Search Results (560)

Understanding urban deprivation and its impact on health is crucial for addressing inequalities in cities. Using Quito as a case study, we developed a census-based socioeconomic urban deprivation index and analyzed three health outcomes: fatal injuries, COVID-19 deaths, and maternal mortality. Spatial patterns were examined using Local Moran’s I, and regression analyses included OLS, spatial lag, spatial error, and GWR models, applied at two spatial scales, census sectors and census zones, with deprivation as the independent variable. Most of the regression models indicated that deprivation does not explain health outcomes, with the exception of fatal injuries and COVID-19 deaths at the census zone scale when spatial error models are applied. Our results also revealed MAUP effects, as spatial patterns and associations between the studied variables vary depending on spatial scale. Spatial models improved explanatory power compared to OLS, uncovering spatial dependence and heterogeneity in the relationships between deprivation and health outcomes. Our findings underscore the importance of multiscale and spatially explicit approaches in urban health research and provide actionable evidence for targeted interventions and urban planning that account for both local and structural patterns of deprivation. Full article

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions. Full article

Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R² = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran’s I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. Full article

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

Evaluating transit-oriented development (TOD) efficiency in metro station areas remains challenging, as the traditional “Node–Place” model gives limited consideration to guiding factors and struggles to account for inter-regional flows under spatial heterogeneity. To address these limitations, this study develops an enhanced “Node–Place–Accessibility” model by introducing an accessibility dimension to better capture station-level connectivity and walkability. DepthmapX and a convex space approach were applied to quantify station-area accessibility, reflecting passengers’ perceived spatial distance during transfers. The model establishes a TOD measurement framework based on spatial coupling and functional connectivity, enabling the identification of factors influencing metro ridership across different spatial scales. Moran’s I was employed to describe spatial agglomeration and a local spatial clustering method integrating both passenger flow and built-environment (BE) characteristics was constructed to reveal differentiated spatial patterns. The Multiscale Geographically Weighted Regression (MGWR) model was further employed to quantify the spatially varying impacts of BE factors on ridership. Results indicate that the improved model provides stronger discriminative power in identifying “balanced stations,” and that BE conditions exert significant impact on metro ridership, particularly in areas with strong coordination among TOD components. Among the BE dimensions, design granularity exerts a more substantial impact on ridership than connectivity, density, and accessibility. This methodology provides large cities with a reliable tool for formulating targeted strategies that promote positive interactions between transportation and land use, thereby supporting sustainable urban development. Full article

This study analyzed the relationships between land-cover types and the distribution of alien and invasive plant species using data from the 5th National Ecosystem Survey of Korea (2019–2023). A total of 711,557 plant occurrence records were collected across 780 map sheets, resulting in the identification of 3842 vascular plant species, including both alien and invasive taxa. To evaluate spatial patterns and environmental drivers, multiple linear regression and spatial regression models—specifically the Spatial Lag Model (SLM) and Spatial Error Model (SEM)—were applied. The results revealed that alien and invasive species exhibited non-random, spatially clustered distributions influenced by habitat type and disturbance intensity. Alien species were more abundant in agricultural areas and wetlands, whereas forests and grasslands acted as resistant ecosystems. In contrast, invasive species were concentrated in bare lands and urbanized drylands, highlighting the importance of habitat openness and human disturbance in facilitating invasion. Spatial autocorrelation analyses (Moran’s I = 0.0777 for alien species; 0.1933 for invasive species) and the strong spatial dependence in the Spatial Error Model (λ = 0.7405 and 0.6428) confirmed that invasion patterns are shaped by spatial connectivity and environmental continuity. These findings demonstrate that invasion processes in Korea are driven by both anthropogenic disturbance and spatial dependency. Effective management therefore requires habitat-specific, spatially coordinated strategies, emphasizing early detection and rapid control in high-risk areas while reinforcing the ecological buffering capacity of forests to maintain biodiversity and ecosystem stability. Full article

Nature reserves serve as core spatial units for maintaining regional ecological security and biodiversity. Owing to their high ecosystem integrity, extensive vegetation cover, and low levels of disturbance, they play a crucial role in sustaining ecological processes and ensuring functional stability. Taking the Giant Panda National Park (GPNP), which spans the provinces of Gansu, Sichuan, and Shaanxi in China, as the study region, the vegetation net primary productivity (NPP) during 2001–2023 was simulated using the Carnegie–Ames–Stanford Approach (CASA) model. Spatial and temporal variations in NPP were examined using Moran’s I, Getis-Ord Gi* hotspot analysis, Theil–Sen trend estimation, and the Mann–Kendall test. In addition, the Optimal Parameters-based Geographical Detector (OPGD) model was applied to quantitatively assess the relative contributions of natural and anthropogenic factors to NPP dynamics. The results demonstrated that: (1) The mean annual NPP within the GPNP reached 646.90 gC·m⁻²·yr⁻¹, exhibiting a fluctuating yet generally upward trajectory, with an average growth rate of approximately 0.65 gC·m⁻²·yr⁻¹, reflecting the positive ecological outcomes of national park establishment and ecological restoration projects. (2) NPP exhibits significant spatial heterogeneity, with higher NPP values in the northern, while the central and western regions and some high-altitude areas remain at relatively low levels. Across the four major subregions of the GPNP, the Qinling has the highest mean annual NPP at 758.89 gC·m⁻²·yr⁻¹, whereas the Qionglai–Daxiaoxiangling subregion shows the lowest value at 616.27 gC·m⁻²·yr⁻¹. (3) Optimal NPP occurred under favorable temperature and precipitation conditions combined with relatively high solar radiation. Low elevations, gentle slopes, south facing aspects, and leached soils facilitated productivity accumulation, whereas areas with high elevation and steep slopes exhibited markedly lower productivity. Moderate human disturbance contributed to sustaining and enhancing NPP. (4) Factor detection results indicated that elevation, mean annual temperature, and land use were the dominant drivers of spatial heterogeneity when considering all natural and anthropogenic variables. Their interactions further enhanced explanatory power, particularly the interaction between elevation and climatic factors. Overall, these findings reveal the complex spatiotemporal characteristics and multi-factorial controls of vegetation productivity in the GPNP and provide scientific guidance for strengthening habitat conservation, improving ecological restoration planning, and supporting adaptive vegetation management within the national park systems. Full article

China’s extensive grasslands are ecologically and economically vital but are increasingly degraded by grasshopper outbreaks. Traditional monitoring approaches are too limited for large-scale management. This study developed an advanced monitoring framework for the Xilingol League by integrating multi-source remote sensing, a degree-day model, and machine learning (ML). Field survey data from 2018 to 2023 were combined with 29 environmental variables aligned to grasshopper life stages. Four ML algorithms—Random Forest (RF), XGBoost, Multilayer Perceptron (MLP), and Logistic Regression (LR)—were evaluated for predictive performance. RF consistently outperformed other models, achieving the highest accuracy and robustness. Spatial autocorrelation analysis (Global Moran’s I) confirmed that grasshopper distributions were persistently clustered across all years, highlighting non-random outbreak patterns. Suitability mapping showed highly suitable habitats concentrated in East Ujumqin, West Ujumqin, and Xilinhot, with pronounced interannual variability, including a peak in 2022. Variable importance analysis identified soil type and vegetation type as dominant universal drivers, while precipitation, soil texture, and humidity exerted region-specific effects. These findings demonstrate that coupling biologically informed indicators with integrated learning provides ecologically interpretable and scalable predictions of outbreak risk. The framework offers a robust basis for early warning and targeted management, advancing sustainable pest control and grassland conservation. Full article

Climate volatility and market uncertainty pose significant challenges to agricultural stability. We assess whether and how agricultural industrial agglomeration shapes China’s agricultural economic resilience using province-level panel data for 2003–2023 and a transparent, entropy-weighted index spanning resistance, recovery, and adaptability. Four results stand out. First, in a two-way fixed-effects model, agglomeration is associated with higher resilience on average, and this finding remains robust across multiple robustness tests and after addressing endogeneity concerns. Second, regional subgroup analyses reveal pronounced heterogeneity, providing evidence for geographically targeted policy design. Third, mechanism analysis reveals that the agricultural research intensity serves as a partial mediator between agglomeration and resilience. Fourth, the agglomeration-resilience relationship is nonlinear—N-shaped in the aggregate, while panel quantile regressions reveal an inverted-U among low-resilience provinces and an N-shaped pattern at the median and upper end of the distribution. In an extension, global Moran’s I statistics for three alternative resilience indices reveal significant positive spatial autocorrelation, indicating that agricultural economic resilience tends to cluster geographically and that spatial spillovers are likely to be present. In conclusion, agglomeration is a net enhancer of agricultural economic resilience, but its payoffs are agglomeration- and distribution-dependent: gains taper or reverse around the mid-range for low-resilience provinces, while the median and upper segments benefit again as specialization deepens, in a setting where resilience itself is spatially clustered. Reinforcing the research channel and tailoring actions to local resilience levels are therefore pivotal. Full article

The Sichuan–Yunnan region, a primary seismic-prone zone on the Qinghai–Tibet Plateau, has experienced heightened seismic exposure due to rapid urbanisation. In order to address the issue of disaster risks and to promote sustainable urban development, this study establishes an integrated urban seismic resilience evaluation framework based on the DPSIR (Driving–Pressure–State–Impact–Response) model. The CRITIC–AHP combined weighting method was utilised to determine indicator weights, and data from 37 prefecture-level cities (2010, 2015, 2020) were analysed to reveal spatial–temporal evolution patterns and correlations. The results demonstrate a consistent improvement in regional seismic resilience, with the overall index increasing from 0.501 in 2010 to 0.526 in 2020. Sichuan exhibited a “decline-then-rise” trend (0.570 to 0.566 to 0.585), while Yunnan demonstrated continuous growth (0.517 to 0.557). The spatial pattern underwent an evolution from “west–low, central–eastern–high” to “south–high, north–low”, with over half of the cities attaining relatively high resilience by 2020. Chengdu and Kunming have been identified as dual high-resilience cores, diffusing resilience outward to neighbouring regions. In contrast, mountainous areas such as Garze and Aba have been found to exhibit low resilience levels, primarily due to high seismic stress and limited socioeconomic capacity. Subsystem analysis has revealed divergent resilience pathways across provinces, while spatial autocorrelation has demonstrated fluctuating global Moran’s I values and temporary local clustering. This research provides a scientific foundation for seismic disaster mitigation and offers a transferable analytical framework for enhancing urban resilience in earthquake-prone regions globally. Full article

The agro-pastoral ecotone of northern China (APENC), a typical semi-arid and ecologically vulnerable zone, has experienced considerable shifts in eco-environmental quality (EEQ) over the past two decades under the combined pressures of climate change and human activities. However, systematic understanding of the spatiotemporal evolution and driving mechanisms of EEQ in this region remains limited. Based on multi-source remote sensing data from 2000 to 2020, this study constructed an ecological quality assessment index (EQAI) using principal component analysis (PCA) and quantitatively identified driving factors through geographical detector modeling. The results reveal a consistent improvement in EEQ over the study period, characterized by a marked expansion of higher-quality areas and a contraction of degraded zones, though spatial heterogeneity remained evident. Global and local spatial autocorrelation analyses (Moran’s I) confirmed a distinct clustering pattern, with persistent low-value clusters in the northwest and high-value clusters in the southeast and north. Notably, the most pronounced EEQ enhancement occurred between 2000 and 2005. Overall, 90.24% of the region exhibited an improving trend, while only 9.76% showed degradation. Hurst exponent analysis further indicated that this improving trend is likely to continue in the future across most areas. Factor detection identified meteorological drivers (precipitation) as the strongest influencer on EEQ, followed by land use type. Socioeconomic factors demonstrated relatively minor impact. These findings provide a scientific basis for ecological restoration policy-making and sustainable land management in the APENC and other ecologically fragile transitional regions. Full article

Background/Objectives: This study investigates end-stage renal disease cases in Texas using a multilevel spatial survival modeling framework. The objective is to evaluate a multilevel model specification that incorporates regional as well as individual factors, and that can be extended with random effects capturing unexplained variation in the independent variables; these random effects can be partitioned into simultaneous spatially structured and spatially unstructured components. Methods: The analysis uses data from 109,018 adult patients who initiated end-stage renal disease treatment between 2009 and 2018, obtained from the United States Renal Data System. This paper presents this model structure for survival analysis using Moran eigenvector spatial filtering, providing an alternative way to conduct advanced spatial survival analysis. Results: Clinical variables, particularly age, cardiovascular comorbidities, and transplant status, are dominant predictors of survival. Racial disparities are observable, with Asian and Black patients exhibiting lower mortality risk relative to White patients. Socioeconomic indicators (poverty, urbanicity, and unemployment rate) show attenuated significance after adjusting for spatial and aspatial random effects, indicating their impact is partly mediated through unobserved regional heterogeneity and spatial autocorrelation. Conclusions: These findings underscore the necessity of accounting for spatial dependencies and multilevel structures in survival analysis to avoid potentially biased inferences. The devised approach can offer a robust framework for guiding geographically targeted health interventions and resource allocation aimed at improving end-stage renal disease patient outcomes and reducing health disparities across diverse regions. Full article

Effective governance of informal commercial spaces is a common challenge faced by cities globally. To break through the superficial governance mindset of traditional spatial regulation, this study focuses on clarifying the spatial distribution characteristics and influencing factors of such spaces. By integrating the theory of “The right to the city” with the “7D” principles of New Urbanism, and focusing on the Jinxiang Street area in Harbin, a representative zone combining traditional industrial and modern residential communities, this study constructed a multidimensional indicator framework including population factors, functional diversity of facilities, accessibility of the built environment, spatial suitability, and intensity of community management, extracting 17 significant variables. Through spatial autocorrelation analysis (Moran’s I), multiscale geographically weighted regression (MGWR), and geographic detector analysis, the results show that informal commercial spaces exhibit clustered yet uneven characteristics between aging and upscale communities; the MGWR model reveals significant spatial heterogeneity in influencing factors; and geographic detector analysis shows that the interaction between public service facilities’ proximity to main roads and enhanced community management has the most significant explanatory power for heterogeneity (q = 0.85). These findings inform differentiated governance strategies and provide scientific support for sustainable governance of informal commercial spaces. Full article

Understanding the spatial drivers of community satisfaction is crucial for achieving inclusive and sustainable urban development. However, traditional spatial regression models often assume linearity and fail to capture complex, spatially heterogeneous relationships. This study integrates a GeoShapley-based explainable GeoAI framework with the XGBoost algorithm to identify and quantify spatially varying factors influencing community satisfaction in Chengdu, China. By incorporating geographic coordinates as explicit spatial features, the GeoShapley method decomposes model outputs into intrinsic spatial effects and feature-specific interaction effects, enabling the interpretation of how and where each factor matters. Results show significant spatial clustering (Moran’s I = 0.60, p < 0.01) and a distinct south–north gradient in satisfaction. Built environment indicators—including building coverage ratio (BCR), walkability index (WI), and distance to green space (DGS)—exhibit nonlinear relationships and clear thresholds (e.g., BCR > 0.15, DGS > 590 m). Social vitality (Weibo check-ins) emerges as a key local differentiator, while education and healthcare accessibility remain spatially uniform. These findings reveal a dual structure of public service homogenization and spatial-quality heterogeneity, highlighting the need for place-specific, precision-oriented community renewal. The proposed GeoXAI framework provides a transferable pathway for integrating explainable AI into spatial sustainability research and urban governance. Full article

Land use change is closely related to land surface temperature (LST). Based on remote sensing data from 2001 to 2020, this study analyzed the spatiotemporal variations and driving mechanisms of daytime and nighttime LST in the Urumqi Metropolitan Area (UMA) by combining traditional methods with the eXtreme Gradient Boosting (XGBoost)–SHAP coupled model. Although the average LST trend in the region was one of warming, the pixel-level significance analysis indicated that statistically significant warming (p < 0.05) is concentrated mainly in the urban core (2.65% of the area), while the majority of the region (70%) showed a non-significant warming trend. LST displayed significant spatial clustering, with Moran’s I remaining above 0.990, indicating a positive spatial autocorrelation in spatial distribution. With the advancement of urbanization, the proportion of impervious surfaces increased from 0.87% to 1.14%, while wastelands consistently accounted for approximately 50% of the total area. Different land use types showed distinct effects on the urban heat island (UHI) phenomenon: water bodies, grasslands, and forests played cooling roles, whereas barren land and impervious areas were the main heat contributors. The XGBoost-SHAP analysis further revealed that the importance ranking of driving factors has evolved over time. Among these factors, Elevation dominates, while the influence of population-related factors increased significantly in 2020. This study provides a scientific basis for regulating the thermal environment of cities in arid regions from the perspective of land use. This study provides a scientific basis for regulating the thermal environment of arid-region cities from the perspective of land use. Full article