Search Results (779)

With the ongoing advancement of urbanization, the renewal of old urban areas has emerged as a central front in enhancing urban quality, with street space improvement playing a pivotal role in advancing sustainable urban development. This study focuses on Chengdu, a highly urbanized megacity, employing a combination of multi-point continuous street view photography, spatial mapping, and landscape design interventions to systematically examine human activity patterns, commercial dynamics, and pathways for spatial optimization along the street-facing interfaces of old residential neighborhoods and their adjacent urban streets. The findings reveal that: (1) commercializing the street-facing façades enhances local employment opportunities; (2) window-type fences demonstrate superior adaptability by effectively balancing commercial accessibility with resident safety; and (3) a diverse mix of commercial types sustains the vitality of street-level economies in these areas. These results not only offer actionable spatial strategies for the renovation of old residential zones in Chengdu but also contribute transferable insights for urban regeneration efforts globally. Full article

Blueberries (Vaccinium spp.) are highly sensitive to winter chilling fulfillment, growing degree days above 7 °C (GDD7), and water balance (WB). By integrating a climate-based natural suitability index (CNSI), three-dimensional kernel density estimation, traditional and spatial Markov chains, and optimal geographic detector analysis, this study examines the spatiotemporal evolution and driving mechanisms of blueberry climatic suitability realization in 19 major producing provinces in China during 2008–2023. Results show that CNSI exhibits a stable and moderately right-skewed distribution, with partial convergence and a narrowing interprovincial gap. Suitability realization is highest in the middle and lower Yangtze River rice-growing belt, whereas the northern dryland belt and the southern subtropical mountainous belt show persistent mismatches between climatic potential and production advantages. Markov results reveal path dependence and moderate mobility, with “low–low lock-in” and “high–high club” phenomena reinforced under neighborhood effects. GeoDetector results indicate that effective facility irrigation and fertilizer input are dominant factors explaining spatial variation in CNSI, while comprehensive transportation accessibility and agricultural labor act as stable complements. Interaction analysis suggests that multi-factor synergies, particularly irrigation-centered combinations, yield strong dual-factor enhancement and near-nonlinear enhancement. These findings highlight the importance of aligning climatic suitability with adaptive infrastructure investment and region-specific management to promote sustainable production-share advantages in China’s blueberry industry. Full article

Quality of life (QoL) is a multidimensional concept involving physical, psychological, social, and environmental dimensions. Therefore, it reflects not only individual well-being but also the overall well-being and sustainability of societies. Current approaches to QoL have expanded from purely economic or health-based indicators to incorporate a range of multidimensional analyses at urban, regional, and national levels, with more recent emphasis on interlinkages between socio-economic and spatial factors. This research investigates how geoinformation methodologies, including remote sensing, spatial analysis, and machine learning, can be applied to assess QoL across multiple spatial scales. Through a systematic review and comparative evaluation, the study aims to identify which indicators, data sources, and analytical tools are used at each spatial level—from neighborhood and urban scale to regional and national levels. Emphasis was placed on understanding how methodological approaches vary across scales and how spatial resolution, data availability, and urban context influence the design and implementation of QoL assessment frameworks. The main objective was to establish a common analytical framework for evaluating QoL across different spatial scales. The review revealed that combining data, machine learning algorithms, and spatial analysis approaches in a common framework will enhance comparative and predictive capabilities beyond the state of the art, although it will face significant data heterogeneity challenges. Future research aims to develop consistent, multidimensional models supportive of policies fostering sustainability and spatial equity in urban and regional contexts. Full article

Climate change and sea-level change (SLC) are intensifying flooding in U.S. coastal communities, with disproportionate impacts on Black and minority neighborhoods that face displacement, economic hardship, and heightened health risks. In Norfolk, Virginia, sea levels are projected to rise by at least 0.91 m (3 ft) by 2100, placing underserved neighborhoods such as Oakleaf Forest at particular risk. This study investigates the compounded impacts of flooding at both the building and urban scales, situating the work within the framework of the UN Sustainable Development Goals (UN SDGs). A mixed-method, community-based approach was employed, integrating literature review, field observations, and community engagement to identify flooding hotspots, document lived experiences, and determine preferences for adaptation strategies. Community participants contributed actively through mapping sessions and meetings, providing feedback on adaptation strategies to ensure that the process was collaborative, place-based, and context-specific. Preliminary findings highlight recurring flood-related vulnerabilities and the need for interventions that address both environmental and social dimensions of resilience. The study proposes multi-scale, nature-based solutions (NbS) to mitigate flooding, restore ecological functions, and enhance community capacity for adaptation. Ultimately, this work underscores the importance of coupling technical strategies with participatory processes to strengthen resilience and advance climate justice in vulnerable coastal neighborhoods. Full article

Urban tree canopy is increasingly recognized as a health-protective form of green infrastructure, yet its distribution remains uneven across socioeconomically stratified neighborhoods. This study quantifies fine-scale tree-canopy inequity across Census Block Groups (CBGs) in Baltimore and examines associations with socioeconomic deprivation and modeled pollution-related cancer risk. We integrated (i) 2023 US Forest Service canopy estimates aggregated to CBGs, (ii) Area Deprivation Index (ADI) national and state ranks, (iii) American Community Survey 5-year population counts, and (iv) EPA NATA/HAPs cancer-risk estimates aggregated to CBGs using population-weighted means. Associations were assessed using Spearman correlations and visualized with LOESS smoothers. Canopy was negatively associated with ADI national and state ranks (ρ = −0.509 and −0.503), explaining 29–31% of canopy variation. Population-weighted canopy declined from 47–51% in the least deprived decile to 13–15% in the most deprived (3.4–4.1× disparity). Beyond socioeconomic gradients, overall distributional inequity was quantified using a population-weighted Tree Canopy Inequality Index (TCI; weighted Gini), yielding TCI = 0.312, indicating substantial inequality. The population-weighted Atkinson index rose sharply under increasing inequality aversion (A_0.5 = 0.084; A₂ = 0.402), revealing extreme canopy deficits concentrated among the most disadvantaged neighborhoods. Canopy was also negatively associated with modeled cancer risk (ρ = −0.363). We constructed a Triple Burden Index integrating canopy deficit, deprivation, and cancer risk, identifying spatially clustered high-burden neighborhoods that collectively house over 86,000 residents. These findings demonstrate that canopy inequity in Baltimore is structurally concentrated and support equity-targeted greening and sustained maintenance strategies guided by distributional justice metrics. Full article

The Silicon Alley model is enhancing neighborhood competitiveness through cultural and technological innovation, while the living experience of its inhabitants serves as a critical foundation for sustainable development. This study investigates neighborhoods developed under the Silicon Alley framework. It explores theoretical models and practical pathways that use inclusive design to enhance public facilities and service strategies, ultimately aiming to build a sustainable living experience system. Utilizing a combined LDA-DEMATEL-ISM-MICMAC methodology, the research first identifies seven key factors influencing living experience from multi-source texts, spanning social, technological, emotional, and governance dimensions. It then reveals the cause-effect relationships, hierarchical structure, and driver-dependency mechanisms among these factors. The findings indicate that sustainable collaborative governance acts as a fundamental driver, diversified community experience and urban attractiveness serve as intermediate transmission factors, while Elderly-Friendly Livelihood Experience, Digital Block Experience, Artistic Life Scene Experience, and Local Cultural and Historical Experience function as surface-level outcome factors. The study proposes short-term priorities focusing on collaborative governance and social integration, and long-term strategies emphasizing livelihood services and cultural identity. These recommendations are intended to enhance neighborhood living experience, promote inclusive and sustainable urban renewal, and provide both theoretical insights and practical guidance for achieving sustainable neighborhood development. Full article

Deploying public EV charging infrastructure while balancing efficiency, equity, and implementation feasibility remains a key challenge for sustainable urban mobility. This study develops an integrated, grid-based planning framework for Wuhan that combines attention-enhanced ConvLSTM demand forecasting with a trajectory-derived, rank-based accessibility index to support equitable network expansion. Using large-scale charging-platform status observations and citywide ride-hailing mobility traces, we generate grid-level demand surfaces and an accessibility layer that helps reveal structurally connected yet underserved areas, including demand-sparse zones that may be overlooked by utilization-only planning. We screen feasible grid cells to construct a new-station candidate set and formulate expansion as a constrained three-objective optimization problem solved by NSGA-II: maximizing demand-weighted neighborhood service coverage, minimizing the Group Parity Gap between low-accessibility populations and the citywide population, and minimizing grid-connection friction proxied by road-network distance to the nearest power substation. Practical deployment plans for 15 and 30 sites are selected from the Pareto set using TOPSIS under an explicit weighting scheme. Benchmarking against random selection and single-objective greedy baselines under identical candidate pools, constraints, and evaluation metrics demonstrates a persistent coverage–equity–cost tension: coverage-driven heuristics improve demand capture but worsen parity, whereas equity-prioritizing strategies reduce gaps at the expense of coverage and feasibility. Full article

Background: Schools play a central role in child development and socialization and can function as protective environments that mitigate the effects of adversity. Building on the Social Ecological Model and Community School Transformation, we propose a “Schools-as-Neighborhoods” framework that conceptualizes schools as intentionally designed microenvironments capable of generating social capital, promoting positive childhood experiences, and buffering harmful neighborhood exposures through trauma-informed programming. Methods: We conducted a convergent mixed-methods study across four public and charter schools in Milwaukee, Wisconsin, serving grades five through nine. STRYV365’s peak team and Brain Agents gamified intervention were implemented between 2022–2024. Quantitative surveys and qualitative data assessed students’ lived experiences, exposure to adversity, emotional awareness, coping skills, and school connectedness/climate across multiple waves. Results: Across the four schools (n = 1626 students), baseline academic proficiency was low, and exposure to adversity was high among surveyed participants (n = 321), including bereavement (74%) and family incarceration (56%). Despite these challenges, qualitative findings revealed strengthened emotional regulation, empathy, motivation, and goal setting among students engaged in trauma-informed programming. Teachers reported improved peer interaction and community building during sustained implementation. Conclusion: The Schools-as-Neighborhoods framework highlights the value of trauma-informed, relationship-centered school environments in promoting student well-being. By positioning schools as cohesive ecosystems that foster belonging and cultivate social capital, this approach offers educators and policymakers a pathway for mitigating the effects of hostile lived environments and supporting students’ mental health, social development, and engagement in learning. Full article

Enhancing the social dimension of sustainability is essential for improving the livability of informal settlements, yet its evaluation is often constrained by the absence of reliable socio-economic data. This study addresses this challenge by demonstrating how a rigorous, form-based analysis can be utilized to interpret the social potential embedded within the physical structure of informal settlements. Focusing on the Saadi neighborhood in Shiraz, Iran, the research applies a validated four-part morphological framework—integrated with Space Syntax principles—to examine how specific spatial configurations create conditions supportive of social interaction and territorial security. Rather than attempting to measure social sustainability directly, the study conceptualizes physical morphology as a tangible proxy through which socially supportive spatial conditions can be inferred. The analysis reveals three critical morphological drivers: (1) a fine-grained urban fabric that directly enhances walkability and co-presence; (2) a low vertical profile that ensures visual permeability and informal surveillance; and (3) semi-private residential clusters that function as defensible space. These findings highlight how the physical form of informal settlements contains an underlying social logic that can be systematically decoded. The paper concludes that form-based analysis provides a replicable pathway for identifying the spatial scaffolding that supports community life, offering valuable insights for socially oriented upgrading strategies in data-scarce contexts. Full article

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable. Full article

Against the dual backdrop of rapid population aging and legacy neighborhood renewal, morphologically planning-locked legacy neighborhoods in high-density cities face persistent imbalances in outdoor activity spaces that undermine aging-in-place participation and health equity. This study advances a Spatial Pathology framework. Using nine representative communities in Longtan Subdistrict, Dongcheng District, Beijing, we develop a GIS-assisted spatial audit, a systematic behavioral observation protocol with temporal-intensity metrics, and a validated perception instrument. These tools form a closed evidentiary loop with explicit indicator definitions, formulas, and decision thresholds, alongside a reproducible analytic and visualization pipeline. Tri-dimensional baselines revealed substantial inter-community disparities: Spatial Quality Index (SQI) ranged from 43.3 to 77.0; activity intensity varied from 1.5 to 15.7 persons/100 m²·hour; and overall satisfaction scores spanned 3.88–4.49. It quantifies and identifies three core paradoxes in outdoor activity spaces within this context: (1) the Functional Failure Paradox with FFI exceeding +0.5 and ELR surpassing 60% in dormant communities; (2) the Value Misalignment Paradox where Facilities & Equipment showed the strongest satisfaction impact (β = 0.344) yet the largest unmet-need gap (VQGI > +8); (3) the Practice–Perception Decoupling Paradox evidenced by a negative correlation (r = −0.38) between usage intensity and satisfaction. These paradoxes reveal the spatial roots of planning-locked legacy neighborhoods—compound mechanisms of planning inertia, decision–demand information gaps, and elderly adaptability masking environmental deficits. We translate the diagnosis into typology-specific prescriptions—reactivating dormant spaces via “route–node–plane” continuity and proximal micro-spaces; decongesting peak periods through elastic zoning and equipment redistribution; and precision calibration of facilities and walking loops—implemented through co-creation and light-touch stewardship. This provides evidence-based, precision-targeted intervention pathways for micro-renewal of aging neighborhoods, supporting localized implementation of UN Sustainable Development Goals (SDG 11 Sustainable Cities; SDG 10 Reduced Inequalities). This methodological framework is transferable to other high-density aging cities, offering theoretical scaffolding and empirical reference for multi-source geographic data-driven urban spatial analysis and equity-oriented age-friendly retrofitting. Full article

The growing emphasis on sustainable manufacturing has motivated the integration of environmental and social factors into traditional assembly line balancing problems (ALBPs). This study introduces a Sustainable Mixed-Model Assembly Line Balancing Problem (S-MMALBP) that jointly considers task precedence, machine selection, worker allocation, carbon-emission control, and green-rating incentives. An exact optimization model is formulated to minimize total operating cost while satisfying sustainability and capacity constraints. To address the problem’s combinatorial complexity, an Adaptive Large Neighborhood Search (ALNS) metaheuristic is developed, incorporating customized destroy and repair operators, adaptive penalty updating, and a simulated-annealing-based acceptance criterion. An analytical lower bound is derived to evaluate the algorithm’s performance, and an enhanced constructive method, Precedence-Driven Task Grouping (PDTG), is proposed to generate high-quality initial solutions. Computational experiments on benchmark instances confirm that the ALNS achieves near-optimal solutions with deviations below 5% from the lower bound, while solving large instances within seconds. A real-world case study on aircraft assembly involving 166 tasks further validates the model’s applicability, achieving a cost deviation below 4% from the theoretical bound under realistic sustainability constraints. The results demonstrate that the proposed model provides an effective and scalable decision-support tool for designing environmentally and socially responsible production systems. The study is the first to incorporate sustainability and worker–machine decisions into a mixed-model ALB framework solved by a tailored ALNS and lower bound. Full article

The relationship between urban morphology and urban design or planning practice is inherently complex and multifaceted. A key challenge stems from the limited relevance and applicability of morphological tools to the practical issues encountered in professional planning contexts. This study seeks to address this gap by investigating how morphological analysis can inform the development of future urban plans and projects, particularly in areas where a strong historical fabric is integrated with contemporary urban structures. The research applies the Morpho method to historical urban environments, examining multiple morphological parameters, including street typology, block dimensions, plot density, the ratio of building height to street width, building alignment, and land-use distribution. Furthermore, the study introduces a new analytical criterion, the density of historical buildings within each street block, to enhance the methodological comprehensiveness of the analysis. The study also integrates the physical analysis of urban form with socio-economic dimensions by incorporating ownership status and population density data, thereby extending the framework and generating significant insights. Four neighborhoods in Beyoğlu, Istanbul, were selected as case studies to illustrate the application of this method. The results demonstrate that the Morpho analysis can serve as a robust foundation for developing planning and design codes, facilitating the harmonious integration of historical urban areas with modern urban fabrics. The study concludes by suggesting how morphological knowledge can be translated into planning action by proposing planning strategies, emphasizing their potential role in guiding sustainable urban development. Ultimately, this study has important outcomes for sustainable urban development by addressing the integration between historical and modern patterns within ongoing urban alterations, the sustainability of historical patterns, and their integration with modern fabric. These outcomes can be used in urban policies for sustainable urban development in historical areas. Full article

This study examines how platformized e-commerce logistics reshapes urban land use at the neighborhood scale, using Shanghai as an empirical case. It argues that last-mile logistics infrastructure operates through two intertwined mechanisms: as physical service nodes that generate localized pedestrian flows sustaining neighborhood retail, and as neighborhood-level execution points within a digitally coordinated logistics system that produces citywide substitution pressures and restructures commercial spaces, particularly community-oriented shopping malls. Theoretically, the study advances platform and logistics urbanism by reconceptualizing last-mile infrastructure as a dual-role urban system with scale-dependent land-use effects. Methodologically, it combines street-segment regression analysis with shopping-mall case studies to link logistics proximity to fine-grained spatial outcomes. Empirically, the findings reveal complementary effects for street retail alongside accelerated restructuring and functional repurposing in community malls—patterns not captured by uniform displacement models. Planning analysis further identifies a governance mismatch in Shanghai’s 2017–2035 Master Plan, underscoring the need for platform-responsive planning to address emerging hybrid commercial–logistics spaces. Full article

Increasing the level of livability in cities is a topical area both in EU policies and in local urban development, as well as in scientific studies. The aim of this article is to assess the degree if livability of the large housing estates (LHEs) built during the socialist period in the main regional cities of Romania and how adequately they meet the requirements of current urban life. The research focused on assessing the accessibility of these neighborhoods to green spaces and other types of public facilities and services of interest, also taking into account the typology of LHEs. For this, we used mixed research methods, namely GIS remote sensing, fieldwork, and questionnaire surveys, with the results being digitally processed and mapped. This comparative research allowed us to see to what extent each type of neighborhood lends itself to the application of the European Green Deal principles (EGD). In this sense, we mapped the transformations undergone by urban green spaces, as a major dimension of sustainability, and we created an LHE accessibility index, using eight groups of indicators. Based on this index, we assessed the degree of livability and the compatibility of LHEs with some of the EGD principles. We believe that this study can be applied in other regions with similar characteristics, for the analysis of territorial accessibility of public services, in the creation of quality of life registers, or for the planning of spatial components of green cities. Full article