Search Results (280)

This study presents a spatially explicit, multidecadal analysis of how land use and land cover (LULC) change and socio-demographic dynamics have influenced land surface temperature (LST) patterns in the Phoenix metropolitan area between 2001 and 2021. Using Landsat-derived summer LST, socio-demographic indicators, and land cover data, we quantify urban land transformation and socio-demographic changes over two decades. To account for spatial heterogeneity, we apply Multiscale Geographically Weighted Regression (MGWR), which improves upon conventional regression models by allowing for variable-specific spatial scales. Results show that the 2001–2011 period was characterized by rapid suburban expansion and widespread conversion of croplands and open space to higher-intensity development, while 2011–2021 experienced more limited infill development. Correlation analysis reveals that agricultural and open space conversions were linked to population and housing growth, whereas redevelopment of existing urban areas was often associated with socio-demographic decline. MGWR results highlight that agricultural land conversion drives localized warming, while shrub/scrub-to-developed transitions are linked to broader-scale cooling. By combining spatial sampling, area-weighted interpolation, and MGWR, this study offers a fi-ne-grained assessment of urban thermal dynamics in a fast-growing desert region. The findings provide actionable insights for planners and policymakers working toward sustainable and climate-resilient urban development in arid environments. Full article

Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This study proposes an effective method for extracting urbanization intensity by integrating Sentinel-1, Sentinel-2, and its derived synthetic aperture radar and spectral indices features, combined with texture features. The small baseline subset interferometric synthetic aperture radar technique was employed to monitor land subsidence in Chongqing between 2018 and 2024. Furthermore, the relationships among urbanization intensity, metro construction, groundwater dynamics, and land subsidence were systematically analyzed. Finally, geographical detector and multiscale geographically weighted regression models were employed to explore the interactive effects of anthropogenic, topographic, geological-tectonic, climatic, and land surface characteristic factors contributing to land subsidence. The findings reveal that (1) the method proposed in this paper can effectively extract urbanization intensity and provide an important approach to analyze the influence of urbanization on land subsidence. (2) Land subsidence along newly opened metro lines was more pronounced than along existing lines. The shorter the interval between metro construction completion and the start of operation, the greater the subsidence observed within the first 3 months of operation, which indicates that this interval influences land subsidence. (3) Overall, groundwater dynamics and land subsidence showed a clear correlation from June 2022 to June 2023, a phenomenon largely caused by the extreme summer high temperatures of 2022, triggering reduced precipitation and a notable groundwater decline. Beyond this period, however, only a weak correlation was observed between groundwater fluctuations and land subsidence trends, indicating that other factors likely dominated subsidence dynamics. (4) The anthropogenic factors have a higher relative influence on land subsidence than other drivers. In terms of q-value, the top six factors are road network density > precipitation > elevation > enhanced normalized difference impervious surface index > population density > nighttime light, while distance to fault exhibits the least explanatory power. Given Chongqing’s exemplary status as a mountainous city, this study offers a foundational reference for subsequent quantitative analyses of land subsidence and its drivers in other mountainous cities worldwide. Full article

Urban rail transit, as a green, environmentally friendly, safe, and efficient mode of transportation, plays a crucial role in urban sustainable development. However, the influencing mechanism of build environment factors on rail transit ridership still needs to be further investigated. Also, the interaction effects between these factors have not been considered. This study aims to explore the relationship and impact of built environmental factors on metro ridership. The research employs the Multiscale Geographically Weighted Regression (MGWR) model to analyze the temporal and spatial effects of built environmental factors on the rail transit ridership. The GeoDetector model is utilized to investigate the interactive effects of these factors on rail transit ridership. The Shanghai Metro ridership data and built environment data are applied to validate the model. Based on data analysis results, we found that Food & Beverages and Accommodation services, respectively, have the greatest impact on metro ridership on weekdays and weekends. Furthermore, the interaction effects between other variable and Land use diversity significantly enhance rail transit ridership, validating the promoting effect of land use diversity on metro ridership. By proposing recommendations for relevant urban planning and policy formulation, we can foster the sustainable development of urban rail transit. Full article

With the rapid advancement of urbanization, urban heat environment issues have become increasingly severe, presenting significant challenges to sustainable urban development. Although previous research has demonstrated the substantial impact of urban morphology on land surface temperature (LST), there is still a lack of comprehensive research on the non-stationary effects of urban morphology on seasonal LST at the block scale. Therefore, this study establishes a comprehensive research framework, utilizing urban functional zones in the core area of Chongqing as the primary research unit, to investigate the seasonal fluctuations in the spatial distribution of LST across various functional zones. Combining Random Forest (RF) with multiscale geographically weighted regression methods (MGWR), the study systematically analyzes the numerical and spatial distribution characteristics of how urban morphology factors influence LST from global and local perspectives. The results indicate that (1) the LST in central Chongqing exhibits marked seasonal variation and a distinct “mountain-water pattern,” with industrial zones consistently hotter and public service areas cooler; (2) biophysical surface parameters and building morphological indicators make a high relative contribution to LST changes across seasons, particularly in public service and commercial areas; (3) building density (BD) and biophysical surface parameters primarily exert local impacts on LST changes, while floor area ratio (FAR) and building height range (RBH) have a global effect. These findings provide new insights into the driving mechanisms of urban heat environments and offer scientific evidence for regulating and mitigating urban heat environment issues across different seasons and urban types. Full article

Fine spatial information of precipitation plays a significant role in regional eco-hydrological studies but remain challenging to derive from satellite observations, especially in complex terrain areas. Sichuan Province, located in the southwest of China, has a highly variable terrain, and the spatial distribution of precipitation exhibits extreme heterogeneity and strong autocorrelation. Multi-scale Geographically Weighted Regression (MGWR) and Random Forest (RF) were employed for downscaling the Global Precipitation Measurement Mission (GPM) products based on high spatial resolution terrain, vegetation, and meteorological data in Sichuan province, and their specific effects on gauged precipitation accuracy and spatial precipitation distributions have been analyzed based on the influences of environmental variables. Results show that the influence of each environmental factor on the distribution of precipitation at different scales was well represented in the MGWR model. The downscaled data showed good spatial sharpening effects; additionally, the biases in the overestimated region were well corrected after downscaling. However, when based on spatial autocorrelation and considering adjacent influences, the MGWR performed poorly in correcting outlier sites adjacent to the high–high clusters. Compared with MGWR, relying on independently constructed decision trees and powerful regression capabilities, superior correction for outlier sites has been achieved in RF. Nevertheless, the influence of environmental variables reflected in RF differs from actual conditions, and detailed characteristics of precipitation spatial distribution have been lost in the downscaled results. MGWR and RF demonstrate varying applicability when downscaling GPM products in complex terrain areas, as they both improve the ability to finely depict spatial information but differ in terms of texture property expression and precipitation bias correction. Full article

PM_2.5 pollution is a significant environmental problem in global urbanization. However, traditional macro-scale studies are constrained by data resolution limitations, failing to accurately characterize the microscale built environment or thoroughly investigate its spatially heterogeneous effects on PM_2.5 concentrations. To address this gap, this study constructs a multidisciplinary framework of “Street View Imagery element extraction–spatial heterogeneity modeling–planning strategy optimization” with Xi’an as the case. Leveraging machine learning techniques, the study employs the ResNet50 deep learning model and the ADE20K dataset to precisely extract ten microscale built environment factors from tens of thousands of street view images. Combined with the High-resolution and High-quality Ground-level PM_2.5 Dataset for China, Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models were used to systematically reveal the impacts of the microscale built environment on PM_2.5 concentrations. Ten built environment factors were identified with varying spatial heterogeneity in their effects on the PM_2.5 concentrations, as follows: (1) factors with positive effects, in descending order of strength, include building, wall, fence, tree, sky, and grass; (2) factors with negative effects, in descending order of strength, include sidewalk, plant, and car; (3) compared with other factors, the road factor showed a relatively weaker effect. This research provides decision-making support for targeted urban planning and environmental protection, while offering valuable references for air pollution control in other cities. Full article

Traditional villages are widely recognized as vast cultural treasure troves, characterized by diverse historical elements and distinctive spatial forms. Within this context, historical factors exert varying degrees of influence on spatial configurations, and each type of space preserves a distinct facet of historical memory. Taking Nankou Village in Beijing as a case study, this paper selects three distinct periods as entry points to explore how historical elements affect the spatial morphology of villages through Multiscale Geographically Weighted Regression analysis and standard deviation ellipse methods. Under the theories of Halbwachs and Nora, this research analyzes the role of village spaces in the bearing and reshaping of historical memory. It further employs qualitative materials to inversely verify the quantitative results, thereby cross-validating the findings through both quantitative and qualitative perspectives in exploring the interactions between “historical elements” and “spatial morphology”. This integrated approach culminates in the innovative proposal of a “history–space” inter-construction mechanism. The findings reveal that different types of historical elements have significant influences on guiding and reshaping spatial features, exhibiting strong spatial heterogeneity. When multiple historical elements are coupled, the evolution of village morphology shows distinct phases, directions, and expansive characteristics. As these spaces undergo continuous practice, they drive the reconstruction of memory and reinterpretation of historical significance. Ultimately, Nankou Village has developed a unique “history–space” inter-construction mechanism, which uncovers the inherent logic of their ongoing evolution. This mechanism further holds theoretical extrapolative value for other historically and culturally significant villages. This study underscores the importance of integrating research and preservation of intangible cultural elements and physical spaces, providing critical insights into understanding spatial evolution patterns of traditional villages and their influencing factors. Full article

Housing markets worldwide have undergone major disruptions during the COVID-19 period, raising questions about how systemic shocks reshape housing preferences and spatial structures. This study develops an integrated spatial framework to examine multi-dimensional housing market restructuring, combining global and local modelling with network-based submarket delineation. Using Shanghai as a case study, we compare pre- and post-pandemic conditions (2019 and 2023) to explore fluctuations in housing prices, shifts in attribute effects, and reconfiguration of submarkets. The results reveal highly differentiated market responses across space. A dual restructuring is observed: decentralisation within the urban core and reinforced integration of outer-peripheral areas into the metropolitan centre, suggesting a gradual transition from a monocentric system towards a more polycentric and context-dependent housing landscape. Methodologically, the study proposes a transferable framework for analysing spatial restructuring under systemic shocks. Empirically, it provides fine-grained evidence of housing market reconfiguration across spatial scales, offering practical insights for spatially informed urban planning and housing market management. Full article

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R² = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors. Full article

Severe depression is shaped by complex interactions between public health crises and socioeconomic conditions, yet the spatial and temporal dynamics of these factors remain underexplored. This study investigates the impact of COVID-19 case rates, vaccination rates, and socioeconomic factors on severe depression rates across 1470 counties in the contiguous USA in 2021 and 2022. We combined Ordinary Least Squares (OLS) regression with Multiscale Geographically Weighted Regression (MGWR) to capture both global associations and local geographic variability. Results show that higher COVID-19 case rates in 2021 were associated with increased rates of severe depression in 2022, while higher vaccination rates during the same period were associated with decreased rates of severe depression. However, these associations weakened when using 2022 data, suggesting a temporal lag in the impact on mental health. MGWR analyses revealed regional disparities: COVID-19 case rates had a stronger impact in the Midwest, while vaccination benefits were more pronounced on the West Coast. Additional factors, such as unemployment, limited sunlight exposure, and the availability of mental health resources, also influenced outcomes. These findings underscore the importance of temporally and geographically nuanced approaches to public mental health interventions and support the need for region-specific strategies to address mental health disparities in the wake of public health crises. Full article

Against the global biodiversity crisis, arid and semi-arid regions are sensitive indicators of terrestrial ecosystems. However, research on their habitat quality (HQ) evolution mechanism faces dual challenges: insufficient multi-scale dynamic simulation and fragmented driving mechanism analysis. To address these gaps, this study takes northern China’s arid and semi-arid regions as the object, innovatively constructing a “pat-tern-process-mechanism” multi-dimensional integration framework. Breaking through single-model/discrete-method limitations in existing studies, it realizes full-process integrated research on regional HQ spatiotemporal dynamics. Based on 1990–2020 Land Use and Land Cover Change (LUCC) data, the framework integrates the InVEST and PLUS models, solving poor continuity between historical assessment and future projection in traditional research. It also pioneers combining the XGBoost-SHAP model and Geographically and Temporally Weighted Regression (GTWR): XGBoost-SHAP quantifies nonlinear interactive effects of natural, socioeconomic, and landscape drivers, while GTWR explores spatiotemporal heterogeneous mechanisms of landscape pattern evolution on HQ, effectively addressing the dual challenges. Results show the following: (1) In 1990–2020, cultivated and construction land expanded, with grassland declining most notably; (2) Overall HQ decreased by 0.82%, with high-value areas stable in the west and northeast, low-value areas concentrated in the central region, and 2030 HQ optimal under the Ecological Protection (EP) scenario; (3) Natural factors contribute most to HQ change, followed by socioeconomic factors, with landscape indices being least impactful; (4) Under future scenarios, landscape Patch Density (PD) has the most prominent negative effect—its increase intensifies fragmentation and reduces connectivity. This study’s method integration breakthrough provides a quantitative basis for landscape pattern optimization and ecosystem management in arid and semi-arid regions, with important scientific value for promoting integration of landscape ecology theory and sustainable development practice. Full article

Rapid urbanization fundamentally transforms how residents perceive and interact with built environments, yet the dynamic relationships between these evolving perceptions and street vitality remain inadequately understood. As cities undergo rapid transformation, traditional assumptions about fixed perception–vitality relationships may no longer hold, necessitating a deeper understanding of how these relationships evolve over time and space. This study aims to investigate how multiple dimensions of built environment perception influence street vitality and how these relationships evolve spatially and temporally in rapidly urbanizing contexts. We developed a multi-level interpretative framework combining Multi-scale Geographically Weighted Regression (MGWR) with machine-learning-based SHAP analysis to analyze multi-source data from Hohhot, China, spanning 2019–2023. Our approach examined four key perception dimensions—comfort, safety, convenience, and pleasure—and their impacts on street vitality patterns during a period of intensive urban development. The analysis reveals three major findings: first, perception–vitality relationships evolved from highly heterogeneous spatial patterns toward increasing homogenization over time, suggesting urban development standardization effects driven by rapid urbanization processes. Second, several perception dimensions underwent significant transformations, with safety perception shifting from negative to positive influence and convenience perception displaying complex nonlinear threshold effects as urban infrastructure matured. Third, the relative importance of perception dimensions changed over time, reflecting evolving urban priorities and resident expectations shaped by urbanization experiences. These findings demonstrate that perception–vitality relationships are dynamic rather than static, challenging assumptions about fixed environmental effects in urban planning. The study provides empirical evidence for implementing adaptive, context-sensitive urban interventions that acknowledge both spatial heterogeneity and temporal evolution, offering valuable insights for enhancing street vitality in rapidly urbanizing environments worldwide. Full article

Clarifying how the community-scale built environment shapes the spatial heterogeneity of cardiovascular disease (CVD) prevalence is essential for precision urban health interventions. We integrated CVD prevalence data from the Guangxi Zhuang Autonomous Region Hospital (2020–2022) with 14 built-environment indicators across 77 communities in Xixiangtang District, Nanning, and compared ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). MGWR provided the best model fit (adjusted R² increased by 0.136 and 0.056, respectively; lowest AICc and residual sum of squares) and revealed significant scale-dependent effects. Distance to metro stations, road network density, and the number of transport facilities exhibited pronounced local-scale heterogeneity, while population density, building density, healthy/unhealthy food outlets, facility POI density, and public transport accessibility predominantly exerted global-scale effects. High-risk clusters of CVD were identified in mixed-use, high-density urban communities lacking rapid transit access. The findings highlight the need for place-specific, multi-scale planning measures, such as transit-oriented development and balanced food environments, to reduce the CVD burden and advance precision healthy-city development. Full article

In the context of low-growth urban development, the interaction between the supply–demand structure of community public service facilities and the housing market has increasingly become a key research concern. Yet, systematic investigations into how supply–demand dynamics influence market value remain limited. To fill this gap, this study takes Wuchang District of Wuhan as the empirical case and establishes an integrated framework of “supply–demand evaluation—value effects” to assess both the equity of facility allocation and its capitalization effects. The results indicate that: (1) all categories of public service facilities in Wuchang District have Gini coefficients above 0.6, indicating substantial imbalance. Among them, elderly care, infant care, and child recreation facilities exceed 0.7, reflecting particularly severe inequality. (2) The “accessibility–housing price” quadrant model further reveals typical mismatch patterns, with “low accessibility–high price” and “high accessibility–low price” zones together accounting for 45.08%, suggesting that mismatches are widespread in the study area. (3) MGWR results show that different facility types exert differentiated effects across locations, with some even displaying opposite positive and negative effects, underscoring significant spatial heterogeneity. Overall, this study uncovers the intrinsic links between facility supply–demand structures and market value, clarifies the differentiated roles of facility types in shaping spatial value, and provides empirical evidence to support improvements in urban public service systems. Full article

Understanding soil moisture’s spatiotemporal variations and the factors influencing it is crucial for the restoration and growth of vegetation across the Loess Plateau, particularly in the Ziwuling region. This study employs soil moisture remote sensing data, complemented by information on soil properties, environmental conditions, and topography, to examine soil moisture variability within the Ziwuling region between 2001 and 2020. Using trend analysis, geographic detectors, and multi-scale geographic weighting techniques, this research aims to elucidate the effects of driving factors on soil moisture’s spatiotemporal patterns. The findings indicate the following: (1) Over the study period, the mean soil moisture in the Ziwuling region exhibited a relatively stable declining trend, with an annual decrease of −0.00047 m³/(m³·a). Spatially, higher soil moisture levels were observed in the south-central area, while lower levels occurred in the northern, western, and eastern peripheries. (2) Geoprobe analysis illustrated that the normalized difference vegetation index (NDVI) had the most notable effect on the spatial distribution of soil moisture in the region. As a direct indicator of vegetation cover, NDVI strongly affects soil moisture distribution through ecological and hydrological processes. Following NDVI, average annual potential evapotranspiration and annual precipitation were identified as the next most influential factors. The combined effect of these factors on soil moisture surpassed that of individual factors, with the interaction between NDVI and annual precipitation being particularly pronounced, predominantly controlling the spatial variability of soil moisture in the Ziwuling region. (3) Different factors exhibited varying effects on soil moisture levels. Notably, slope and elevation consistently had negative impacts, whereas variables such as soil texture (loam and sand), land use, temperature, precipitation, NDVI, and slope aspect showed bidirectional influences. This study offers a comprehensive analysis of the spatiotemporal variability of soil moisture and its controlling factors in the Ziwuling region, ultimately offering a scientific basis to support ecological restoration and sustainable development initiatives on the Loess Plateau. Full article