Search Results (83)

Integrating machine learning with spectral data provides an effective and cost-efficient scheme for estimating cadmium (Cd) contents in soils compared with labor-intensive laboratory analyses. However, conventional machine learning–based spectral estimation methods often yield unsatisfactory performance because they rely on an unrealistic assumption that the functional relationships across geographically distinct subregions are homogeneous, thereby reducing the accuracy and stability of soil Cd estimation. To address this issue, a geospatial information-guided XGBoost (GIGS) model was developed, in which a spatial weighting module was incorporated into the XGBoost framework to account for spatial heterogeneity in functional relationships among sampling locations. The spatial heterogeneity of each soil spectral sample was quantified and assigned a corresponding spatial weight, which was subsequently incorporated during model calibration. The optimal principal component spectra (PCS) derived from spectral data were used as model inputs, with measured soil Cd content as the dependent variable, thereby establishing a robust spectral estimation model for soil Cd. Results indicated that the GIGS model exhibited satisfactory performance in the spectral estimation of soil Cd content, with R², RMSE, and RPIQ values of 0.78, 0.04, and 2.02, respectively. Compared to commonly used spectral estimation models (e.g., XGBoost, random forest, support vector regression), the GIGS model achieved a maximum performance improvement of approximately 27.87% and a minimum improvement of approximately 16.42% (with reference to the R² value). Five PCS of soil spectral data were extracted as predictors for the model. PCS-1 was found to be closely associated with iron oxides, while PCS-2 primarily reflects the spectral characteristics of clay minerals. The spectral bands at 600 nm and 815 nm contribute most strongly to PCS-3, which is linked to soil organic matter and indirectly reflects the soil Cd status. PCS-4 and PCS-5 represent mixed spectral information derived from materials associated with soil Cd. An integrated framework combining the GIGS model and PCS data developed in this study provides an accurate and reliable tool for spectral estimation and mapping of soil Cd, thereby supporting cost-effective soil management and environmental sustainability worldwide. Full article

Day–night mobility differentiation provides important insights into the spatial organization of migrant activities, yet its spatial variation and underlying drivers remain insufficiently understood in Asian metropolitan areas. Using kernel density estimation (KDE), spatial autoregressive models, and multiscale geographically weighted regression (MGWR), the study examines how the built environment, socioeconomic context, economic attractiveness, and accessibility factors shape variations in migrant mobility across space among Chinese migrants in Seoul, South Korea. The results reveal pronounced spatial clustering, with higher levels of mobility differentiation concentrated in central and southeastern Seoul, whereas lower levels are observed in migrant-concentrated districts such as Guro-gu and Geumcheon-gu. Migrant stock is identified as the most influential and spatially consistent determinant, exhibiting a significant negative association across most areas. Land-use mix also negatively affects mobility differentiation, while office facilities, industrial facilities, and subway accessibility exert positive effects. Model comparison demonstrates that MGWR substantially outperforms ordinary least squares (OLS) and geographically weighted regression (GWR), achieving the highest explanatory power (R² = 0.758; adjusted R² = 0.705) and the lowest corrected Akaike information criterion (AICc) (763.656). Furthermore, MGWR uncovers considerable spatial heterogeneity in the effects of employment facilities, apartment concentration, and service-oriented facilities. These findings suggest that migrant day–night mobility differentiation is shaped by both citywide contextual factors and localized neighborhood characteristics, highlighting the importance of accounting for spatially varying relationships when examining migrant mobility patterns in metropolitan areas. Full article

With the growing focus on people-centered urban development sustainability in megacities, urban amenities have emerged as an important factor consistently associated with residential differentiation and restructuring. Understanding how it relates to the structure of social space is essential to advancing spatial equity. The study developed an analytical framework that integrates functional characteristics and supply patterns and applied Multi-scale Geographically Weighted Regression (MGWR) to examine how amenities shaped socio-spatial differentiation within Beijing’s Fifth Ring Road from 2015 to 2025. The results indicate that socio-spatial differentiation showed a rise followed by a decline across the three time points examined, yet its spatial pattern maintained a stable agglomeration characteristic of “high in the core area and low in the peripheral areas.” Significant differences exist in the roles of amenities across different attributes and scales. Market-driven factors, represented by amenity density and amenity diversity, typically exert their influence over larger spatial scales and are generally associated with spatial mixing and provide baseline opportunities for potential social interaction. Attributes such as amenity publicness and amenity uniqueness, which are more influenced by institutional and capital factors, primarily operate at local scales. While they are often associated with exclusionary effects in traditional core areas, they are also consistent with a certain degree of spatial integration in some revitalized districts. This study offers a more nuanced explanation for understanding the socio-spatial restructuring of megacities in transition and provides empirical evidence for advancing more equitable and sustainable urban governance. Full article

Cities in climatic transition zones face coupled radiative and evaporative stresses, and their carbon emission mechanisms differ significantly from those in humid regions. Taking Xi’an, a typical megacity in the transition zone, as a case study, this research utilises a 500 m × 500 m grid to integrate multi-source data for carbon emission accounting. By applying spatial autocorrelation and the Multi-scale Geographically Weighted Regression (MGWR) model, this study examines the spatial heterogeneity of carbon emissions and the mechanisms through which urban planning influences them. The results indicate that carbon emissions in Xi’an exhibit a “core–periphery” agglomeration pattern, with commercial land use exhibiting the highest emission intensity. Carbon emissions and land surface temperature are spatially coupled, consistent with a hypothesised positive feedback loop of the “dry heat island” effect. Morphological factors exhibit spatial non-stationarity: floor area ratio is positively associated with emissions in the old city centre, whereas mutual shading among super-high-rise buildings in the High-Tech Zone coincides with a weaker effect. Building density shows a positive association only where ventilation is limited. Land use mix and blue–green spaces show non-linear negative associations with emissions, with higher marginal benefits in arid–hot environments. This study proposes carbon reduction strategies for the renewal of old urban areas, business cores, and new ecological districts, providing empirical evidence and decision-making references for low-carbon spatial planning in cities within the climatic transition zone. Full article

Urban–rural integration and common prosperity are two major strategic goals in China that constitute the dual driving force for the transformation of urban–rural relations in the process of Chinese modernization. The digital economy provides new momentum for the simultaneous advancement and mutual collaboration of those working to achieve them. This study draws on panel data covering 30 Chinese provinces over the period 2013–2024 and employs a mixed-method approach that combines the entropy weight method, coupling the coordination degree model, geographically and temporally weighted regression (GTWR), and dynamic qualitative comparative analysis (QCA). It systematically investigates how the coupling-coordination degree between urban–rural integration and common prosperity has evolved over time and space, as well as the multiple pathways through which the digital economy drives this coupling coordination. The study found the following: (1) The average coupling-coordination degree rose from 0.467 to 0.580, with a clear spatial divide, specifically, high in the eastern seaboard and centrally administered cities, versus depressed levels in the west and southwest. (2) The significant positive driving effect of the digital economy is significant, with a more prominent marginal effect in underdeveloped areas. (3) The study reveals three interchangeable paths: technology–organization synergy, organization–environment linkage, and all-encompassing driving, with digital resources serving as the core common condition across all pathways. The study offers both theoretical and empirical support for designing digital economy policies tailored to local conditions, thereby advancing urban–rural integration and common prosperity, and thereby advancing sustainable development in China and offering valuable lessons for other developing countries. Full article

Understanding how the street built environment (SBE) relates to street vitality is critical for promoting livable and sustainable cities, yet its multiscale and spatially heterogeneous patterns remain insufficiently understood, particularly in polycentric urban contexts. Focusing on the core urban area of Chongqing, this study adopts 7951 street segments as the analytical unit to capture street-level spatial processes. A street vitality index was constructed using multi-source data integrating population, social, and economic activities. The SBE was quantified across three dimensions: macroscale street-network structure derived from spatial design network analysis, mesoscale functional characteristics measured using point-of-interest data, and microscale streetscape perception extracted from street-view imagery. The multiscale geographically weighted regression (MGWR) model was employed to examine spatially varying associations between the SBE and street vitality. Results reveal clear spatial non-stationarity in these associations. Closeness, functional density, and functional mix show positive associations with street vitality, whereas connectivity, betweenness, and greenness exhibit mainly negative associations. Transit stop density and enclosure demonstrate bidirectional spatial associations. These findings provide empirical evidence of spatially differentiated associations between the SBE and street vitality in polycentric cities and offer a data-driven basis for differentiated street planning and urban spatial optimization. Full article

As a key link between short-distance urban mobility and public transport, dockless bike-sharing (DBS) systems have expanded rapidly in recent years. However, existing studies are limited by insufficient factor coverage, incomplete temporal analysis, and inadequate assessment of spatial-scale effects. To address these gaps, this study uses Shenzhen as a case study, integrating 40 million DBS trip records from August 2021 with multi-source geospatial data to develop a spatiotemporal analytical framework. First, it examines differences in riding patterns between weekdays and weekends, further segmenting trips into six time periods to capture intra-day temporal variations. Through multicollinearity and spatial autocorrelation tests, a 700-m grid was identified as the optimal analysis unit. Subsequently, a Multi-scale Geographically Weighted Regression (MGWR) model quantified how multiple sources of factors collectively shape DBS usage behavior. Results indicate that higher frequency, faster speeds, and longer distances during peak periods characterize weekday trips. Office POIs and transit accessibility positively affect DBS usage during weekday peaks, whereas Residential POIs and Convenience Service POIs have a greater influence on weekend trips. Population density and land-use mix consistently promote DBS use across all periods. Younger residents (<30 years) were the main users, especially during weekday peak and weekend no-peak periods, whereas gender and education had limited impact. These findings provide empirical evidence to optimize bike-sharing deployment, enhance multimodal transport integration, and support sustainable urban mobility planning. Full article

Background/Objectives: Cardiovascular diseases (CVD) in Chile are profoundly shaped by place-based determinants of diet. This study examines the association between food deserts—areas with structurally limited access to nutritious, affordable food—and population-level cardiovascular risk across Chile’s three largest metropolitan areas (Santiago, Valparaíso, Concepción). Methods: We constructed a geospatial food desert index combining OpenStreetMap-derived retail accessibility with census information, and linked it to georeferenced cardiovascular health records. To overcome the limitations of global models that assume spatial stationarity, we applied Multiscale Geographically Weighted Regression (MGWR) to allow coefficients to vary across space and to recover variable-specific process scales. Results: The MGWR results indicate pronounced spatial non-stationarity in the food desert–CVD association. The relationship is predominantly positive across Gran Valparaíso, predominantly negative in Gran Concepción, and highly mixed within Gran Santiago, evidencing divergent local mechanisms rather than a single national pattern. Conclusions: The observed heterogeneity undermines “one-size-fits-all” national interventions and supports place-sensitive, equity-oriented strategies. Policy implications include territorially tailored food-retail regulation and primary-care outreach, co-designed with local actors, with MGWR providing a critical analytic basis for actionable, context-specific public health planning. Full article

Reliable cropland mapping in Karst landscapes is hindered by high topographic heterogeneity and landscape fragmentation. Focusing on the Yunnan–Guizhou Plateau in Southwest China, this study evaluates the accuracy and spatial consistency of seven global land cover products (i.e., GlobeLand30, CLCD, GLC_FCS30, CACD, ESA WorldCover, Esri Land Cover, and FROM-GLC10) against the Third National Land Survey released by China’s Ministry of Natural Resources. Furthermore, we employed Multiscale Geographically Weighted Regression (MGWR) to diagnose key impact factors. The results reveal that the 10 m ESA WorldCover offers superior reliability (OA = 0.81, $R^2$ = 0.84), whereas GLC_FCS30 exhibits the weakest performance among the evaluated datasets (OA = 0.72, $R^2$ = 0.29), highlighting significant uncertainty in this complex terrain. Crucially, MGWR diagnostics (adjusted $R^2=0.923$) uncover how mapping uncertainty is driven by spatially non-stationary environmental constraints. Landscape fragmentation was identified as the primary global driver, exhibiting a consistent negative correlation with accuracy and indicating that the mixed pixel dilemma is the pervasive error source. In contrast, topographic slope operated as a dominant local constraint, with its inhibitory effect intensifying specifically in high-relief gorges where terrain shadowing compromises optical signals. Based on these mechanism diagnostics, we propose a region-adaptive decision framework integrating multi-source fusion and temporal logic to specifically target these topography- and fragmentation-induced uncertainties in future mapping. Full article

Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1) Urban vitality exhibits a multi-center, clustered structure, gradually expanding from gentle to steeper slopes over time, with activity patterns shifting from an afternoon peak to an all-day distribution. (2) Significant differences in regional vitality resilience were observed: the core vitality areas exhibited stable ST-AOI spatial patterns, flexible temporal rhythms, and strong adaptability; the emerging vitality areas recovered quickly with low losses, while low-vitality areas showed slow recovery and insufficient resilience. (3) The density of commercial service facilities and the level of housing prices were continuously enhancing factors for vitality improvement, whereas the density of subway stations and the degree of functional mix played key roles in supporting resilience during the COVID-19 pandemic. (4) The synergistic effect between transportation systems and commercial facilities is crucial for forming high-vitality zones in mountainous cities. In contrast, reliance on a single factor tends to lead to vitality spillover. This study provides a crucial foundation for promoting sustainable urban development in Guiyang and other mountainous regions. Full article

The purpose of this study is to explore the spatial impact of short-term rental activity on house prices in the city of Athens, Greece. It is well established that the increasing number of short-term rentals has a number of consequences on the functions and living standards in several cities around the world. An aspect that is not studied very often is the effect of short-term rentals on house prices and, especially, the spatial distribution of this effect. In this paper, spatial regression models are presented, incorporating several of the commonly employed house characteristics, such as structural and locational characteristics, with the addition of the short-term rentals as an explanatory factor. Geographically Weighted Regression (GWR) models, in particular, produce the geographic distribution of regression coefficients, allowing for the study of the short-term rentals’ influence on house prices at the local level. Furthermore, spatial regression models are compared to global linear models. Although global models indicate that short-term rentals have an overall positive contribution on house prices, Geographically Weighted Regression, through the local regression coefficients, reveals spatial patterns with mixed effects, both positive and negative. The greater positive impact is observed in the area around the historical center of the city where short-term rentals’ presence is intense. Full article

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects. Full article

Guided by the “Healthy China” initiative, understanding the impact of the built environment on running behavior is essential for encouraging regular physical activity and advancing public health. This study addresses a critical gap in healthy city research by examining the spatial heterogeneity in how urban environmental contexts affect residents’ running preferences. Focusing on two contrasting areas of Suzhou, namely the historic Gusu District and the modern Industrial Park District, we developed a 5Ds-based analytical framework (density, accessibility, diversity, design, and visual) that incorporates Suzhou’s unique water networks and street features. Methodologically, we used Strava heatmap data and multi-source environmental indicators to quantify built-environment attributes and examined their relationships with running-space selection. We applied linear regression and interpretable machine learning to reveal overall associations, while geographically weighted regression (GWR) was used to capture spatial variations. Results reveal significant spatial heterogeneity in how the built environment influences running-space selection. While the two districts differ in their urban form, runners in Gusu District prefer dense and compact street networks, whereas those in Industrial Park District favor open, natural spaces with higher levels of human vibrancy. Despite these differences, both districts show consistent preferences for spaces with a more intense land use mix, stronger transportation accessibility, and larger parks and green spaces. The multi-dimensional planning strategies derived from this study can improve the urban running environment and promote the health and well-being of residents. Full article

The construction industry is a major contributor to global resource consumption and waste. This sector extracts over two billion tons of raw materials each year and contributes over 30% of all solid waste generated annually through construction and demolition debris. The movement toward circularity in the built environment aims to replace linear processes of extraction and disposal by promoting policies favoring building preservation and adaptive reuse, as well as the salvage and reuse of building materials. Few North American cities have implemented explicit policies that incentivize circularity to decouple urban growth from resource consumption, and there remain substantial hurdles to adoption. Nonetheless, existing regulatory and planning tools, such as zoning codes and historic preservation policies, may already influence redevelopment in ways that could align with circularity. This article examines spatial patterns in these indirect pathways through a case study of a college town in New York State, assessing how commonly used local planning tools shape urban redevelopment trajectories. Using a three-stage spatial analysis protocol, including exploratory analysis, Geographically Weighted Regressions (GWRs), and Geographic Random Forest (GRF) modeling, the study evaluates the impact of zoning regulations and historic preservation designations on patterns of demolition, reinvestment, and incremental change in the building stock. National historic districts were strongly associated with more building adaptation permits indicating reinvestment in existing buildings. Mixed-use zoning was positively correlated with new construction, while special overlay districts and low-density zoning were mostly negatively correlated with concentrations of building adaptation permits. A key contribution of this paper is a replicable protocol for urban building stock analysis and insights into how land use policies can support or hinder incremental urban change in moves toward the circular city. Further, we provide recommendations for data management strategies in small cities that could help strengthen analysis-driven policies. 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