Search Results (253)

Traditional Chinese villages are important carriers of rural heritage, collective memory, vernacular landscapes, and living cultural traditions. However, rapid urbanization, agricultural modernization, climate change, and tourism development have increasingly threatened their spatial integrity and cultural continuity, highlighting the need for evidence-based conservation and adaptive management. This study examines the spatial distribution patterns and associated factors of 8155 national-level traditional villages in China. An integrated spatial analytical framework was developed by combining kernel density estimation, spatial autocorrelation analysis, Geodetector, and multiscale geographically weighted regression (MGWR). The results show that: (1) traditional villages are unevenly distributed across China and form a distinct “three-core and multi-node” spatial pattern, with major high-density clusters concentrated in several cross-provincial regions and secondary clusters distributed in other heritage-rich areas; (2) the spatial differentiation of traditional village density is statistically associated with natural, cultural, and socioeconomic factors, among which temperature and precipitation show the strongest explanatory power, while cultural endowment, ecological quality, and socioeconomic variables show more context-dependent associations; and (3) compared with OLS and conventional GWR, MGWR improves model performance by capturing spatially heterogeneous and scale-dependent relationships through variable-specific bandwidths. These findings provide national-scale empirical evidence for differentiated conservation planning and support the integration of traditional village protection with rural revitalization, cultural heritage conservation, and sustainable regional development. Full article

Northeast China is one of China’s major commodity grain bases and plays a strategic role in national food security. Against the background of rural population outflow and agricultural modernization, new agricultural business entities (NABEs), including family farms, farmers’ cooperatives, and agribusinesses, have become important actors in reshaping agricultural production organization. Based on registration data for 2014, 2018, and 2023, this study uses kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, spatial autocorrelation analysis, ordinary least squares (OLS) regression, and multiscale geographically weighted regression (MGWR) to examine the spatiotemporal patterns and driving factors of NABEs in Northeast China. The results show that: (1) NABEs expanded rapidly from 2014 to 2023 and became increasingly concentrated in agriculturally advantageous plain areas. (2) Family farms showed the fastest expansion, farmers’ cooperatives had the widest spatial coverage, and agribusinesses were mainly concentrated around transport corridors and market nodes. (3) In terms of industrial structure, crop-production entities remained dominant, followed by animal husbandry entities, while forestry, fishery, and agricultural support service entities accounted for relatively small shares; however, their numbers continued to increase. (4) The OLS results showed that the reclamation rate and road network density had relatively stable associations with the spatial distribution of multiple entity types, whereas economic development, science and technology investment, and fiscal support showed differentiated relationships across entity types and regions. (5) The MGWR results further reveal spatial heterogeneity in the effects of driving factors. These findings provide empirical evidence for type-specific cultivation and differentiated policy support for NABEs in major grain-producing 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

Rapid urbanization reshapes urban land systems and intensifies surface thermal heterogeneity, yet nonlinear day–night land surface temperature (LST) responses to grey–green spatial organization and building morphology remain insufficiently understood, particularly in thermally stressed areas across the urban–rural gradient. Using Xi’an, China, as a case study, this study develops a priority-area-based land–climate interaction framework. Priority areas were defined as grid cells where elevated LST coincided with relatively strong local explanatory relationships between LST and land-cover or morphological variables. Multiscale geographically weighted regression (MGWR), gradient boosting decision trees (GBDTs), SHAP-based interpretation, and threshold sensitivity analysis were combined to identify dominant drivers, nonlinear response patterns, and interaction structures of daytime and nighttime LST. The results show pronounced day–night differentiation: daytime hotspots were concentrated in the built-up core, whereas nighttime hotspots extended toward the urban–rural fringe. Daytime LST was mainly associated with building coverage and grey-space organization, while nighttime LST was more strongly related to mean building height and the cooling contribution of green-space coverage. The analysis further identified localized empirical response ranges for built-up intensity, grey-space connectivity, building height, and green-space coverage within the priority areas. These findings clarify how land-cover configuration and building morphology jointly shape day–night surface thermal responses and provide context-specific evidence for land-use planning and targeted urban heat mitigation. Full article

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions. Full article

Land-use and land-cover changes around reservoirs mediate the interface between watershed land systems and managed surface-water resources. In regulated reservoirs, water-level regulation can rapidly expose or inundate shore-zone land, yet evidence remains limited on where these transitions occur, how landscape configuration changes, and how such information can inform watershed and reservoir-margin management. Using 0.5 m Jilin-1 optical imagery from April and September of 2024 and 2025, this study mapped land-use/land-cover change (LUCC) in the Danjiangkou Reservoir shore zone and integrated transition matrices, class-level landscape metrics, shoreline-distance gradients, reach-level zoning, paired hydrological records, and multiscale geographically weighted regression (MGWR). The classification achieved an overall accuracy of 93.1% and a Kappa coefficient of 0.921. The strongest land-cover shift occurred between September 2024 and April 2025, when the water proportion declined from 78.74% to 60.10% and bare land expanded during the lowest observed reservoir stage (151.02 m). Subsequent refill was accompanied by partial re-inundation and increases in grassland, cropland, and forest. The 0–30 m shoreline belt was the principal response zone, indicating that hydrologically driven land-cover replacement was concentrated in the immediate reservoir margin. MGWR showed spatially varying positive associations between change-patch characteristics, distance to permanent water, and elevation, but the low explanatory power requires these results to be interpreted as spatial diagnostics rather than causal attribution. The study links land-cover monitoring with reservoir water-level regulation, identifies priority shoreline belts, and provides spatial information for field verification and reservoir-margin management. Full article

Climate change and rapid urbanization increasingly threaten water security in large river basins, yet existing assessments often fail to capture the multi-scale interactions between hydroclimatic extremes and human activities. To address this gap, we developed an integrated framework combining risk assessment, multi-method driver diagnosis (Geodetector, Multi-Scale Geographically Weighted Regression (MGWR), and Structural Equation Modeling (SEM)), and Zoned Management. Using a landscape-derived Ecological Risk Index (ERI) as a proxy indicator of runoff and non-point source potential, based on established empirical linkages between landscape metrics and hydrological processes, we applied the framework to three major urban agglomerations in the Yangtze River Basin from 2000 to 2020. Our results reveal three distinct risk mechanisms: in the Chengdu–Chongqing area (CYUA), a 165.8% increase in impervious surfaces drives altered runoff; in the Middle Reaches (MRC), the q-value of the Standardized Precipitation Index (SPI) rose from 0.017 in 2000 to 0.146 in 2020, corresponding to a 759% relative increase. Although the absolute q-value of SPI remains moderate at around 0.15, its rapid rise suggests increasing hydrological sensitivity of the MRC’s river–lake system to precipitation extremes; in the Yangtze River Delta (YRD), socioeconomic activities exert overriding pressure. Based on these diagnostics, we propose tailored strategies for water environment management, adaptive planning, and disaster mitigation. This framework offers a scientific basis for differentiated water governance in large river basins facing coupled anthropogenic and hydroclimatic pressures. 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

As a key driver of sustainable urban development, the digital economy transforms urban spatial structures through novel organizational forms such as digital enterprises. Understanding the spatiotemporal distribution of these enterprises is crucial for fostering equitable and efficient urban growth. Focusing on Hangzhou, a leading digital city in China, this study applies kernel density estimation, the standard deviational ellipse, and the nearest neighbor index to analyze the evolution patterns of newly established digital enterprises (NDEs) from 2010 to 2020. It further integrates geodetector and multiscale geographically weighted regression (MGWR) to uncover the drivers behind their spatial differentiation. The results indicate that: (1) The spatial pattern of NDEs evolved from “single-core diffusion” to a “dual-core with multi-center and axial contiguous” structure, yet the density gap between cores and peripheral counties persisted. (2) NDEs exhibited increasing spatial agglomeration over time. (3) Global drivers: the nighttime light index exerts the strongest positive effect, while land costs and population density show negative effects, reflecting cost-squeeze and decentralized locational preferences. (4) Locally, bus accessibility, innovation level and science-education-culture level, display strong spatial heterogeneity; innovation level has very high positive coefficients in innovation poles but negative effects in ecologically sensitive or deindustrialized areas, revealing an “innovation multiplier effect” alongside resource misallocation risks. These findings provide empirical evidence of how digital economy actors spatially manifest, offering insights for urban planners and policymakers to leverage digital growth for guiding sustainable spatial restructuring, enhancing resource allocation efficiency, and promoting balanced regional development. Full article

This study investigates the association between urban greenness and sales in commercial alleys. We focus on 1090 commercial alleys in Seoul, South Korea, defined as neighborhood-scale open commercial streets or districts composed of small retail, service, cafe, and restaurant businesses, and combine spatially explicit measures of greenness with data on weekend sales to assess how variation in vegetation is associated with local economic performance. Greenness is measured by the normalized difference vegetation index (NDVI) derived from remote sensing imagery. We employ a set of global and spatially explicit models, including Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), Multiscale Geographically Weighted Regression (MGWR), and a Python Geographical Random Forest (PyGRF, v0.0.12), to capture both overall and location-specific relationships. The results show that higher levels of greenness are significantly associated with higher weekend sales, with spatial heterogeneity observed across different areas of the city. The green investment efficiency index (GIEI) results further identify clusters of high investment efficiency in areas characterized by strong greenness–sales associations and relatively limited existing greenness. High GIEI values were concentrated in areas near natural amenities and dense residential neighborhoods, indicating potential priority locations for targeted greening interventions. By linking objective measures of greenness to observed sales at the scale of everyday commercial environments, this study contributes to a better understanding of how urban greenness is associated with consumer behavior and local economic activity. The findings provide practical implications for identifying areas where greening strategies may be considered as part of broader efforts to support more resilient and sustainable neighborhood commercial areas, while recognizing that the observed relationships are associative rather than causal. Full article

Agricultural watersheds must simultaneously support multiple Ecosystem Services (ESs), yet the coordination between Ecosystem Service Value (ESV) growth and synergies of ESs remains poorly understood. Taking the Liaohe River mainstream Basin (LRMB), a typical agricultural watershed, as a case, this study investigates the spatiotemporal dynamics of ESV and trade-offs among ESs, along with their driving factors. Five key ESs—Food Production (FP), Water Conservation (WC), Water Purification (WP), Soil Conservation (SC), and Landscape Aesthetics (LA)—were selected. The InVEST model, Function-based Valuation Method, Root Mean Square Deviation (RMSD), and Coupling Coordination Degree (CCD) were comprehensively applied to assess the spatiotemporal variations in ESV, trade-off intensity, and their coupling coordination degree in the watershed from 2000 to 2023. Furthermore, the Optimal Parameters-based Geographical Detector (OPGD) and Multiscale Geographically Weighted Regression with Spatial Auto-correlation (MGWR-SAR) were employed to explore the driving mechanisms underlying changes in ESV and trade-off intensity, and to identify the major driving factors and their spatial heterogeneity. The results reveal the following: (1) From 2000 to 2023, total ESV in the LRMB increased by 69.5% from 77.66 to 131.59 billion yuan, with WC and FP accounting for 42.8% and 41.9% of this growth. Spatially, ESV shifted from a west-to-east increasing gradient to a U-shaped pattern, with high values concentrated in mountainous areas and low values along the mainstream. (2) Mean trade-off intensity remained stable at approximately 0.29, yet exhibited pronounced spatial polarisation. High trade-off zones shifted from the southwestern estuary toward the mainstream corridor, driven primarily by intensifying conflicts between FP and other ESs. (3) Despite a stable watershed-average CCD of 0.71–0.73, the CCD along the Liaohe River mainstream declined by over 15%, forming a corridor of coordination decay and revealing that ESV growth occurs at the expense of internal synergy. (4) Nonlinear interactions dominated ES dynamics, with the interaction of precipitation and human disturbance intensity exhibiting the highest explanatory power (q-values of 0.61 for ESV and 0.58 for RMSD). (5) Natural climatic factors (precipitation, temperature) predominantly enhanced synergy in mountainous areas, whereas human and landscape factors (human disturbance intensity, Shannon’s Diversity Index, PLAND of water) intensified trade-offs along the mainstream and central plains. This study establishes an integrated “ESV–trade-off–CCD” diagnostic framework and proposes a differentiated management strategy, offering a potentially transferable paradigm for sustainable governance in agricultural watersheds. Full article

Mitigating heavy metal (HM) contamination in soil is vital for ecological and food security. Accurately mapping these pollutants and understanding their drivers are essential prerequisites for informed regional environmental governance. However, conventional spatial interpolation techniques used to estimate HM concentrations are susceptible to systematic biases and inadequate spatial resolution. To address these limitations, this study developed a novel hybrid model, termed HASM–ANN, coupling high-accuracy surface modeling (HASM) with artificial neural networks (ANNs). This approach generated high-resolution spatial distributions of HMs (As, Cd, Cu, Hg, Cr, and Pb) in agricultural soils of the Eastern Longquan Mountain region, Chengdu, China. Furthermore, the geographical detector (GD) and the Multiscale geographically weighted regression (MGWR) models were employed to explore driving mechanisms. Results indicate that HASM–ANN significantly outperformed conventional interpolations (ordinary/universal kriging, IDW) and HASM–coupled other machine learning downscaling methods. The proposed model demonstrated high predictive accuracy, yielding R² values between 0.75 and 0.86, and consistently achieved a significantly lower RMSE across all targeted soil heavy metals compared to the HASM. Analysis of the explanatory power (q) revealed that soil As was primarily influenced by clay content (CC, q = 0.45) and available phosphorus (AP, q = 0.42), whereas Cd was mainly driven by AP (q = 0.51) and PM_2.5 (q = 0.43). The spatial distribution of Hg was largely governed by soil organic matter (SOM, q = 0.53). Additionally, Cu concentrations were determined by SOM (q = 0.38), CC (q = 0.34), and pH (q = 0.31). Notably, Cr was significantly influenced by CC (q = 0.42), pH (q = 0.38), and elevation (q = 0.31), while Pb was further driven by SOM (q = 0.46) and PM_2.5 (q = 0.39). By offering high-precision mapping and elucidating the underlying driving mechanisms, this research directly facilitates informed environmental governance to protect ecological integrity and public health. Full article

Over the past three decades, geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) have become essential tools for spatial analysis in urban, environmental, and land-use research. This scoping review systematically maps and synthesizes the global literature on GWR and MGWR published between 1996 and 2026, aiming to identify the research hotspots, evolutionary paths, and cutting-edge trends. Bibliometrics and CiteSpace visualization tools are used to conduct a multi-dimensional visual analysis of thousands of selected articles, including countries, institutions, core authors, highly cited keywords, and key documents. The results show that the current research focuses on spatial heterogeneity, multiscale analysis, GWR model optimization, non-stationarity characterization, and simulation of urban land-use change. Potential future directions include the construction of spatiotemporal integrated models, the integration of high-performance computing, and the expansion of interdisciplinary applications. The results of this study can help scholars fully understand the current research status and future directions, and provide a scientific spatial analysis framework for practitioners in urban planning, land resource management, and environmental assessment. Furthermore, the conclusions can provide theoretical support and a decision-making basis for the government to formulate intelligent and refined urban development policies. Full article

Rapid urbanization and stringent ecological protection policies in China have reshaped spatial competition among urban, agricultural, and ecological spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate slope structure evolution and spatial competition patterns from 1990 to 2023. The results reveal a distinct topographic stratification: urban space dominates low-slope regions (<6°) but exhibits a pervasive “upslope expansion” trend, with its average slope increasing from $1.81°$ to $2.07°$, equivalent to an annualized increase of approximately $0.008°{\mathrm{yr}}^{-1}$; agricultural space characterizes the transition zones (6–$15°$), showing an “upslope migration” in the Southeastern Hills associated with urban expansion pressure in low-slope areas; and ecological space functions as a stable barrier in steep terrains (>15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligned with China’s macro-topography, including “low-slope agglomeration” in the Eastern Plains, “interwoven upslope” patterns in the Southern Hilly Regions, and ecological dominance in the Western Highlands. Association analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is most strongly associated with human activity factors, especially human footprint and nighttime lights ($q>0.29$), which show the highest explanatory power among the examined factor groups. The interaction between human activity and elevation further shows relatively high explanatory power ($q=0.41$), suggesting that spatial competition is more pronounced where intensive human activities overlap with topographic constraints. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to “three-dimensional topographic regulation,” advocating differentiated management strategies—such as strict “slope redlines” for urban-agricultural transition zones—to mitigate intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability. Full article