Search Results (1,768)

Tuberculosis (TB) remains a major public health burden in China. Although meteorological and environmental factors are recognized to influence TB transmission, their non-linear effects and spatiotemporal heterogeneity have not been fully elucidated. Based on monthly TB incidence data from 31 provinces in China during 2005–2020, this study systematically investigated these effects by integrating nine meteorological and air pollution variables within a combined machine learning and spatial statistical modeling framework. The results indicated that the Extreme Gradient Boosting (XGBoost) model effectively captured the complex non-linear relationships between environmental exposure and TB incidence. SHAP interpretability analysis identified surface pressure (SP), vegetation coverage, and PM_2.5 as the key drivers and revealed pronounced nonlinear response patterns and threshold effects. In particular, the promoting effect of PM_2.5 on TB incidence increased sharply at medium-to-high concentration levels. To further investigate spatial and temporal non-stationarity, Geographically and Temporally Weighted Regression (GTWR) was applied. The results demonstrated strong spatiotemporal heterogeneity in driver effects across provinces. The influence of PM_2.5 showed a consistently positive association with TB incidence and exhibited a distinct temporal evolution characterized by an initial strengthening before 2015 followed by a weakening thereafter, closely aligning with China’s air pollution control process. These findings provide new insights into the nonlinear and spatiotemporally heterogeneous effects of meteorological and environmental factors on TB incidence and support the development of more targeted, region-specific TB prevention strategies. Full article

The past few years has witnessed the rapid development of China’s logistics industry. However, the industry still faces problems such as uneven regional development, low-cost efficiency, insufficient technology application, and pressure for green transformation. To support more effective policy and strategic planning, this study used composite location entropy, spatial autocorrelation analysis, and kernel density estimation to analyze the spatiotemporal evolution of logistics industry agglomeration based on China’s city-level panel data from 2010 to 2023. Geographic detectors and geographically weighted regression were used to explore its driving mechanisms from multiple perspectives. The results indicated that (1) China’s logistics industry agglomeration exhibited a decreasing gradient from east to west and the regional disparities gradually narrowed down over time. (2) China’s logistics industry showed significantly positive spatial autocorrelation, characterized mainly by high-high and low-low clusters. Northeastern China experienced the most active and tortuous local spatial evolution of logistics agglomeration, while Eastern China exhibited high tortuosity but stable spatial structure. Western China showed a smooth evolution, and Central China followed a relatively independent evolutionary path. Spatially, China’s logistics industry presented a pattern of high concentration in the southeast and sparse distribution in the northwest, with high-value zones expanding toward the central and western regions. (3) Transportation accessibility was the primary factor influencing logistics industry agglomeration, and the interaction among factors was stronger than the effect of individual factors. Specifically, the degree of openness exhibited a driving pattern centered on coastal areas and decreasing towards inland regions; the level of commercial development showed a positive correlation in the west and a negative correlation in the east; the spatial pattern of transportation capacity shifted from a pronounced east–west polarization to a more fragmented multi-cluster distribution; and transportation accessibility demonstrated spatial heterogeneity, with positive correlation in the southeast coastal areas and negative correlation in the west. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

Focusing on contrasting climate and land use pathways, this analysis explores the changing trajectories of water conservation function over time. An integrated framework combining the PLUS and InVEST models with Spearman’s correlation analysis and geographically weighted regression (GWR) was applied to examine the spatiotemporal heterogeneity and underlying drivers of water conservation function in the Chengdu–Chongqing Economic Zone during the period 2000–2020. Thus, it further predicted the evolution trend under two scenarios, namely SSP1-1.9 (Sustainable Development Pathway) and SSP2-4.5 (Medium Development Pathway), for the period 2030–2050. The findings reveal that: (1) Between 2000 and 2020, the spatial distribution of water conservation function shifted markedly, with low-value areas contracting and high-value zones expanding, alongside a progressive transition toward a predominantly medium-to-high functional structure. (2) In mountainous and hilly transition zones, precipitation (PRE) and forest cover proportion (FCP) exhibited notably positive effects, whereas evapotranspiration (PET) exerted a negative effect. In contrast, in plain and urbanized areas, built-up land proportion (BUP), population density (POP), and gross domestic product density (GDP) demonstrated pronounced negative effects. (3) Future simulations indicate that under the sustainable development pathway (SSP1-1.9), the combined area of high and extreme functional zones will recover by 2050, whereas under the moderate development pathway (SSP2-4.5), such extreme functional zones will be nearly eliminated. These results underscore the substantial impact of development pathways on regional water security and sustainability. Full article

Extreme climate events have emerged as a critical threat to the economic resilience and environmental sustainability of urban systems. As a central pillar of the low-carbon transition, improvements in carbon emission efficiency (CEE) are increasingly recognized as a potential pathway to mitigate the occurrence and intensity of such events. Drawing on a balanced panel dataset of 163 cities from 2006 to 2022, this study integrates an Extreme Gradient Boosting (XGBoost) model augmented with SHAP (Shapley Additive Explanations) analysis and a Geographically and Temporally Weighted Regression (GTWR) framework to examine the nonlinear and spatially heterogeneous effects of CEE on the Climate Physical Risk Index (CPRI). The results reveal a distinct two-stage dynamic pattern, in which CEE initially exacerbates and subsequently mitigates climate risk, indicating a nonlinear transition from short-term intensification to long-term alleviation. This relationship shows clear differences across city levels and climate types. The strongest effects appear in peripheral cities and in areas with extreme rainfall dominance (ERD). Spatial analysis based on GTWR also shows a clear north–south pattern. The effect of CEE in reducing risk becomes stronger from the south to the north. Based on these results, the study suggests different land-use policy strategies for different city types and climate conditions. The results give actionable insights for designing targeted carbon governance policies. These policies aim to deal with the growing challenges caused by extreme climate events under ongoing climate change. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

High-skilled labor plays an important role in regional economic development, yet accurately identifying its influencing patterns remains challenging due to complex factor interactions, spatial spillover effects, and fuzzy boundaries among urban characteristics. Traditional regression-based approaches primarily focus on isolated linear effects, making it difficult to capture multi-factor combinatorial relationships underlying talent agglomeration. To address these limitations, this study proposes a spatially aware fuzzy association rule mining framework by integrating soft-gated spatial weighting and concept stability theory. Using data from the Sixth and Seventh National Population Censuses and the China City Statistical Yearbook, the framework is applied to the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Middle Reaches of the Yangtze River (MRYR) regions from 2010 to 2020. The results show that the associative patterns of high-skilled labor evolved substantially across regions. In the BTH region, dominant factors shifted from administrative hierarchy and environmental amenities to stronger interactions between economic growth and talent inflow. In the YRD region, economic dynamism gradually replaced static geographic advantages, while in the MRYR region, market-oriented drivers increasingly surpassed administrative-led resource concentration. Overall, the findings suggest a transition from single-factor dependence to multi-factor coupled patterns in China’s regional talent agglomeration. 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

Landslides cause thousands of fatalities and billions in economic losses annually, yet reliable susceptibility mapping across heterogeneous landscapes remains challenging because conventional models assume stationary relationships between landslide occurrence and environmental controls. Ensemble methods, though promising, rely on either globally fixed aggregation weights or kernel-constrained local averaging, failing to adapt when the reliability of base models varies nonlinearly across space. To overcome this, we propose a two-stage hierarchical spatial adaptive ensemble (HSE) framework. In stage one, three complementary base learners are deployed: geographically weighted regression (GWR) for local spatial non-stationarity; a geographically optimal similarity (GOS) model, grounded in the Third Law of Geography, to represent similarity-based local dependence; and a deep neural network (DNN) for nonlinear covariate interactions. In stage two, a multi-branch attention-based network learns spatially varying fusion weights via multi-scale feature extraction, abandoning fixed weights or kernel constraints. We validate HSE on a typical landslide-prone catchment, comparing against single models (GWR, DNN, GOS). Results demonstrate that our method consistently achieves superior predictive accuracy, spatial consistency, and out-of-sample robustness. Moreover, the attention-derived spatially varying weights provide interpretable insights into where each base learner dominates, bridging predictive performance with geophysical interpretability. These findings confirm that explicitly learning spatial heterogeneity during ensemble fusion is essential for reliable landslide susceptibility mapping, with strong potential for transfer to other geospatial prediction tasks. Full article

The urban heat island (UHI) effect has become a global environmental challenge, and quantifying the spatial heterogeneity of its driving mechanisms while developing differentiated regulation strategies remains a critical research gap. This study takes Berlin, Germany as a case study, integrating spatial autocorrelation analysis with a coupled geographically weighted regression (GWR) model to systematically investigate the spatial heterogeneity of the driving mechanisms of Local Climate Zones (LCZs) on surface urban heat island intensity (SUHII), and proposes refined regulation strategies. First, the WUDAPT method was employed to generate a LCZ map, and global and local Moran’s I were used to identify SUHII spatial clustering characteristics, dividing the study area into High–High (HH), Low–Low (LL), and Not Significant (NS) clustering zones. Second, Ordinary Least Squares (OLS) and GWR coupled models were constructed to analyze the global and local relationships between LCZ composition and SUHII. The results indicate: (1) Berlin’s SUHII exhibits significant spatial clustering characteristics (Moran’s I = 0.984), with clear differentiation between the HH zone (25.8%, mean 2.67 °C) and the LL zone (26.4%, mean −0.16 °C); (2) the GWR model (R² = 0.921, AICc = 1279.538) significantly outperforms the OLS model (R² = 0.822, AICc = 2871.608), confirming strong spatial heterogeneity in the LCZ-SUHII relationship, with more pronounced advantages of GWR in urban–rural fringe areas; (3) LCZ 5 (low-density mid-rise buildings) and LCZ 2 (high-density mid-rise buildings) are key warming factors across the entire study area, but their warming effects are stronger in suburban areas than in central urban areas; LCZ A (dense trees) and LCZ G (water bodies) are key cooling factors across the entire area, but their cooling effects are stronger in central urban areas than in the suburbs. Based on these findings, this study establishes a differentiated strategy framework of “Zoning—Identifying Heterogeneity—Regulating”, proposing that HH zones should implement “carbon sink enhancement and source reduction”, NS zones should balance “ecological expansion with growth management”, and LL zones should adopt “strict protection and development restriction”. This framework provides a quantifiable scientific basis and practical guidance for refined urban thermal environment management. Full article

Understanding trade-offs and synergies among ecosystem services (ESs) along environmental gradients is crucial for sustainable oasis management. This study investigated four key ESs—carbon storage (CS), habitat quality (HQ), water yield (WY), and soil conservation (SC)—in three typical oases along water–heat gradients in arid northwestern China. The InVEST model was used to quantify ESs in 1990, 2005, and 2022, and Pearson correlation, geographically weighted regression, K-means clustering, and random forest models were applied to analyze service relationships, ecosystem service bundles (ESBs), and driving factors. The results showed that CS and HQ maintained strong synergies, while the WY–SC relationship shifted from weak trade-offs under drier conditions to stronger synergies under more favorable water–heat conditions. Geographically weighted regression revealed spatial heterogeneity and directional asymmetry in ES relationships. Four ESB types were identified: ecologically fragile zones, ecological transition or buffer zones, agricultural production zones, and core ecological source zones. Driving-factor analysis indicated that vegetation-related services were mainly associated with land-cover structure and vegetation growth, whereas hydrological and erosion-related services were more closely linked to precipitation, potential evapotranspiration, temperature, and topography. These findings support differentiated oasis management through ecological restoration, development regulation, water-saving agriculture, and strict ecological protection. 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