably, inequality persists even in cities with high overall shade coverage, where wealthier areas benefit from disproportionate abundance. By focusing on public pedestrian spaces, rather than general coverage, this study highlights the importance of measuring heat burden through the lens of human experience. We call for equity-centred adaptation strategies that target shade provision where it is most needed, particularly in underserved and exposed communities.

ABSTRACT Short videos have become essential in constructing destination image, yet strategies tailored to diverse city types and video genres remain insufficiently studied. This research selects three Chinese “wanghong” tourism cities – Harbin, Zibo, and Ganzi – as cases. Integrating the cognitive–affective-conative model, Message Sensation Value, and visual framing theory, it develops a multidimensional framework to analyze differentiated image construction strategies. Findings reveal three city types – resource-driven, culture-embedded, and celebrity-led – each employing distinct strategies. Official videos emphasize macro imagery and positive emotions, whereas user-generated videos focus on experiential authenticity. The study offers theoretical and practical insights for effective destination marketing adapted to city and account type.

Chicago, Illinois is one of the most diverse cities in the United States. Yet, its history is fraught with inequities which pervade every pillar of its modern infrastructure, from housing and education to healthcare. Established healthcare institutions, like Rush University Medical Center (RUMC), are uniquely positioned to combat historically ingrained disparities. Their physical presence, economic influence, and associated medical schools provide the opportunity and resources to support sustainable health equity strategies such as the Cardiometabolic Health Initiative (CHI). CHI is a health equity initiative founded by Rush Medical College (RMC) students. Its mission is to provide comprehensive cardiometabolic screenings directly to West Chicago residents. Partnering with RUMC, CHI leverages its medical school cohort as well as the medical center's resources to provide preventative care to those in need. Since its inception, CHI has screened over 300 West Chicago patients. The preliminary results emphasize the disproportionate prevalence of cardiometabolic disease in this population, and underscore the need for health equity organizations like CHI. The health inequity seen across Chicago is not unique. The same patterns of cardiometabolic disease are mirrored across American cities. CHI serves as an innovative blueprint for addressing urban health inequity. By harnessing the resources of established health systems and their affiliated medical schools, student-run initiatives, like CHI, can improve urban health nationwide.

BackgroundIt is estimated that type 2 diabetes (T2D) impacts an estimated 5.3 million Canadians, despite the condition being largely preventable. Laboratory-based diabetes prevention programs (DPPs) have limited effectiveness when translated into community settings due to their low-quality delivery and inability to reach people in the community most in need. To date, no community-based DPPs have been implemented nationwide across Canada. Small Steps for Big Changes (SSBC) is a diet and physical activity counseling intervention that significantly reduces the risk of developing T2D and has been designed for feasible delivery by community-dwelling peers. To ensure maximum public health impact, SSBC must be optimally implemented, demonstrate effectiveness for diverse groups, and be sustainable over time.ObjectiveThis project aims to adapt SSBC and evaluate the implementation, effectiveness, and sustainability of SSBC in diverse urban communities across Canada.MethodsA hybrid type 2 implementation-effectiveness study design using multiple and mixed methods will be used to evaluate the implementation and effectiveness of SSBC over 6 years in partnership with 11 regional Young Men’s Christian Associations across 8 provinces in Canada. Beginning in 2024, we will (1) adapt and implement SSBC in diverse urban cities across Canada; (2) examine the implementation (including implementation strategies), effectiveness, and cost-effectiveness of SSBC (2024-2028); and (3) determine the sustainability of SSBC at each delivery location (2028-2029). Data will be collected from SSBC clients, coaches, site leads, and senior leadership municipality partners. The project will be overseen by an advisory group and 3 committees focused on sex, gender, and inclusivity; program evaluation; and diabetes prevention engagement. This study has received ethical approval from the University of British Columbia Clinical Research Ethics Board (H23-01930).ResultsFunding for this project began in October 2022, and Institutional Review Board approval was obtained in October 2023. Program implementation within each region is occurring in a phased approach, with partners beginning program delivery in one site (2024-2025) before expanding to any additional locations (2025-2027). Program enrollment occurs continuously during the implementation phase across all sites. From 2024-2025, a total of 13 delivery sites began program delivery, 722 participants have enrolled in the program, and 406 have begun the program (153/342, 44.7% non-Western or Eastern European; 80/345, 23.2% men or 82/347, 23.6% male). An additional 13 sites have confirmed they will launch the program in 2026.ConclusionsThis study will demonstrate that SSBC can be scaled up nationwide to effectively and equitably reduce the Canadian population-level risk of T2D. This work will determine best practice implementation determinants, outcomes, and strategies critical for sustaining DPP implementation across Canada and beyond. Project findings will be shared with municipality partners and will be copresented with partners and SSBC clients to community organizations, local interested parties, and academics.Trial RegistrationClinicalTrials.gov NCT06440395; https://clinicaltrials.gov/study/NCT06440395International Registered Report Identifier (IRRID)DERR1-10.2196/80276