Search Results (226)

Wellington, New Zealand, is highly vulnerable to disaster-induced food security crises due to its geography and geological characteristics, which can disrupt transportation and isolate the city following disasters. Urban agriculture (UA) has been proposed as a potential alternative food source for post-disaster scenarios. This study examined the potential of urban agriculture for enhancing post-disaster food security by calculating vegetable self-sufficiency rates. Specifically, it evaluated the capacity of current Wellington’s community gardens to meet post-disaster vegetable demand in terms of both weight and nutrient content. Data collection employed mixed methods with questionnaires, on-site observations and mapping, and collecting high-resolution aerial imagery. Garden yields were estimated using self-reported data supported by literature benchmarks, while cultivated areas were quantified through on-site mapping and aerial imagery analysis. Six post-disaster food demand scenarios were used based on different target populations to develop an understanding of the range of potential produce yields. Weight-based results show that community gardens currently supply only 0.42% of the vegetable demand for residents living within a five-minute walk. This rate increased to 2.07% when specifically targeting only vulnerable populations, and up to 10.41% when focusing on gardeners’ own households. However, at the city-wide level, the current capacity of community gardens to provide enough produce to feed people remained limited. Nutrient-based self-sufficiency was lower than weight-based results; however, nutrient intake is particularly critical for vulnerable populations after disasters, underscoring the greater challenge of ensuring adequate nutrition through current urban food production. Beyond self-sufficiency, this study also addressed the role of UA in promoting food diversity and acceptability, as well as its social and psychological benefits based on the questionnaires and on-site observations. The findings indicate that community gardens contribute meaningfully to post-disaster food security for gardeners and nearby residents, particularly for vulnerable groups with elevated nutritional needs. Despite the current limited capacity of community gardens to provide enough produce to feed residents, findings suggest that Wellington could enhance post-disaster food self-reliance by diversifying UA types and optimizing land-use to increase food production during and after a disaster. Realizing this potential will require strategic interventions, including supportive policies, a conducive social environment, and diversification—such as the including private yards—all aimed at improving food access, availability, and nutritional quality during crises. The primary limitation of this study is the lack of comprehensive data on urban agriculture in Wellington and the wider New Zealand context. Addressing this data gap should be a key focus for future research to enable more robust assessments and evidence-based planning. Full article

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicly available large-scale data, combined with machine learning models, can improve our understanding. We explore the potential of Sentinel-2 to classify and extract meaningful features from urban landscapes. Using advanced machine learning techniques, we aim to develop a robust and scalable framework for classifying urban environments. The proposed models will assist in monitoring changes in green spaces across diverse urban settings, enabling timely and informed decisions to foster sustainable urban growth. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

This study proposes a novel method to assess urban park accessibility by incorporating perceived utility based on both park area and distance. Departing from conventional models that treat accessibility as a function of geometric proximity alone, we define park utility as a distance-discounted benefit of park area, thereby allowing for a more behaviorally grounded measure. A customized discounting function is introduced, where larger park sizes proportionally reduce perceived travel cost, and walking speed adjustments are applied based on demographic user profiles (children, adults, and older adults). The methodology was implemented using a Python-based v.3.12.9 geospatial workflow with network-based distance calculations between 18,614 census block groups and all urban parks in Seoul. Population-weighted utility scores were computed by integrating park size, distance, and age-specific mobility adjustments. The results reveal significant intra-urban disparities, with a citywide deficit of 4,066,046 m in population-weighted distance, particularly in areas with large populations but insufficient proximity to high-utility parks. Simulation analyses of 30 candidate sites demonstrate that strategic park placement can yield substantial utility improvements (maximum gain: 493,436 m), while indiscriminate expansion may not. These findings offer spatial decision support for optimizing limited public resources in urban green infrastructure planning and underscore the need to consider both park scale and user-specific walking behavior in evaluating accessibility. Full article

Housing informality in Morocco has taken root within Rabat’s formal neighborhoods, quietly reshaping façades, extending plot lines, and redrawing the texture of entire blocks. This ongoing transformation runs up against the rigidity of official planning frameworks, producing tension between state enforcement and tacit tolerance, as residents navigate persistent legal and economic ambiguities. Prior Moroccan studies are neighborhood-specific or socio-economic; the field lacks a city-wide, multi-class analysis linking everyday tactics to long-term governance dilemmas and policy design. The paper, therefore, asks how and why residents and architects across affordable, middle-class, and affluent districts craft unapproved modifications, and what urban order emerges from their cumulative effects. A mixed qualitative design triangulates (i) five resident focus groups and two architect focus groups, (ii) 50 short, structured interviews, and (iii) 500 geo-referenced façade photographs and observational field notes, thematically coded and compared across housing types. In addition to deciphering informality methods and impacts, the results reveal that informal modifications are shaped by both reactive needs—such as accommodating family growth and enhancing security—and proactive drivers, including esthetic expression and real estate value. Despite their legal ambiguity, these modifications are socially normalized and often viewed by residents as value-adding improvements rather than infractions. Full article

Extreme rainfall events driven by climate change are increasing flood risks. Addressing flood mitigation solely from either a hydraulic engineering or urban planning perspective may overlook both feasibility and effectiveness. This study focuses on Tainan City and the Tainan Science Park in Taiwan, applying the NbS framework to assess flood mitigation strategies. From an urban planning perspective, Agricultural Development Zone Type II (Agri-DZII), parks, green spaces, and Taiwan Sugar Corporation (TSC) land were selected as flood detention sites. Hydraulic modeling was used to evaluate their effectiveness under both current and climate-change-induced rainfall conditions. Simulation results show that under current rainfall conditions, flood mitigation measures reduced inundated areas with depths exceeding 2.0 m by up to 7.8% citywide and 20.8% within the Tainan Science Park Special District Plan Area. However, under climate change scenarios, the reduction effects declined significantly, with maximum reductions of only 1.6% and 17.8%, respectively. Results indicate that, even when utilizing all available detention areas, the overall flood reduction in Tainan City remains limited. However, TSC agri-land within the Tainan Science Park overlaps with high-flood-risk zones, demonstrating significant local flood mitigation potential. This study recommends integrating hydrological analysis into urban planning to prevent high-density residential and economic zones from being designated in flood-prone areas. Additionally, policymakers should consider reserving appropriate land for flood detention to enhance climate resilience. By combining urban planning and hydraulic engineering perspectives, this study highlights the flexibility of NbS in disaster management, advocating for the integration of Natural Water Detention Measures into flood adaptation strategies to improve urban water management and climate adaptability. Full article

Urban Air Mobility (UAM) requires reliable communication and surveillance infrastructures to ensure safe Unmanned Aerial Vehicle (UAV) operations in dense metropolitan environments. However, urban infrastructure is inherently heterogeneous, leading to significant spatial variations in monitoring performance. This study proposes a unified framework that integrates infrastructure readiness assessment with Deep Reinforcement Learning (DRL)-based UAV path planning. Using Singapore as a representative case, we employ a data-driven methodology combining clustering analysis and in situ measurements to estimate the citywide distribution of surveillance quality. We then introduce an infrastructure-aware path planning algorithm based on a Double Deep Q-Network (DQN) with a convolutional architecture, which enables UAVs to learn efficient trajectories while avoiding surveillance blind zones. Extensive simulations demonstrate that the proposed approach significantly improves path success rates, reduces traversal through poorly monitored regions, and maintains high navigation efficiency. These results highlight the potential of combining infrastructure modeling with DRL to support performance-aware airspace operations and inform future UAM governance systems. Full article

Understanding changes in urban green and blue infrastructure (UGBI) associated with land use management can inform planners on trends in environmental change that may impact urban resilience. While UGBI change resulting from land use conversion has received significant research interest, UGBI change within otherwise consistent land uses has received scant attention. This study developed a high-resolution spatiotemporal analysis framework to map fine-scale UGBI change across all land use classes in Manchester, UK, over a period (2000–2017) of significant population growth. The study found that UGBI declined in 17 out of 29 land use classes, with an overall city-wide UGBI loss of 11.9%, compared to UGBI gains for 6.4% of the city. Declines were most concerning in residential areas, which cover 33.6% of Manchester, as UGBI in these areas is important for delivering ecosystem services to citizens. Extrapolation of change rates indicate that two-thirds of future UGBI loss could occur in residential areas. These results provide insights into socio-economic processes which are likely to have similar implications for UGBI trends in other urban areas. Such knowledge is critical to inform land use planning and management to identify where UGBI is at risk and implement appropriate policies to reverse or minimise losses. Full article

This study investigates the relationship between neighbourhood walkability, recreational walking, and physical activity and well-being outcomes in Bangkok, Thailand. A cross-sectional survey was conducted with 881 residents from 50 districts in Bangkok. The Neighbourhood Environment Walkability Scale-Abbreviated (NEWS-A) was employed to assess neighbourhood walkability and its association with recreational walking behaviour. The Global Physical Activity Questionnaire (GPAQ) and WHO-5 Well-Being Index were used to explore the links between recreational walking, physical activity, and well-being. The findings reveal that neighbourhood environment characteristics significantly influence recreational walking behaviour, with high-rise and planned neighbourhoods showing higher odds of recreational walking compared to unplanned neighbourhoods. Additionally, recreational walking was positively associated with both achieving sufficient physical activity and better well-being scores. These findings suggest that neighbourhood-level interventions aimed at promoting recreational walking could be effective strategies for encouraging physical activity and enhancing well-being in urban residents. The study recommends that targeted, neighbourhood-level interventions focused on creating supportive walking environments may be more effective in promoting health outcomes compared to broader city-wide urban design strategies. Our results also provide evidence-based support for shifting from tourism-centric to neighbourhood-focused walking infrastructure development in Bangkok. Full article

To enhance sustainability in urban design, it is essential to consider urban planning and morphology. This study explores the relationship between key morphological layers and the degree of sustainability in urban forms. Understanding how morphological characteristics influence sustainable urban form parameters provides valuable insights into urban areas’ sustainability potential. Based on this premise, a theoretical model is proposed to assess the potential of urban design for sustainability. The study examines urban forms across multiple scales, from material level to city-wide analysis, employing typo-morphological analysis inspired by Italian and British urban morphology schools. The model has been evaluated by academics from various universities who assessed the relationship between sustainable urban form parameters and morphological elements by weighting their relevance. Analytical tools were applied, including SPSS 29.0 and Excel-based mathematical methods. The results confirm a strong correlation between morphological elements and sustainable urban form characteristics, influenced by scale and classification. Additionally, the study identifies the most impactful parameters for enhancing sustainability in urban design. This research contributes to a comprehensive framework for sustainable urban morphology, offering practical insights for planners and designers in shaping more sustainable cities. Full article

Rapid urbanization has accelerated the transformation of community dynamics, highlighting the critical need to understand the interplay between subjective perceptions and objective built environments in shaping life satisfaction for sustainable urban development. Existing studies predominantly focus on linear relationships between isolated factors, neglecting spatial heterogeneity and nonlinear dynamics, which limits the ability to address localized urban challenges. This study addresses these gaps by utilizing multi-scale geographically weighted regression (MGWR) to assess the spatial nonstationarity of subject perceptions and built environment factors while employing gradient-boosting decision trees (GBDT) to capture their nonlinear relationships and incorporating eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy. Using geospatial data (POIs, social media data) and survey responses in Suzhou, China, the findings reveal that (1) proximity to business facilities (β = 0.41) and educational resources (β = 0.32) strongly correlate with satisfaction, while landscape quality shows contradictory effects between central (β = 0.12) and peripheral zones (β = −0.09). (2) XGBoost further quantifies predictive disparities: subjective factors like property service satisfaction (R² = 0.64, MAPE = 3.72) outperform objective metrics (e.g., dining facilities, R² = 0.36), yet objective housing prices demonstrate greater stability (MAPE = 3.11 vs. subjective MAPE = 6.89). (3) Nonlinear thresholds are identified for household income and green space coverage (>15%, saturation effects). These findings expose critical mismatches—residents prioritize localized services over citywide economic metrics, while objective amenities like healthcare accessibility (threshold = 1 km) require spatial recalibration. By bridging spatial nonstationarity (MGWR) and nonlinearity (XGBoost), this study advances a dual-path framework for adaptive urban governance, the community-level prioritization of high-impact subjective factors (e.g., service quality), and data-driven spatial planning informed by nonlinear thresholds (e.g., facility density). The results offer actionable pathways to align smart urban development with socio-spatial equity, emphasizing the need for hyperlocal, perception-sensitive regeneration strategies. Full article

Background/Objectives: Effective HIV treatment and prevention rely heavily on patient adherence to the prescribed regimen. Therapeutic drug monitoring (TDM), which involves measuring medication concentrations in blood circulation, offers an objective method to evaluate toxic or ineffective drug levels. TDM is not routinely used in HIV treatment in clinical practice. Therefore, the purpose of this study is to survey infectious disease providers’ attitudes and barriers toward therapeutic drug monitoring for antiretroviral therapy in people living with HIV. Materials and Methods: A 15-item online survey was distributed to infectious disease providers in the Greater Houston area, including physicians, pharmacists, and mid-level practitioners involved in HIV patient care. The survey was disseminated via the Houston Citywide Infectious Disease Provider Network and the Houston AIDS Education and Training Center. The survey employed close-ended questions to evaluate providers’ attitudes, perceptions, practices, and barriers toward antiretroviral drug level monitoring. Responses were recorded using a five-point Likert scale. Demographic characteristics and information regarding research involvement were collected to contextualize the findings. The survey results were analyzed using descriptive statistics, with categorical variables expressed as frequencies and percentages using SAS software. Results: A total of 139 responses were received, with 89 participants meeting the inclusion criteria; the majority were female (62.9%), nearly half were aged 34 or younger (53.4%), 50% were physicians and 36.3% pharmacists, and most worked in hospitals (52.3%) or clinics (35.2%). The findings demonstrate participants’ predominantly positive attitudes toward TDM. Nearly 70% agree (agree or strongly agree) that TDM will be helpful and will positively impact improving drug efficacy and medication adherence. The results revealed barriers to implementing TDM, including a lack of evidence supporting TDM’s impact on HIV outcomes, and the absence of clinical guidelines. The results indicated that >90% were ambivalent or agreed that there was not enough evidence to support the use of TDM, and nearly all recognized that the guidelines do not endorse it or did not know if they do not endorse it. Conclusions: This study highlighted positive attitudes and significant barriers to implementing therapeutic drug monitoring, including a lack of evidence supporting TDM’s impact on HIV outcomes and the absence of clinical guidelines supporting TDM’s widespread use. The findings emphasize the need for clinical trials and longitudinal studies to establish definitive evidence on the effectiveness of TDM in improving HIV treatment outcomes. Full article

Interdisciplinary research has significantly advanced our understanding, benefits, and measurements of Urban Green Space (UGS). Further, the rapid expansion of research on this topic has resulted in a diverse array of definitions, which can rely on implicit assumptions without a formal definition. This variability highlights the need for a carefully structured framework to refine and combine these definitions. This narrative review examines constructs underlying UGS, particularly focusing on the spatial aspects of how we spatially measure UGS, the measurements of UGS, and how we define exposure; the latter focuses on two methods: viewsheds and image segmentation. Our findings reveal a shift in UGS measurement focus, moving beyond simple quantification of how much green space exists, to incorporate visibility, accessibility, and availability dimensions. Furthermore, advancements in computational tools, including artificial intelligence-driven methods, now enable high-resolution visibility measurements on a city-wide scale, supporting epidemiological research and urban development. These insights aim to guide researchers and practitioners in selecting suitable methodologies and datasets, as well as explicitly defining UGS in their work through a construct-based approach. Full article

This review paper examines how Generative AI (GAI) and Large Language Models (LLMs) can transform smart cities in the Industry 5.0 era. Through selected case studies and portions of the literature, we analyze these technologies’ impact on industrial processes and urban management. The paper targets GAI as an enabler for industrial optimization and predictive maintenance, underlining how domain experts can work with LLMs to improve municipal services and citizen communication, while addressing the practical and ethical challenges in deploying these technologies. We also highlight promising trends, as reflected in real-world case studies ranging from factories to city-wide test-beds and identify pitfalls to avoid. Widespread adoption of GAI still faces challenges that include infrastructure and lack of specialized knowledge as a limitation of proper implementation. While LLMs enable new services for citizens in smart cities, they also expose certain privacy issues, which we aim to investigate in this study. Finally, as a way forward, the paper suggests future research directions covering new ethical AI frameworks and long-term studies on societal impacts. Our paper is a starting point for industrial pioneers and urban developers to navigate the complexity of GAI and LLM integration, balancing the demands of technological innovation on one hand and ethical responsibility on the other. Full article

Urban traffic-related air pollution has emerged as a significant concern for the physical environment in densely populated urban areas. This study numerically investigates the dispersion of air pollutants and ventilation within typical urban blocks in Shanghai, considering the prevailing annual winds—northerly in winter (4.64 m/s) and easterly in summer (5.85 m/s). Multiple factors influence the dispersion of urban pollution. In this research, we examine the effects of viaducts and urban ventilation corridors, alongside the impact of urban parameters on pedestrian-level ventilation, by analyzing variations in building forms along residential streets in Shanghai. A novel approach for analyzing pollution dispersion is proposed, which involves performing a sensitivity analysis on the buffer radius and mapping various radii onto the C* parameter. The results indicate that: (1) enhancing air fluidity in regions with stagnant winds can be achieved by introducing vertical turbulence; (2) the prevailing wind direction, urban ventilation corridors, and urban permeability play a crucial role in determining the direction of pollutant dispersion at pedestrian levels in densely populated urban environments; (3) the contribution of pollutants released at ground level is significantly higher than those from viaducts at pedestrian height (248.58%). Drawing on both theoretical and experimental research, this study explores the spatial dispersion of air pollutants across various scales, including city-wide, block-level, and building-specific perspectives. The findings provide recommendations for the design of environmentally sustainable urban streets in residential areas. Full article