Search Results (65)

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly rely on microclimate numerical simulations, while comparative assessments of OTC from the human thermal perception perspective remain limited. This study employs the thermal walk method, integrating microclimatic measurements with thermal perception questionnaires, to conduct on-site OTC investigations across three urban blocks with contrasting spatial morphologies—a business district (BD), a residential area (RA), and a historical neighborhood (HN)—in Beijing, a hot summer and cold winter climate city. The results reveal substantial OTC differences among the blocks. However, these differences demonstrated great seasonal and temporal variations. In summer, BD exhibited the best OTC (mTSV = 1.21), while HN performed the worst (mTSV = 1.72). In contrast, BD showed the poorest OTC in winter (mTSV = −1.57), significantly lower than HN (−1.11) and RA (−1.05). This discrepancy was caused by the unique morphology of different blocks. The sky view factor emerged as a more influential factor affecting OTC over building coverage ratio and building height, particularly in RA (r = 0.689, p < 0.01), but its impact varied by block, season, and sunlight conditions. North–South streets generally perform better OTC than East–West streets, being 0.26 units cooler in summer and 0.20 units warmer in winter on the TSV scale. The study highlights the importance of incorporating more applicable physical parameters to optimize OTC in complex urban contexts and offering theoretical support for designing climate adaptive urban spaces. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

Urban ventilation corridors play a critical role in improving wind environments, mitigating the urban heat island (UHI) effect, and enhancing urban climate resilience. Traditional Computational Fluid Dynamics (CFD) methods offer high accuracy in simulating wind fields but are computationally intensive and inefficient for large-scale, multi-scenario urban planning tasks. To address this limitation, this study proposes a morphology-driven, machine learning-based framework for ventilation corridor identification. The method integrates Lattice Boltzmann Method (LBM) simulations, neighborhood-based feature normalization, and a random forest regression model to establish a predictive relationship between morphological indices and wind speed distributions under prevailing wind conditions. Input features include raw and log-transformed LBM values, neighborhood-normalized indicators within multiple radii (100–2000 m), and porosity statistics. The model is trained and validated using CFD-simulated wind speeds, with the dataset randomly divided into training (80%), validation (10%), and testing (10%) subsets. The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. Primary corridors are closely aligned with large rivers and lakes, while secondary corridors are shaped by arterial roads and localized open spaces. Compared with conventional approaches such as FAI classification, Least Cost Path (LCP), and circuit theory models, the proposed framework offers higher spatial resolution and better alignment with the CFD results while significantly reducing computational cost. This study demonstrates the feasibility of using morphological and data-driven approaches to support efficient and scalable urban ventilation analysis, providing valuable guidance for climate-responsive urban design. Full article

Accurate extraction of dynamically evolving haul roads in open-pit mines from very-high-resolution (VHR) satellite imagery remains a critical challenge due to domain gaps between urban and mining environments, prohibitive annotation costs, and morphological irregularities. This paper introduces OMRoadNet, an unsupervised domain adaptation (UDA) framework for open-pit mine road extraction, which synergizes self-training, attention-based feature disentanglement, and morphology-aware augmentation to address these challenges. The framework employs a cyclic GAN (generative adversarial network) architecture with bidirectional translation pathways, integrating pseudo-label refinement through confidence thresholds and geometric rules (eight-neighborhood connectivity and adaptive kernel resizing) to resolve domain shifts. A novel exponential moving average unit (EMAU) enhances feature robustness by adaptively weighting historical states, while morphology-aware augmentation simulates variable road widths and spectral noise. Evaluations on cross-domain datasets demonstrate state-of-the-art performance with 92.16% precision, 80.77% F1-score, and 67.75% IoU (intersection over union), outperforming baseline models by 4.3% in precision and reducing annotation dependency by 94.6%. By reducing per-kilometer operational costs by 78% relative to LiDAR (Light Detection and Ranging) alternatives, OMRoadNet establishes a practical solution for intelligent mining infrastructure mapping, bridging the critical gap between structured urban datasets and unstructured mining environments. Full article

Notwithstanding the Korean Urban Regeneration Act 2013’s support for sustainable neighborhood regeneration programs, the number and scale of such projects relative to large-scale urban redevelopment remain limited. To address this imbalance, this research advances existing form-based approaches through a multi-scalar morphological analysis encouraging harmonized urban transformation and sustainable urban regeneration. The analysis encompasses the macroscale (metropolitan area development), mesoscale (urban characterization of the central urban area), and microscale (aging urban fabric detailed analysis). The case study focuses on Daegu, a major Korean city experiencing population decline. Mappings and quantitative and qualitative analysis used Geographic Information System QGIS, as well as the Python suite Momepy. The study revealed that large-scale urban redevelopments are driving urban densification and demographic shifts. While older low-rise structures occupy most of the urban landscape in the central city area, piecemeal high-rise redevelopment is increasingly fragmenting it. The overly fine urban grain resists regeneration, limiting car access, building scales, and urban density. The research findings help identify the urban areas that are most appropriate for urban regeneration and redevelopment projects and streamline and coordinate planning efforts and the adjusting of regulations. The method developed is transferable to other Korean and international cities, fostering sustainable urban regeneration. Full article

Urban vulnerability creates structural imbalances, leading to unsafe conditions and urban decline. One of the key root causes of urban vulnerability is significant changes in urban layout morphology, which significantly influences the determination of accessibility potential, causing some areas to grow while others decline. This study aims to examine the morphological structural factors that influenced physical vulnerability, with a focus on commercial buildings, which were affected by the transformation of urban structure resulting from the layout and connectivity of the transportation network at the global, local, and community levels, depending on their location; these factors contribute to spatial vulnerability in varying degrees. This study applied an indicator-based quantitative research methodology, constructing a Physical Vulnerability Index (PVI) by using Principal Component Analysis (PCA) to create new factors or components and compare physical vulnerability levels across different areas. The research findings found that the most influential morphological structural factor on physical vulnerability was micro-level morphology, primarily due to the relationship between the configuration of space and the level of usage popularity. The second most influential factor is macro-level morphology, resulting from the relationship between the accessibility potential of urban-level and neighborhood-level transportation networks. Full article

Analyzing how urban ritual spaces transform into everyday living environments is crucial for understanding the spatial structure of contemporary historical districts, particularly in the context of ancestral temples. However, existing research often neglects the integration of both building-level and block-level perspectives when examining such spatial transitions. Grounded in urban morphological principles, we identify the fundamental spatial units of ancestral temples and their surrounding blocks across the early 20th century and the post-1970s era. Using the topological characteristics of an access structure, we construct corresponding network graphs. We then employ embeddedness and conductance metrics to quantify each temple’s changing position within the broader block structure. Moreover, we apply community detection to uncover the structural evolution of clusters in blocks over time. Our findings reveal that, as institutional and cultural factors drive spatial change, ancestral temples exhibit decreased internal cohesion and increased external connectivity. At the block scale, changes in community structure demonstrate how neighborhood clusters transition from a limited number of building-based clusters to everyday living-oriented spatial clusters. These insights highlight the interplay between everyday life demands, land–housing policies, and inherited cultural norms, offering a comprehensive perspective on the secularization of sacred architecture. The framework proposed here not only deepens our understanding of the spatial transformation process but also provides valuable insights for sustainable urban renewal and heritage preservation. Full article

Clarifying the mechanisms by which the micro-scale built environment influences urban vitality is an important scientific challenge, to guide precise urban planning in the context of urban renewal. In this study, we quantify the intensity of human activities through Baidu heat maps, analyze social interaction patterns using social media check-in data, and integrate built environment elements such as road network topology, 3D building morphology, and the spatial distribution of points of interest (POIs). A machine learning technique combining a real-encoded Accelerated Genetic Algorithm-Projective Pathfinding Model (RAGA-PPM) and Shapley Additive Projection for Interpretability (SHAP) for Interpretability Analysis (IPA) was used to investigate the nonlinear mechanisms of 17 factors affecting urban vitality in Macau Peninsula, China. Firstly, the explanatory power of the built environment for comprehensive vitality was significantly better than the other dimensions. Two factors, population vitality and microblogging check-in vitality, contributed the most to the composite vitality value. Secondly, road network density was the most important built environment factor affecting urban vitality in Macau Peninsula (SHAP = 0.025). Finally, the impacts of built environment factors on urban vitality showed nonlinearities, and the threshold effects of the core factors (road network density, spatial fractal dimension, and openness to the sky) showed a consistent neighborhood-level pattern. This study establishes a framework for micro-vitality mechanisms in high-density cities, addressing the limitations of traditional methods in modeling complex nonlinear relationships. The methodological integration of RAGA-PPM and SHAP advances the innovative paradigm of applying interpretable machine learning to the study of urban form. Full article

The contradiction between urban density and sustainable environmental development is increasingly prominent. Although numerous studies have examined the impact of urban density on air pollution at the macro level, most previous research at the micro scale has either neglected socioeconomic factors, failed to analyze heterogeneous effects, or ignored historic neighborhoods where high pollution coexists with high density. By considering population, commercial buildings, vegetation, and road factors, an integrated social-biophysical perspective was introduced to evaluate how urban density influences PM_2.5 concentration in a historic neighborhood. The study area was divided into 56 units of 120 m × 150 m granularity, as determined by the precision of the LBS population data. The lasso regression and quantile regression were adopted to explore the main factors affecting PM_2.5 and their heterogeneous effects. The results showed that (1) building density was the most important driving factor of pollutants. It had a strong and consistent negative effect on PM_2.5 concentrations at all quantile levels, indicating the homogeneity effect. (2) Short-term human mobility represented by the visiting population density was the second main factor influencing pollutants, which has a significantly positive influence on PM_2.5. The heterogeneous effects suggested that the areas with moderate pollution levels were the key areas to control PM_2.5. (3) Vegetation Patch Shape Index was the third main factor, which has a positive influence on PM_2.5, indicating the complex vegetation patterns are not conducive to PM_2.5 dispersion in historic neighborhoods. Its heterogeneous effect presented a curvilinear trend, peaking at the 50th quantile, indicating that moderately polluted areas are the most responsive to improvements in vegetation morphology for PM_2.5 reduction. These findings can provide effective support for the improvement of air quality in historical neighborhoods of the city’s central area. Full article

Improving microclimate conditions is a pivotal aspect of urban design, particularly in hot, arid climates, where it directly influences outdoor comfort, mitigates the urban heat island (UHI) effect, and reduces the indoor cooling energy demand. The objective of this study is to quantitatively assess the impacts of neighborhoods’ urban size when combined with compact streets’ geometry regarding the outdoor thermal comfort generated in a typical vernacular settlement of the Saharan region of Algeria. The Ksar of Al-Atteuf in the city of Ghardaïa is taken as a case study. The related interior thermal conditions of buildings assumed to be potentially affected by the urban morphology are also examined. To study the effectiveness of the two urban morphology parameters (i.e., urban size and compactness) on outdoor and indoor thermal conditions, a mixed methods approach was adopted, integrating in situ climatic measurements and dynamic simulations. Indoor temperatures were examined in a traditional house located in the core of the Ksar. Year-round operative temperature (OT) simulations were achieved using the Ladybug tool within Grasshopper, and they were complemented by the Universal Thermal Climate Index (UTCI) values calculated during peak hot and cold weeks. Furthermore, a parametric analysis was conducted, focusing on the thermal performance of the compact urban fabric by varying progressively the neighborhood sizes from 20 m, 40 m, and 60 m. The results indicate stable indoor thermal conditions across the monitored residential building, which suggests that the architectural envelope is closely affected by its immediate surroundings. On the other hand, the UTCI analysis revealed significant differences in outdoor thermal comfort since the larger urban area provides better mitigation of heat stress in summer and cold stress in winter, the improved outdoor thermal conditions generated at the neighborhood level, being proportional to the size of the urban area. The findings underscore the value of compact urban fabrics in creating climate-responsive built environments and provide further insights into sustainable urban planning and energy-efficient design practices in hot, arid regions. Full article

A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a “morphology generation–performance evaluation–morphology optimization” framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a “morphology–performance” dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor–area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume–Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization. Full article

In recent climate-adaptive design strategies, there has been a growing interest in creating healthy and comfortable urban microclimates. However, not enough attention has been paid to the influence of street interface morphology in order to better understand the wind–thermal conditions of various commercial streets within the city and create a sustainable built environment. This research summarizes and categorizes commercial streets according to their functions and types of attributes and then abstracts the ideal models of three types of typical commercial streets to explore the effects of changes in specific morphological parameters on their wind–thermal environments. Firstly, this study selects out design parameters that affect the street interface morphology. Then, it uses the numerical simulation software PHOENICS2019 to simulate and investigate the effects of three types of typical commercial street interface morphology on their wind environment and thermal comfort. The results show that (1) in neighborhood-commercial streets, reducing void ratio and variance of height fluctuations can enhance the average wind speed of the street while reducing average temperature and improving the thermal comfort; (2) in business-office streets, the value of the void ratio is negatively correlated with the wind environment and thermal comfort, while the changes in the variance of height fluctuations and the average aspect ratio are positively correlated; and (3) in comprehensive-commercial streets, the decrease of the void ratio will reduce the average wind speed of its street and increase the average temperature, thus weakening the thermal comfort of pedestrians. In contrast, the variance of height fluctuations as well as the average aspect ratio do not significantly affect its wind–thermal environment. These conclusions from this research provide a theoretical basis and methodological reference for the creation of safer, resilient and sustainable built environments. Full article

PM2.5 air pollution is a critical global health issue. This paper introduces an innovative framework to explore the multi-scale relationship between urban morphology and PM2.5 concentrations. An enhanced Land Use Regression (LUR) model integrates geographic, architectural, and visual factors, enabling analysis from neighborhood to regional scales. A stratified sampling strategy, combined with standardized mobile monitoring and fixed-site data, establishes a robust and verifiable data collection methodology. Cross-validation (CV R² > 0.70) further confirms the model’s reliability and robustness. The nested buffer analysis reveals scale-dependent effects of urban morphology on PM2.5 concentrations, providing quantitative evidence for planning interventions. Quantitative analysis shows land use (β = 0.42, p < 0.01), visual factors (β = 0.38, p < 0.01), and building density (β = 0.35, p < 0.01) in descending order of influence. Geographic factors are significant at the regional scale (2000–3000 m) while architectural parameters dominate at the neighborhood scale (50–500 m), informing both macro-scale spatial optimization and micro-scale design. This framework, through standardized parameters and reproducible procedures, supports cross-regional and cross-scale air quality assessments, providing quantitative metrics for urban planning, neighborhood optimization, and public space design. Full article

This research paper examines threshold spaces in traditional housing within historic Indian cities, emphasizing how these transitional areas are shaped by cultural, social, and environmental influences. It underscores that thresholds function beyond mere physical divisions between interior and exterior; they are intricate spatial elements that engage the senses and perception. Key findings include the following: (a) Historical evolution: traditional dwellings display layers of history, shaped by cultural, economic, and climatic factors. (b) Character and morphology: this paper explores the qualities and structures of threshold spaces, analyzing features such as transparency, material choice, hierarchy, and enclosure. (c) Social significance: thresholds play essential roles, supporting a variety of activities, providing shelter, defining boundaries, enhancing community interaction and security, and contributing to residents’ identities. (d) Design implications: insights from this study suggest that a deeper understanding of these spaces can enhance design strategies for transitional areas in housing, highlighting their functional and socio-cultural value. The study adopts a structured comparative analysis of six case studies, evaluated at four scales—township, neighborhood, dwelling, and threshold—focusing on spatial parameters including design, function, definition, structure, and sequence. Overall, this paper highlights the critical role of threshold spaces in architecture and urban design, demonstrating their potential to enhance social interaction, define spatial relationships, and reflect cultural significance in contemporary design practices. Full article

Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m² aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R² coefficients ranging from 0.879 to 0.997 with the majority exceeding an R² of 0.95, all with p-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience. Full article