Search Results (610)

The existing research has rarely integrated within-province associations, interprovincial spatial linkages, and multidimensional heterogeneity when examining how digitalization is related to increasing household consumption. Using a balanced panel of 30 provincial-level regions in China for 2011–2023, compiled from national and provincial statistical yearbooks (CSMAR) and the Peking University Digital Financial Inclusion Index, this study constructs a 0–1 digital economy development index with entropy-weighted TOPSIS. Two-way fixed effects estimate the average within-province relationship; global and local Moran’s I and a spatial Durbin model evaluate spatial dependence and decompose direct, indirect, and total effects. Panel quantile regressions and alternative spatial weight matrices serve as robustness checks, whereas instrumental variables and double/debiased machine learning provide supplementary identification evidence. Digital economy development is positively associated with consumption upgrading in the baseline model. Under economic distance weights, the direct, indirect, and total effects are all significantly positive, although their structure differs across consumption categories, urban and rural groups, regions, and temporal stages. The findings support combining digital infrastructure with service capacity, skills, consumer protection, and interprovincial governance while avoiding uniform policy prescriptions across regions. Full article

Anthropogenic climate change has disrupted the global hydrological cycle, increasing compound extreme events like Dry–Wet Abrupt Alternations (DWAAs). Regarding the Mediterranean Basin, Greece is highly susceptible to these abrupt hydroclimatic shifts, which frequently overwhelm reactive disaster management. This study quantifies the spatiotemporal dynamics of DWAA events across Greece from 1990 to 2024. Using the 1-month Standardized Precipitation Evapotranspiration Index (SPEI-1) from ERA5 reanalysis, transitions were classified into dry-to-wet (DW) and wet-to-dry (WD) across moderate (±1.0), severe (±1.5), and extreme (±2.0) thresholds. Core physical metrics (duration, severity, and intensity) were evaluated using Anselin Local Moran’s I (LISA) and Mann–Kendall tests to identify spatial hotspots and temporal trends. Results revealed a spatially decoupled hazard regime dictated by topography and atmospheric mechanics. Severe DW transitions primarily manifest as intense autumn flash floods (62.7%) concentrated in western and southern districts. Conversely, severe WD transitions emerge as high-magnitude summer agricultural flash droughts (52.5%) clustered in central and northern continental plains. Crucially, while the magnitudes of these events demonstrate historical temporal stationarity, their decadal frequency doubled in the 2020s. This increase validates the idea that global warming accelerates systemic climate extremes, necessitating an urgent shift toward proactive, highly localized adaptation strategies. Full article

Digital transformation is portrayed both as a lever of territorial convergence and as a driver of polarisation between urban cores and peripheries, yet its effect on regional economic resilience has rarely been measured systematically. This study transposes the Economic Resilience Index framework from the national to the regional scale, building a Regional Resilience Index (R-ERI) for 236 NUTS2 regions of the EU-27 from Eurostat indicators, anchored in the capacities of absorption, recovery, and adaptation and measuring resilience as a capacity rather than as a realised shock trajectory. Two complementary models are estimated: a spatial Durbin panel with two-way fixed effects (2018–2023), spanning the COVID-19 pandemic and 2022 energy shocks, and an exploratory cross-sectional difference model exploiting regional artificial intelligence (AI) adoption data disaggregated by NACE branch (2023–2025). The results show that resilience is strongly spatially autocorrelated (Moran’s I between 0.66 and 0.74; p = 0.001); that digital connectivity generates a positive indirect effect on neighbouring regions despite a negative own-region effect; and that the synergy hypothesis—that digitalisation yields more resilience when combined with traditional sectors—does not hold robustly, the interaction being null in the panel and only marginally positive in the AI layer (p = 0.10). We conclude that digital connectivity is not, on its own, an automatic convergence mechanism, and that cohesion policy should account for each region’s sectoral structure and peripheral position. Full article

This study evaluates groundwater quality in the Bismil Plain (Diyarbakır, Southeast Türkiye) using a total of 208 samples collected from 26 wells during eight seasonal sampling periods conducted between 2022 and 2024. In each sample, pH, electrical conductivity (EC), and the major ions Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl⁻, SO₄²⁻, HCO₃⁻ and NO₃⁻ were analyzed, and a WHO-based Water Quality Index (WQI) was calculated for every observation. The study combines classical hydrochemical interpretation methods, including descriptive statistics, hierarchical correlation analysis, variance inflation factor, and Piper and Gibbs diagrams, with an explainable machine learning framework integrating SHAP-based feature selection into Random Forest, XGBoost, support vector regression, and stacking ensemble models. In addition, spatial residuals were evaluated using Moran’s I and ordinary kriging, anomalies were identified using Isolation Forest and Local Outlier Factor algorithms, and predictive uncertainty was quantified through bootstrap resampling. WQI values ranged from 79.37 to 125.48 (mean: 99.67), with all samples classified only within the “Good” (49.5%) and “Poor” (50.5%) quality categories, indicating that the aquifer is close to a critical water-quality threshold. Spatially, the highest (poorest-quality) WQI values form a coherent zone in the south-western and central parts of the plain, whereas the central-eastern wells return the lowest values; the same pattern is reproduced by all four models. XGBoost and the stacking ensemble models showed comparable predictive performance (R² = 0.911 and 0.910; RMSE = 3.29 and 3.27, respectively), while SHAP analysis identified EC as the dominant controlling factor, followed by NO₃⁻, SO₄²⁻, Ca²⁺, Mg²⁺ and Cl⁻ (mean |SHAP| = 6.86, 1.57, 1.10, 1.09, 0.85 and 0.72 WQI units, respectively). Moran’s I computed on the residual fields was −0.067 (p = 0.275) for XGBoost and −0.068 (p = 0.273) for the stacking ensemble, so ordinary kriging of these residuals produced an essentially null correction, whereas the SVR residuals remained spatially autocorrelated (I = 0.242; p = 0.001) and were meaningfully corrected by the geostatistical step. The originality of the study lies in integrating explainable machine learning, geostatistical residual analysis, anomaly detection, and bootstrap-based uncertainty assessment within a unified framework for a multi-season groundwater dataset, while also evaluating the effectiveness of spatial correction using a Moran’s I-based approach. Full article

Official cadastral values in Latin American intermediate cities may fail to keep pace with rapidly changing asking-price patterns, weakening the responsiveness of local property-tax bases. This study introduces the Relative Gap Rate (TBR), defined as the proportional divergence between observed vacant-land asking prices and official cadastral values, and applies it to Ambato, Ecuador. A dataset of 182 georeferenced asking-price observations was interpolated across a common cantonal support of 19,231 hexagonal cells. Predictors comprised a hierarchically weighted Urban Vitality Index (UVI), distance to the structural road network, elevation, and traffic-incident count derived from 1787 records. The TBR surface exhibited very strong positive spatial autocorrelation (Moran’s $I=0.956$, $p<0.001$), while the discrete asking-price observations were also spatially autocorrelated (Moran’s $I=0.282$, $p<0.001$). Lagrange Multiplier diagnostics supported a Spatial Autoregressive specification (pseudo-$R^2=0.927$; $\rho =0.885$). The Random Forest model achieved a test-set $R^2$ of 0.8319 and an MAE of 5.3252, with UVI accounting for 64.37% of impurity-based feature importance. The median TBR was 6.0, meaning that the median asking price was seven times the corresponding cadastral value; the maximum TBR reached 43.0. Traffic-incident count retained a positive spatial-regression coefficient but contributed only 5.53% of Random Forest importance. The framework provides an adaptable municipal diagnostic for identifying cadastral–asking-price divergence, subject to local validation and city-specific calibration. Full article

Karst hillslope land systems are characterized by strong microtopographic heterogeneity, exposed rock–soil patches, and management-related disturbance, resulting in pronounced fine-scale variation in ecological responses. This study investigated three contrasting karst hillslopes within the Guanling–Zhenfeng Huajiang Rocky Desertification Comprehensive Control Demonstration Area in Guizhou Province, China, designated as High, Medium, and Natural sites according to their observed land cover and management characteristics, using UAV multispectral imagery and LiDAR-derived DEM data. A terrain–ecology coupling strength indicator (TECSI) framework was developed to assess the spatially transferable predictability of ecological response patterns by terrain variables. At the 5 m block scale, FVC, OSAVI, NDRE750, rock–soil exposure index (REI), and GLCM contrast were linked with LiDAR-derived microtopographic predictors using generalized additive models, residual Moran’s I, random cross-validation, and spatial block cross-validation. FVC, OSAVI, NDRE750, and GLCM contrast differed significantly among the three sites (p < 0.001), whereas REI mainly reflected patchy non-vegetated rock–soil substrate exposure within hillslopes. Across the 15 site–indicator models, deviance explained ranged from 5.5% to 35.8%. Under 25 m spatial block cross-validation, integrated TECSI ranked Medium (0.143) > High (0.086) > Natural (0.036), and this ranking remained stable across sensitivity analyses. Steep-slope units at or above 25° in the High and Medium sites were associated with poorer vegetation condition and increased substrate exposure. TECSI provides a reproducible spatial validation framework for identifying terrain-sensitive ecological response units and supporting fine-scale monitoring, soil–water conservation screening, and vulnerable land unit management in karst hillslopes. Full article

Tree-ring chronologies are essential proxies for investigating ecosystem dynamics and reconstructing environmental variability, yet integrative approaches for assessing chronology quality and sampling representativeness across heterogeneous regions remain limited. We analyzed 190 tree-ring chronologies distributed across Mexico and developed two composite indicators: the Signal Quality Index (SQI), integrating internal coherence, interannual sensitivity, common growth signal strength, and the Sampling Representativeness Index (SRI), quantifying the statistical adequacy of sampling efforts. Both indices were standardized and evaluated using Moran’s I, Local Indicators of Spatial Association (LISA), Getis–Ord Gi* hotspot analysis, and correlations with climatic, hydrological, and edaphic variables. Results revealed a marked decoupling between chronology signal quality and sampling representativeness. SQI exhibited significant positive spatial autocorrelation, with clusters of high and low values associated with hydroclimatic gradients. It was strongly related to indicators of water availability and atmospheric evaporative demand, suggesting greater growth coherence under water-limited conditions. In contrast, SRI displayed weak spatial structure and largely non-significant relationships with environmental variables, indicating that representativeness is driven primarily by methodological decisions and sampling design. These findings highlight complementary ecological (SQI) and methodological (SRI) dimensions of dendrochronological networks and provide a practical framework for improving chronology evaluation, comparability, and network development across environmentally heterogeneous regions. Full article

Agricultural nonpoint-source phosphorus (NPS-P) losses threaten receiving waters, but regional control is complicated by differences in source intensity, erosion sensitivity, and hydrologic connectivity. This study jointly applied an improved export coefficient model (IECM), the Revised Universal Soil Loss Equation (RUSLE), Global and Local Moran statistics, and Getis-Ord Gi* analysis to six contrasting basins. The outputs were cross-interpreted without a formal composite index. Average annual soil erosion ranged from 1.96 to 18.47 t ha⁻¹ yr⁻¹, with very slight and slight erosion dominating all basins. Annual NPS-P export ranged from 2169.55 to 12,028.32 t yr⁻¹ (1.26–2.74 kg ha⁻¹ yr⁻¹), and cultivated land contributed 48.60–69.32% of modeled export. Under 999 random permutations, Global Moran’s I ranged from 0.615 to 0.834 (pseudo p = 0.001), and Gi* hot spots occupied 26.18–33.59% of valid cells. The basin-level perturbation analysis indicated greater ranking robustness for clearly high- and low-load basins than for intermediate basins, while the cultivated-land sensitivity analysis quantified the influence of the dominant coefficient. The framework supports regional screening, monitoring prioritization, and subsequent field verification rather than calibrated event-scale prediction. Full article

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ²(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches. Full article

Climate change and rapid urbanization are reshaping the spatial relationship between flood risk and socioeconomic development, yet basin-scale evidence on where risk pressure and development capacity diverge remains limited. Taking 54 cities in China’s Pearl River Basin as the study area, this study adopts a dual-system evaluation framework integrating game-theory weighting, coupling coordination analysis, and spatial autocorrelation. Flood risk is characterized through hazard, exposure, vulnerability, and Resilience, while socioeconomic development is represented by economic scale, urban construction, and population agglomeration. This design extends previous Pearl River Delta-centered analyses to the entire basin and enables city-scale identification of risk–development imbalance. The results reveal a pronounced downstream–upstream gradient. The Pearl River Delta has the highest flood exposure (mean exposure index = 0.2603, 117% higher than the basin average of 0.1208), but strong Resilience and socioeconomic capacity prevent high exposure from translating into the highest overall risk. By contrast, east-central Guangxi emerges as a critical hotspot where moderate to high hazard overlaps with high vulnerability and weak Resilience. Coupling coordination remains generally low, shifting from basic coordination in parts of the Pearl River Delta to severe imbalance in upstream Guizhou, Guangxi, and Yunnan. The global Moran’s I is 0.4261 and significant at the 1% level, indicating marked spatial clustering, with high-high clusters concentrated in core Pearl River Delta cities and low–low clusters in eastern Yunnan and southwestern Guizhou. By distinguishing high exposure cities from high priority intervention areas, this study provides a potentially transferable framework for differentiated flood governance, infrastructure investment, and Resilience enhancement across large river basins. Full article

Heritage tourism is central to Spain’s cultural and territorial development, but its spatial distribution and intensity are still unevenly understood. This study analyzes heritage tourism in Spain as a territorially differentiated phenomenon by comparing provincial differences in tourism intensity, heritage tourism density, and cultural heritage concentration. Drawing on official experimental statistics from the National Institute of Statistics of Spain, the analysis integrates mobile phone geolocation data from 181,670,728 devices across 3214 destinations, tourism expenditure information, and a Heritage Concentration Index constructed for architectural cultural heritage. Four one-way ANOVA models were applied to assess differences among Spain’s 52 provinces in the Internal Tourism Intensity Index, External Tourism Intensity Index, Heritage Tourism Density, and Heritage Concentration Index. Hochberg-adjusted post hoc comparisons and Tukey HSD robustness checks were used to examine interprovincial differences, while Welch ANOVA and Moran’s I were added to assess robustness and spatial structure. The results show statistically significant territorial differences across all four indicators, indicating that heritage tourism in Spain is not spatially homogeneous. The study contributes an integrated, data-driven approach for measuring heritage tourism at the provincial scale and provides diagnostic evidence that may inform differentiated tourism planning, destination management, and more balanced heritage-based territorial strategies. Full article

Highway–rail grade crossing (HRGC) incidents in the United States declined substantially for several decades before stabilizing in recent years. Understanding this persistence is important because future safety improvements may depend on identifying locations where incident occurrence remains resistant to further reduction. This study developed an integrated framework to characterize persistent HRGC incident environments using 50 years (1976–2025) of Federal Railroad Administration incident records. Trend, structural-break, variance, and stationarity tests were first applied to determine whether the historical decline transitioned into a distinct persistence regime. A county-level persistence index (PI) was then developed to quantify the combined effects of incident burden and resistance to decline during the plateau period. Distributional analysis characterized the statistical behavior of the PI, while global and local Moran’s I statistics evaluated its spatial organization. Explainable machine learning methods were subsequently used to identify incident characteristics associated with elevated persistence. The results identified a statistically significant regime change around 2010. Prior to 2010, incidents exhibited a strong declining trend, whereas the subsequent period displayed a statistically significant but substantially weaker decline, lower variance, and behavior consistent with a persistence regime characterized by a markedly attenuated rate of improvement. The PI followed a strongly right-skewed distribution that was best represented by a bounded heavy-tailed unit log-logistic model, indicating that persistence is concentrated within a relatively small subset of counties. Spatial analysis revealed significant positive spatial autocorrelation (Moran’s I = 0.180, p = 0.001) and geographically coherent clusters concentrated primarily in the southeastern United States and several major freight-oriented regions. Explainable machine learning models identified train-operating characteristics, warning device contexts, movement patterns, and temporal conditions as key attributes associated with high-persistence counties. The findings demonstrate that the post-2010 incident plateau is sustained disproportionately by a limited number of geographically concentrated environments and provide a framework for supporting more targeted safety interventions. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

Rural green development is essential for coordinating ecological protection, rural revitalisation and spatial governance in ecological spaces within urban agglomerations. Taking the Chang-Zhu-Tan Ecological Green Heart region in China as a case study, this study evaluates rural green development in 10 counties, county-level cities and districts from 2013 to 2022. A combined weighting–TOPSIS model is used to measure development levels, while Global Moran’s I, the Theil index and XGBoost–SHAP are applied to examine spatial patterns, regional disparities and key associated factors. The results show that rural green development improved overall, with the regional average index increasing from 0.315 in 2013 to 0.378 in 2022. High-value areas gradually expanded, and the Changsha group formed the main high-value core, but no statistically significant global spatial clustering was identified. Regional disparities narrowed during the study period, although intra-group disparities, especially within the Zhuzhou group, remained the main source of imbalance. Human capital, medical resources, income level, urbanisation level, PM_2.5 concentration and pesticide use intensity were closely associated with rural green development, with socioeconomic factors generally contributing positively and environmental pressures acting as constraints. These findings suggest that rural green development in ecological green heart regions is a spatially differentiated and multi-factor process, providing empirical evidence for differentiated county-level governance, coordinated ecological protection and rural green transformation in urban agglomerations. Full article

Climate change is intensifying hydrological extremes, yet most frameworks assess drought and flood hazards independently, limiting integrated risk management. This study proposes a two-dimensional analytical framework to characterize the drought-flood continuum, moving beyond single-index approaches. We introduce the Hydro-Hazard Index (HHI) as a directionality metric (HHI = Flood Severity − Drought Severity) to classify the dominant hazard type, and the Total Severity Index (TSI = Flood Severity + Drought Severity) as a complementary metric to quantify overall hazard magnitude. Analyzing multi-temporal data from 115 hexagonal units (2018–2024), we employed dynamic features (trends, changes, volatility) and four machine learning models to classify areas as “flood-prone” based on validated flood records. Our results show HHI values ranging from −2.44 to 8.81, with 20.9% of areas classified as Flood-Dominated (mean HHI = 4.58) and 79.1% as Normal (mean HHI = 0.76). Crucially, the two-dimensional analysis revealed that areas with identical HHI values can have vastly different TSI values, under scoring the importance of our dual-index approach. Random Forest achieved the highest performance in predicting flood-prone status (Accuracy = 0.913, AUC = 0.967, Recall = 1.00), with flood_volatility as the most important predictor (24.2%). Spatial autocorrelation confirmed strong clustering of high-risk areas (Moran’s I = 0.716, p < 0.001). By analyzing flood and drought as distinct but interacting dimensions, this framework provides a more robust and nuanced tool for integrated risk assessment. While acknowledging limitations related to data availability and the need for further independent validation, the proposed framework supports sustainable water resource management and climate adaptation planning under increasing hydrological uncertainty. Full article