Path dependency of urban resilience to recurring COVID-19 waves: the role of initial invasion contexts and urban scaling structure

Urban polycentric spatial structure and residents’ subjective well-being: The mediating role of commuting, housing and public service delivery

Exploring the relationship between urban polycentric structure and green total factor productivity in China: Insights from urban development patterns and scale borrowing

Role of urban green space structure and configuration in regulating land surface temperature in NCT Delhi using explainable artificial intelligence

Urban forest tree species and 3D structure seasonal contributions on spatial sensible heat exchange at individual tree and cluster scales

Flow-driven analysis of divergences between virtual and physical urban structures: Insights from Shenyang, China

Introduction: The configuration of the food environment directly influences the eating habits and nutritional health of the population. In Chile, although research on these environments has increased in the Metropolitan Region, gaps still exist in other urban areas such as Antofagasta, making it difficult to understand territorial inequalities in access to healthy food. Objective: Analyze the diversity of the food supply in the cities of Santiago and Antofagasta using georeferencing tools and ecological indices, characterizing territorial differences in food environments. Materials and Methods: A descriptive and comparative study was conducted in two cities in Chile. Nutrition and Dietetics students collected data by recording food establishments within a four-block radius of their homes. The establishments were classified as healthy (SAL), unhealthy (NOSAL), mixed (MIX), and supermarkets (SUPER). The data were compiled in Excel spreadsheets, and absolute and relative frequencies were calculated. Food diversity was assessed using the Shannon and Simpson indices. The results were presented in tables, graphs, and thematic maps. Results: A total of 323 establishments were identified: 165 in Santiago and 158 in Antofagasta. In both cities, unhealthy food options predominated, especially in districts like Lo Prado and Independencia. However, Antofagasta showed greater food diversity, with a more balanced distribution of establishment types, reflected in higher diversity indices (Shannon Index: 1.29 vs. 1.14; Simpson Index: 0.68 vs. 0.63). Conclusions: Both cities exhibit food environments characterized by a greater availability of unhealthy foods and intra-urban inequalities. Georeferencing and ecological indices allow for a precise characterization of the urban food structure. These findings reinforce the need for urban and public health policies aimed at improving access to healthy food and reducing territorial inequities in Chile.

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing ventilation corridors often rely on empirical weighting or linear models, which struggle to accurately reveal the resistance coefficients of resistance indicators and fail to reflect the threshold at which indicators transition between positive and negative impacts. Consequently, this study employs Shanghai, China, as a case study, integrating machine learning models with the minimum cost path (MCR) model. Key variables were screened through multiple linear regression and variance inflation factor (VIF) analysis. Subsequently, machine learning models were compared to select the optimal model, with parameter optimisation conducted using Optuna, followed by computational implementation. The results indicate that built environment factors (such as building height, shape complexity, and road density) exert a significantly greater influence on ventilation potential than natural green space factors. By introducing the SHAP method, the positive and negative effects of each indicator on the ventilation environment and their threshold relationships were revealed. Negative indicators were converted into ventilation resistance factors to construct a resistance surface. Building upon this, cold and heat sources were identified using LST, NPP, and population density data. The MCR model was then employed to calculate the minimum resistance paths from cold to heat sources, forming an urban ventilation corridor network. The results indicate that primary corridors align with prevailing wind directions, following urban rivers and low-density green spaces. This study reveals the nonlinear effects of building and green space elements on ventilation systems, proposing machine learning-based optimisation strategies for ventilation corridors. It provides quantitative decision support for mitigating the urban heat island effect and enhancing city livability.

Mobile phone data provide valuable insights into urban structure, yet traditional clustering methods assign a single label (e.g., home or work) per location, oversimplifying mixed-use areas. We present MAPLID, a supervised multi-label framework for place identification using Call Detail Records (CDRs) from Telecom Italia. The study area in Milan (23.5 k m 2 ) was divided into 10,000 grid cells, with a 20 × 20 subgrid (4.7 k m 2 ) manually labelled for home, work, and high/medium/low density. Several multi-label classifiers (Label Powerset, Binary Relevance, Classifier Chain, and ML-kNN) were evaluated using 10-fold cross-validation and metrics such as accuracy, F1-score, and Hamming loss. The best performance, achieving 88.3% average precision and leading in four of nine metrics, was achieved with Label Powerset and Random Forest applied to outgoing call data. MAPLID was further validated in the province of Trento, confirming its robustness in more irregular and forested terrain. Incorporating multimodal Point of Interest (POI) features from OpenStreetMap improved accuracy in data-sparse regions like Trento. The results demonstrate MAPLID’s capability to capture multiple concurrent urban functions and to generalise across contrasting geographic contexts, offering a computationally efficient framework for dynamic land-use mapping, urban planning, and mobility analysis using large-scale telecom data.

This article provides a detailed analysis of the morphology and urban structure of the parish of Crucita la Bella, located in the province of Manabí, Ecuador. It reveals profound urban weaknesses linked to the spontaneous growth of the parish, the disorder of land use, the lack of planning, the scarcity of green areas and the lack of public infrastructure. Despite these weaknesses, strengths have been identified that allow the formulation of concrete proposals for improvement. Through the development of thematic maps and multivariate analysis, sustainable intervention strategies are established based on road reorganization, strategic pedestrianization, the creation of green circuits and the integration of resilient solutions to climate change. These proposals focus on improving sustainability indicators such as population density, land use, accessibility, road connectivity, proportion of green spaces, energy efficiency, quality of public space and sustainable mobility. In this context, a comprehensive intervention is foreseen that will include the zoning of urban intervention polygons as general strategies, applying key urban indicators to evaluate the current state of sustainability in the locality. Thus, the article seeks to provide a useful technical and conceptual basis for decision-making in land-use planning and coastal urban resilience policies, promoting a more orderly and sustainable development in the region. This comprehensive approach will improve the quality of life of the inhabitants and strengthen the culture.

In many Latin American cities, rapid densification, shrinking public land reserves, and growing spatial, social and biophysical fragmentation have heightened the urban significance of large, private, closed open spaces (COSs). COS, marked by restricted access and social homogeneity, operate as capsular urban models that limit socio-environmental integration, urban continuity and resilience. Far from being mere enclaves, the reconfiguration of COS emerges as a critical response to contemporary urban challenges with the capacity to reshape urban structures by generating new social and spatial connectivities. This article examines the transformation of COSs in urban contexts, such as golf clubs, into accessible public landscapes as hybrid open spaces (HOSs), a topic that remains underexplored internationally. For that, this research proposes a design-oriented, multiscalar framework (city and zonal/local) that integrates open and closed spatial programs within the wider urban open space system. Considering urban, biophysical, and sociocultural dynamics, and drawing on the concepts of accessibility, connectivity, diversity, and flexibility, the study develops guidelines and design strategies for hybridising private and public recreational and environmental uses to strengthen urban integration. Using El Rodeo Gold Club in Medellín as a case study, the work contributes to landscape architecture by advancing the transformation of underutilised COS into inclusive, multifunctional HOS, positioning COS as a strategic asset for sustainable urban environments. The framework can be replicable in other similar contexts.

Progressive climate change and building morphology influence the specific microclimate of built-up areas. This has a fundamental role in research on energy use and thermal comfort inside buildings. Most studies using data for dynamic energy simulation are based on information collected at meteorological stations in rural areas. This can lead to erroneous predictions. The main goal of the study was to combine two simulation tools—ENVI-met for microclimate predictions around historical building layouts, and DesignBuilder for assessing indoor comfort. Illustrating the impact of input data on simulation results was conducted using three types of weather data: (1) from a field campaign, (2) from a suburban station, and (3) from the typical meteorological year. The obtained results confirm that the highest precision was achieved in analyses where information obtained at a real scale in the city centre was used as boundary conditions (field measurements: MAPE = 0.6 °C, RMSE = 0.7 °C). The next step was to estimate the thermal sensations inside the living room of the existing residential building. Thermal comfort was determined using the operative temperature as an indicator. Incorporating realistic urban weather inputs enhanced the reliability of indoor comfort modelling and provided a more accurate basis for planning thermal resilience in historic residential buildings.

Integrating machine learning algorithms for post-fire damage assessment and rehabilitation prioritization of urban structures

The variation of structural and compositional characteristics of urban forests is influenced by the urban landscape heterogeneity and several biotic, abiotic, and human factors. Urban forests provide numerous ecosystem and social benefits key to the wellbeing of citizens and to enhance environmental conditions in cities. However, the quantity and quality of these services are determined by the urban forest structure, composition, and spatial variation. There has been little research on the heterogeneity in the urban forest structure and composition across the entire urbanized area of Mexico City, one of the largest and most populated cities in North America. This study explores urban forest composition, diversity and structure across the entire urbanized area and within six urban land uses and the 16 boroughs of Mexico City using tree data from 500 fixed-area plots of 400 m2 distributed across the city. Alfa and beta diversity analysis, and analysis of variance revealed differences in tree diversity and structure within land uses and boroughs. Green areas had higher basal area but less species richness than the residential and the commercial-residential land-use types. The lower values of basal area and canopy cover were found in the boroughs in the east part of the city, and the highest species richness was in boroughs in the south. Land use types and boroughs are ecologically heterogeneous units (β = 0.5, β = 0.6, respectively) and urban forest planning needs to consider their specific conditions. The higher proportion of non-native species found in this study highlights the need to diversify prioritizing native species.

ABSTRACT Cities throughout the U.S. South created local planning institutions and engaged with urban planning professionals beginning in the early 1900s to address longstanding problems of unregulated development and blighted urban conditions. Central to the planning process was an effort to codify spatial segregation of black residences and businesses through implementation of racial zoning. The unconstitutionality of racial zoning shifted the strategy to embedding racial data in local planning processes to provide guidance to public investment and planning initiatives to sustain residential segregation. The increasing role of federal investments in military spending, urban renewal, highway development, and public housing from the 1930s through the 1960s accelerated transformation of center city neighborhoods while sustaining the segregation system. Concurrently, these planned interventions contributed to expansion of the city footprint and transformation of the South by the early 21st century to the region in the US whose urban social spatial structure was dominated by sprawl. Within this new urban pattern, however, southern center cities retained residential segregation of their black population. While no longer sanctioned by law, racial residential segregation remained a defining facet of southern urban life and largely attributable to the region's planning legacy.

Population migration is an important indicator for measuring the interactions and connections between cities. Analyzing the spatiotemporal distribution pattern of the migration flows between cities is highly important to understanding urban development relationships and regional structures. From a spatiotemporal perspective, we conduct STFlowLISA (Space-Time Flow Local Indicator of Spatial Association) spatiotemporal autocorrelation analysis using population migration data from Hubei Province from 2018 to 2023 and, on this basis, calculate the spatiotemporal hub index and identify spatiotemporal clusters. The research aims to reveal the regional spatial structure shaped by migration flows and compare it with that of existing town system planning to evaluate deviations and provide a decision-making basis for the regional synergistic development of Hubei Province. The key findings include: (1) the population migration flows between Wuhan and its surrounding cities mostly exhibit a spatiotemporal distribution pattern of HH (high-value agglomeration), whereas the long-distance migration flows between eastern and western Hubei mostly follow a pattern of LL (low-value agglomeration), and these urban connections show improvement after the epidemic; (2) the spatiotemporal hubs of Hubei Province demonstrate a “core-periphery” structure with Wuhan as the absolute core, while Xiangyang’s role as a subcenter does not meet planning expectations; and (3) urban spatiotemporal clusters are dense in the east and sparse in the west, with Enshi and Shiyan showing disconnection from the main network, which deviates from the planned polycentric spatial pattern. By examining the spatiotemporal autocorrelation of migration flows, this study enriches the empirical understanding of regional spatial structure in Hubei Province within the framework of classical spatial theory and highlights the necessity of incorporating dynamic flow analysis into regional planning and policy-making.

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models—Proportional–Integral–Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)—were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision–Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms.

The development of Madiun from 1900 to the 2000s was shaped by political, economic and infrastructure changes that influenced its urban structure. This research examines the city's transformation from the colonial (1990-1945) to the reform era (1998-now), focusing on the relationship between accessibility and the growth of new economic centres. Although previous studies have explored its development, they lack insights into this specific connection. Using a spatial and diachronic approach, the study analyses road networks, economic centres and urban structures using historical maps, together with journals, field surveys and satellite imagery. The data were processed in several stages: (1) georeferencing and digitising of the historical maps to align with contemporary spatial data; (2) mapping of road networks and economic centres using GIS-based network analysis; (3) land use classification and detection of changes from satellite imagery; and (4) validation of the results through field surveys at key locations. These steps, undertaken with the use of ArcGIS Pro, produced diachronic maps detailing urban changes and the spatial relationship between accessibility and economic growth. The findings reveal that accessibility, particularly through transportation networks, has been a key driver of new economic centres. Areas with high accessibility tend to host diverse and intensive economic activities. These centres have a significant influence on Madiun’s urban structure, with agricultural land decreasing from 47.01% in 1984 to 34.57% in 2024, while built-up areas, including residential zones, expanded from 52.99% to 65.43%. The expansion of residential areas, which reached 57.5% during the reform era, indicates a major shift in land use, driven by accessibility and economic growth. The centres also have a strong influence on Madiun's urban structure, with a transition from agriculture to residential, commercial and industrial functions. The research highlights the role of transportation infrastructure in shaping economic development and urban adaptation. It contributes valuable insights into the interplay between accessibility and economic centres, offering a reference for future urban planning to help foster sustainable growth in cities such as Madiun.

Environmental courts play an important role in upholding environmental justice and promoting green development. Drawing on the staggered establishment of environmental courts in China since 2007, we adopt a multi-period difference-in-differences (DID) framework to assess their impact on green total factor productivity (GTFP). First, we find that cities with environmental courts tend to experience increases in GTFP after their establishment. Second, this effect is more pronounced in regions with stronger governmental environmental commitment, higher enforcement capacity, and non-resource-based urban structures. Third, the mechanism analysis suggests that cities with environmental courts tend to achieve higher GTFP by fostering green innovation, enhancing pollution control, and raising public environmental attention. By adopting an institutional perspective, this paper offers new empirical evidence on the role of environmental courts in advancing green development.

High-rise building areas (HRBs), a key urban land-cover type defined by distinct morphological and functional characteristics, play a critical role in urban development. Their spatial distribution and temporal dynamics serve as essential indicators for quantifying urbanization and analyzing the evolution of urban spatial structure. This study addresses the dynamic monitoring needs of HRBs by developing a temporal change detection model, TCA-Unet (Temporal Change-Aware U-Net), based on a temporal change-aware attention module. The model adopts a dual-path design, combining a temporal attention encoder and a change-aware encoder. By explicitly modeling temporal difference features, it captures change information in temporal remote sensing images. It incorporates a multi-level weight generation mechanism that dynamically balances temporal features and change-aware features through an adaptive fusion strategy. This mechanism effectively integrates temporal context and enhances the model’s ability to capture long-term temporal dependencies. Using the Xiong’an New Area and its surrounding regions as the study area, experiments were conducted using Sentinel-2 time-series imagery from 2017 to 2024. The results demonstrate that the proposed model outperforms existing approaches, achieving an overall accuracy (OA) of 90.98%, an F1 score of 82.63%, and a mean intersection over union (mIoU) of 72.22%. Overall, this study provides an effective tool for extracting HRBs for dynamic monitoring and offers valuable guidance for urban development and regulation.