Search Results (1,231)

Background: Socioeconomic deprivation is a well-established determinant of participation in colorectal cancer (CRC) screening. However, less is known about the geographic clustering of screening adherence and whether spatial inequalities mirror regional patterns of socioeconomic disadvantage. This study investigated the association between socioeconomic deprivation and adherence to CRC screening across Italian regions and explored the presence of spatial autocorrelation using geographic analytical methods. Methods: We conducted a nationwide ecological cross-sectional study including 21 Italian regional units. Pearson correlation analysis was performed to explore associations between colorectal cancer (CRC) screening adherence, primary care physician (PCP) workload and socioeconomic deprivation. Multivariable linear regression was used after assessment of multicollinearity using variance inflation factors (VIFs). Sensitivity analyses included alternative deprivation indicators and leave-one-region-out analyses. Spatial dependence was evaluated using Global Moran’s I and Local Indicators of Spatial Association (LISA). Results: CRC screening adherence showed substantial geographic variability across Italy, with higher participation in northern regions and lower participation in southern areas. Screening adherence was inversely associated with socioeconomic deprivation. Sensitivity analyses confirmed the robustness of the association across alternative deprivation measures and sequential exclusion of each regional unit. Spatial analysis demonstrated significant positive spatial autocorrelation (Moran’s I = 0.275; p = 0.039), indicating that neighbouring regions tended to exhibit similar screening behaviours. LISA analysis identified significant High–High clusters in northern Italy, Low–Low clusters in southern and insular regions, and isolated spatial outliers, highlighting persistent territorial inequalities in preventive healthcare utilisation. Conclusions: Regional disparities in CRC screening participation were associated with socioeconomic deprivation and modest but significant spatial clustering. These findings provide a regional-level description of inequalities in screening participation and may help inform future public health strategies aimed at reducing disparities in organised colorectal cancer screening. Full article

Net primary productivity (NPP) is a core diagnostic variable of terrestrial carbon cycling, yet consistent annual NPP simulation remains challenging in alpine heterogeneous regions where topography, hydrothermal gradients, vegetation structure, and nutrient constraints interact. Remote-sensing products such as MODIS provide valuable observational constraints, whereas light-use-efficiency models such as CASA retain process transparency and scenario transfer capability. This study develops a CASA-based, MODIS-constrained framework for annual NPP simulation over the Gannan Plateau. The framework preserves a locally parameterized CASA baseline and adds a geographically weighted regression (GWR) residual-alignment layer trained on CASA–MODIS residuals during 2005–2013. The fitted correction relationship was then applied to the 2014–2020 temporal transfer period and evaluated in a 2030s SSP scenario transfer experiment. MODIS was treated as the correction target rather than ground truth, and GLASS was adopted as an independent product-level benchmark. During 2014–2020, the GWR-corrected product showed improved pooled pixel-level agreement with the MODIS-constrained target relative to parameter-localized CASA, with R² increasing from 0.438 to 0.708 and RMSE decreasing from 91.4 to 68.7 g C m⁻² yr⁻¹. Residual Moran’s I also decreased, indicating weaker residual spatial organization after correction. Product-level comparison with GLASS showed a moderate, directionally consistent improvement relative to uncorrected CASA, although this comparison was not interpreted as ground-truth validation. The 2030s scenario transfer experiment indicated that the correction layer changed the spatial expression of NPP divergence among SSP pathways. Overall, the proposed framework provides a process-model-preserving and observation-constrained approach for improving agreement between annual NPP estimates and the MODIS-constrained target in alpine heterogeneous regions, while its applicability remains subject to product uncertainty, spatial dependence, and future nonstationarity. 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

Ammonium nitrogen (NH₄⁺-N) contamination in groundwater beneath chemical industrial parks exhibits extreme spatial heterogeneity, yet the comparative effectiveness of spatial prediction methods under such conditions remains poorly understood. At a chemical industrial park in a region of Shanxi Province, northern China, we analyzed 133 monitoring wells sampled across four campaigns (April–October 2024) at two aquifer depths. Global Moran’s I (0.040–0.118) and variogram nugget ratios (>75%) indicated weak spatial autocorrelation. Consequently, on the raw concentration scale, all six geostatistical methods yielded near-zero or negative leave-one-out cross-validation (LOO-CV) R². Evaluated on the log₁₀ scale, machine learning (ML) models achieved positive predictive skills, with Extreme Gradient Boosting (XGBoost) performing best (R² ≈ 0.75). Three hybrid ML–kriging methods produced physically coherent plume surfaces while retaining their predictive skills; the April upper-layer result (R² ≈ 0.67)—the only campaign without retained within-well information—best represents spatial generalization, whereas the higher later-campaign values (R² > 0.97) are optimistic. Exceedance probability mapping based on XGBoost (area under the ROC curve, AUC = 0.959) revealed a persistent high-risk zone. Because the geostatistical and ML metrics span different response scales and validation schemes, their comparison is indicative rather than a direct ranking. Spatial autocorrelation diagnostics should precede method selection at point-source-dominated industrial sites. 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

Ensuring a high quality of life for citizens is a fundamental objective for every city, with public safety representing one of the most critical challenges to social sustainability and equitable urban development. This study analyzes the spatiotemporal dynamics of violent crime in Mexico City across pre-pandemic, pandemic, and post-pandemic periods (2019–2023) to evaluate how COVID-19 mobility restrictions were associated with changes in crime patterns. Using publicly available crime reports, we applied Seasonal-Trend decomposition using Loess (STL) and Moran’s I to examine four violent crimes: homicide, robbery, kidnapping, and rape. The results reveal crime-specific pandemic-related patterns. While robbery showed a sustained decline, its spatial clustering intensified significantly, with Moran’s I increasing from 0.22 to 0.59, indicating highly localized risk zones. Conversely, rape exhibited a steady increase that appeared unaffected by lockdown measures, while maintaining significant spatial autocorrelation. Likewise, Cuauhtémoc borough persisted as the main urban hotspot across all phases. Overall, crime did not decline uniformly during the pandemic; instead, mobility restrictions reshaped the geographic distribution and concentration of specific offenses. This research contributes to the understanding of crime dynamics during and after a public health emergency. Full article

Oil extraction and agricultural production are central to North Dakota’s economy, yet their spatial interactions remain poorly understood. This study conducts a statewide geospatial analysis integrating OpenStreetMap data, GIS processing, DBSCAN clustering, and spatial statistics to examine the colocation of 7102 oil well and 4277 grain silo sites. Hotspot and spatial heterogeneity tests using the Getis–Ord Gi* statistic and local Moran’s I reveal a pronounced spatial divide: oil activity is tightly clustered in the western Bakken region, whereas grain storage facilities concentrate across central and eastern counties. The limited geographic overlap suggests minimal systemic land-use conflict, though localized high-intensity interactions emerge in McKenzie, Dunn, and Mountrail counties. These patterns provide stakeholders with insight into potential shared logistics pressures and localized land-use tensions. More broadly, the study demonstrates the value of spatial data mining techniques applied to free, publicly available data for identifying intersectoral industrial patterns that inform policy and infrastructure planning across North Dakota. Full article

The middle reaches of the Yellow River Basin are ecologically fragile and face increasing tension between ecological restoration and socioeconomic development. This study developed an integrated risk-service framework to assess spatiotemporal ecological security, identify dominant drivers, and support zoning-based management. A Comprehensive Ecological Security Index combining landscape ecological risk with four InVEST-derived ecosystem services showed a steady improvement from 2000 to 2025, especially after 2010. Higher ecological security was mainly concentrated in the southern and southeastern mountainous areas, whereas lower values persisted in the central plains and northern semi-arid areas. The index showed significant positive spatial autocorrelation (Moran’s I = 0.413–0.474, p < 0.001). Forest coverage was the strongest driver (q = 0.504), with a threshold effect at 40–50% coverage. ${\mathrm{P}\mathrm{M}}_{2.5}$ concentration and population density had negative effects, and most driver interactions showed nonlinear enhancement. Five ecological security zones were identified for differentiated management. The proposed framework links ecological risk, ecosystem service capacity, interpretable machine learning, and zoning-based governance. It provides a transferable approach for ecological security assessment and spatial management in the Middle Yellow River Basin and comparable dryland watersheds. Full article

Understanding how species richness is distributed across space is a central goal of macroecology and biogeography. The gecko genus Hemidactylus ranks among the most speciose and broadly distributed of all squamate genera, with species spanning tropical and subtropical environments on most continents. Yet the large-scale structure of its diversity has rarely been examined at a global scale. Here we assembled georeferenced occurrence data for the genus from the Global Biodiversity Information Facility (GBIF Access data is 14 March 2026) and, following cleaning and quality control, retained 143,858 records covering 157 currently recognized species (approximately 79% of the genus) to characterize broad-scale biodiversity patterns. Our analyses addressed the latitudinal diversity gradient, the distribution of species range sizes, continental richness, spatial clustering of diversity, and regional beta diversity. Richness declined steadily from the tropics toward the poles, producing a pronounced latitudinal diversity gradient (Pearson r = −0.71, p < 0.001). A generalized additive model captured a strong non-linear association between latitude and richness (deviance explained = 98.1%), consistent with tropical regions acting as principal centers of diversity. Range-size distributions were markedly right-skewed, with the majority of species restricted to comparatively small areas; however, these estimates co-varied strongly with per-species sampling effort (r = 0.90) and warrant cautious interpretation. We found only a weak, marginally significant association between range size and latitudinal midpoint, offering little support for Rapoport’s rule in the genus. At the continental scale, Hemidactylus diversity was overwhelmingly concentrated in Afro-Asian tropical regions (Asia, 93 species; Africa, 66), whereas Europe and Oceania supported few species (four each). Spatial analyses indicated significant autocorrelation in richness (Moran’s I = 0.56, p < 0.001) and well-defined diversity hotspots in East Africa, the Indian subcontinent, and Southeast Asia, and regional beta diversity was high (Jaccard dissimilarity up to 0.97), reflecting pronounced faunal turnover among continents. Full article

Geographic variation (GV) in screening patterns for breast cancer exists among Zip Code Tabulation Areas (ZCTAs) in Florida, but most spatial analyses are based on frequentist point estimates which do not formally represent uncertainty. This study used a three-stage analytical approach to the breast cancer screening data at the zip code tract (ZCTA) level in Hillsborough County, Florida (n = 55 ZCTAs): first, a frequentist Poisson regression was applied with diagnostics for multicollinearity; second, global spatial autocorrelation (GSA) analysis was conducted using Moran’s I; and third, a Bayesian Poisson and a Bayesian negative binomial regression were performed, both estimated via the No-U-Turn Sampler in the brms/Stan framework. In ArcGIS Pro 3.6, spatial analyses were carried out. The dependent variable was the number of breast cancer screening exams conducted at the ZCTA level over the study period. Across all racial/ethnic subgroups, the observed number of screenings was correlated with the number of females in the household, while no independent correlation was found for median household income, insurance status or age by stratum variables after adjustment. There was no significant and strong spatial autocorrelation across the study area (Moran’s I = 0.003, z = 0.326, p = 0.745). The Poisson model did best among the Bayesian models with a Bayesian R² of 0.91, RMSE of 5.40, and MBE of 0.02. The results show the usefulness of Bayesian uncertainty quantification in small area public health surveillance and offer a framework for quantifying geographic variation in screening activity in a probabilistic manner. The results only compare screening examination (not population-standardized screening rates) and should be considered to reflect screening volume rather than screening participation. Full article