Search Results (491)

Administrative emergency records can support spatiotemporal analysis in territories that are rarely represented in urban-computing research, but recorded volume is not equivalent to population risk or underlying incidence. This study develops an integrated framework for characterizing and predicting ECU911 recorded incident volume in Loja and Zamora Chinchipe, Ecuador, from January 2015 to 30 June 2026. The analysis used 925,000 administrative rows; 905,038 fell within the prespecified study period, and 899,232 were retained for spatial analysis after date and coordinate control. Annual spatial analysis used a 1 km grid with 3414 active cells; k = 10 nearest neighbours, including self-neighbours; row-standardized weights; conditional permutations; and Benjamini–Hochberg adjustment. The main analysis used 999 permutations; additional sensitivity analyses used 199 permutations. Global Moran’s I ranged from approximately 0.461 to 0.524 (permutation p = 0.001). Local Moran’s I produced no significant clusters after adjustment, whereas Gi* identified a small number of local hotspots. Across the complete years 2015–2025, no cell was identified as a hotspot in at least 50% of years; temporal classes comprised 18 occasional, 7 consecutive, 4 sporadic, 1 new, and 3384 cells, with no pattern detected. Leakage-free random forest regression achieved MAE = 0.552, RMSE = 2.230, RMSLE = 0.285, and R² = 0.982 in the temporal test, but for pooled spatial block cross-validation, these rates declined to MAE = 0.797, RMSE = 10.423, and R² = 0.661. A high-count classifier using the upper quintile of positive monthly cell counts achieved F1 = 0.883, ROC–AUC = 0.998, and PR–AUC = 0.966 in the temporal test. The contribution lies in the dataset, the understudied territory, and the methodological integration rather than in algorithmic novelty. Full article

Drinking-water quality risk in Colombia is monitored through a single municipal indicator, the Water Quality Risk Index (IRCA), yet its spatial structure has not been examined. This study analyzes the 87 municipalities of the department of Santander between 2007 and 2020 (1202 municipality-year observations from SIVICAP/INS) using exploratory spatial data analysis. Global Moran’s I is estimated annually and by period, together with local indicators of spatial association (LISA), the Getis-Ord Gi* statistic, and a cluster-persistence test based on a binomial contrast with false discovery rate correction. Spatial dependence was positive and significant before 2014 (I = 0.113; p = 0.036) and became undetectable afterward (I = 0.046; p = 0.183); the share of IRCA variance attributable to spatial structure fell from 5.0% to 1.0%. The change in IRCA showed no detectable spatial clustering (I = 0.044; p = 0.198), suggesting, though not confirming, that the improvement did not spread through geographic contiguity. Cluster persistence reveals an asymmetry: eight municipalities maintain statistically significant low-risk cluster status after multiple-testing correction, led by Páramo (9 of 14 years) and San Gil (8 of 14), while no high-risk cluster survives that correction. The mean distance among the ten lowest-risk municipalities (≈60 km) remains below the departmental average (82.1 km) in both periods, whereas that among the ten highest-risk municipalities increases from 70.6 to 90.2 km. Finally, 2020 shows significant negative autocorrelation (I = −0.114; p = 0.040), coinciding with an increase in municipalities reporting an IRCA of zero (31 of 87, versus 1 in 2018), which suggests a disruption in the surveillance system rather than an actual change in water quality. Water safety consolidates territorially, whereas high risk does not form stable spatial structures. Full article

Accommodation establishments constitute a core component of tourism infrastructure, and their location choices directly affect water resource utilization, land pressure, and the spatial equilibrium of tourism development—issues that are particularly acute in vast arid regions. Yet the spatial organization of accommodation supply across extensive drylands characterized by fragmented oasis distribution, and the reasons why standard and non-standard accommodation follow divergent location logics, remain poorly understood. This study addresses three questions: (1) How are nine accommodation categories, differentiated by type and quality, distributed across Xinjiang? (2) Do directional spatial associations exist among categories that are consistent with hierarchical, path-dependent development? (3) Which factors drive these patterns, and do their effects exhibit the nonlinearity and threshold behavior predicted by location theory? Drawing on 12,073 accommodation establishments from the Ctrip platform, we construct a staged analytical framework in which each technique answers a specific question: the nearest-neighbor index and standard deviational ellipse characterize global patterns; kernel density estimation and OPTICS clustering identify local agglomerations; directional local co-location quotients measure asymmetric spatial associations; and XGBoost–SHAP isolates nonlinear drivers and threshold effects. Results reveal a highly concentrated “single-core, multi-center” structure anchored by Urumqi, Yining, and Kashgar, with rapid expansion toward the Ili Valley, Kashgar, and Altay since 2019. Standard accommodation tracks urban centrality and transport nodes, while non-standard accommodation tracks tourism resource endowments, consistent with location-theoretic expectations. Directional co-location analysis reveals hierarchical spatial associations among categories, and driving factors exhibit pronounced nonlinear threshold effects. From a sustainability perspective, the identified thresholds—elevation (1360 m), water-body proximity, and distance to rural tourism demonstration sites (3 km)—constitute quantifiable, spatially explicit sustainability indicators that can be incorporated into planning tools to monitor and steer accommodation development away from ecologically sensitive zones. Global Moran’s I diagnostics of model residuals (reduction of 83–99.7%) suggest that these findings are unlikely to be artifacts of spatial autocorrelation; this diagnostic, however, complements rather than replaces spatially blocked validation. The study contributes category-differentiated, spatially directed evidence for policies balancing tourism expansion against water security and ecosystem integrity, serving sustainable tourism development in arid-region destinations. Full article

The spatial organization of public service facilities within transit-oriented development TOD blocks is closely related to residents’ daily travel conditions and travel-related carbon emissions. This study examined all 172 operating metro station areas in Qingdao using public service facility POIs, buildings, pedestrian road network and population data, field observations, and a resident travel survey. K-means clustering, spatial syntax analysis, Global Moran’s I, spatial regression, FDR-adjusted Pearson correlation analysis, and scenario simulation were jointly applied. Four facility layout types were identified: Spatially Balanced Type, Main-Road-Concentrated Type, Point-Concentrated Type, and Scattered-and-Disordered Type. The survey included 240 valid respondents distributed across all 41 station areas along Qingdao Metro Line 1. The Spatially Balanced Type had the lowest mean weekly per capita travel carbon emissions, followed by the Main-Road-Concentrated Type, whereas the Point-Concentrated and Scattered-and-Disordered types had similarly higher emission levels. The density and accessibility of Commercial and Entertainment facilities, Medical and Health facilities, and total facilities remained negatively associated with travel carbon emissions after FDR correction. Spatial syntax analysis showed that higher road network integration and connectivity were associated with stronger facility agglomeration. Facility density, road density, and population density exhibited significant positive network-based spatial autocorrelation, and the spatial error model provided the best fit, identifying positive associations of facility density with road density and population density. Based on these findings and field observations, three differentiated renewal pathways—node embedding, proximity coordination, and intensive integration—were proposed. Under the specified scenario, a 20% increase in facilities was associated with modeled reductions in aggregate weekly carbon emissions of 32.3% in Li Village, 28.1% in the University of Petroleum station area, and 33.0% in Jinggangshan Road. These results suggest that improving overall facility coverage may support lower-carbon travel across different facility layout contexts. This study connects facility layout typology, spatial structure, travel carbon emission associations, and differentiated renewal strategies at the TOD-block scale. Full article

To understand the epidemiological characteristics and patterns of mushroom poisoning in Zhejiang Province from 2012 to 2023, and to overcome the limitations of previous descriptive studies in precise early warning and spatial identification, this study explored the feasibility of identifying spatial distribution characteristics and high-risk spatiotemporal clusters. First, descriptive epidemiological analysis was conducted on 2276 cases from the Foodborne Disease Case Surveillance System and 408 outbreaks from the Foodborne Disease Outbreak Surveillance System reported over the 12-year period to clarify the basic characteristics and trends of poisoning. Subsequently, spatial autocorrelation analysis (Moran’s I) was employed to reveal spatial dependence and clustering patterns. Finally, spatiotemporal scan statistics (SatScan) were used to precisely identify high-risk spatiotemporal clusters, systematically analyzing the spatiotemporal distribution and clustering patterns of mushroom poisoning cases. The results showed a distinct summer–autumn seasonal peak (June–October), attributed to the subtropical monsoon climate with high temperatures and abundant rainfall, which is conducive to mushroom growth. Farmers were the most affected population (47.93%), and homes were the primary poisoning locations (71.7%), reflecting widespread foraging habits and insufficient risk awareness in rural areas. Chlorophyllum molybdites (36.27%) and Russula japonica (10.05%) were the dominant poisoning mushroom species, with gastrointestinal symptoms being the predominant clinical manifestation (84.07%). Spatial analysis revealed significant spatiotemporal clustering of mushroom poisoning in Zhejiang Province. The global Moran’s I index showed significant positive autocorrelation in some years (p < 0.05), with local hotspots mainly distributed in western Zhejiang counties. This pattern is driven by a dual model of environmental suitability and behavioral risk, resulting from the high forest coverage and humid climate of the western Zhejiang mountainous areas providing suitable habitats, combined with long-standing foraging habits among local residents. Retrospective spatiotemporal scanning identified high-risk clusters for each year from 2018 to 2023, with the Lishui area in 2023 being the most significant cluster (Relative Risk (RR) = 15.44, Log-Likelihood Ratio (LLR) = 114.49). The results confirm that mushroom poisoning in Zhejiang Province exhibits a stable and identifiable spatiotemporal clustering pattern, providing a quantitative basis for precise health education and targeted prevention and control in high-risk counties of western Zhejiang during June–October, thereby shifting the approach from passive reporting to targeted intervention. Full article

Urban expansion is often tracked with annual land-cover data, yet year-to-year classification noise can masquerade as persistent urban growth. Previous work has usually treated detection, morphology, spatial structure, driver analysis, and forward-looking modelling separately. Here we follow urban growth in Tehran County from 2016 to 2025 within one linked workflow and then extend the analysis to a 2025–2030 growth-pressure screening under static covariates. Annual 10 m built-up composites from Dynamic World were passed through a temporal stability filter to retain persistent change. Stable growth was classified into four regimes, tested for clustering and interaction scale, and examined using a Spatial Durbin Model and multiscale geographically weighted regression. A two-stage machine-learning branch then produced a ranked growth-pressure surface. In this study, growth-pressure screening means identifying where recent spatial conditions are most compatible with continued growth under unchanged covariates; it is intended for relative spatial ranking under stated assumptions rather than deterministic estimation of future urbanization. Stable new built-up area totaled 107.64 km², most of it ribbon growth (57.60%) and edge expansion (32.77%). A stratified local validation of 300 samples returned a weighted overall accuracy of 97.30%, and a 27-scenario threshold test retained ribbon growth as the largest regime and edge expansion as the second largest in every case. Clustering was significant (Global Moran’s I = 0.326), with a dominant interaction range of about 8–10 km. Historical backtesting showed strong discrimination and ranking (ROC AUC = 0.979; PR AUC = 0.984; Spearman ρ = 0.902), while exact growth-magnitude performance was more moderate (R² = 0.341). The growth-pressure surface is therefore more useful for hotspot identification and relative ranking than for estimating exact future growth magnitude. Full article

Urban low-carbon transition is increasingly shaped by the interaction between digital infrastructure, intelligent applications, land-space allocation, and regional governance. Existing studies have mainly examined whether the digital economy or smart-city development can reduce emissions, but less attention has been paid to the coordination between digitalization and intelligentization, the carbon cost of digital infrastructure, and the spatial consequences of local gains. This research defines digital–intelligent integration as the coupling coordination between digitalization and intelligentization subsystems. Using panel data for 39 prefecture-level cities in the Middle Reaches of the Yellow River Urban Agglomeration from 2013 to 2022, it applies Global Moran’s I, a spatial Durbin model, partial-derivative effect decomposition, alternative spatial weight matrices, alternative dependent variable tests, and multidimensional heterogeneity analysis. The own-city coefficient of digital–intelligent integration in the carbon-efficiency model is positive (0.0282, p < 0.05), whereas the spatial-equilibrium direct effect is statistically insignificant. These quantities are not short- and long-run estimates: the former is a conditional model coefficient, while the latter incorporates spatial feedback. The indirect effect on neighboring carbon efficiency is negative and remains negative under contiguity, economic-distance, and geo-economic nested matrices. Under an otherwise identical fixed-effects specification, digital–intelligent integration lowers local construction-land carbon intensity but raises neighboring intensity. The structural estimates further show that local conversion is weaker in industrially and energy-intensive cities. Digital–intelligent integration should therefore be interpreted as a governance capacity rather than a net-carbon technology; its regional effect depends on industrial lock-in, infrastructure-energy demand, and cross-city responsibility sharing. Full article

This study employs panel data from 11 provinces and municipalities along the Yangtze River Economic Belt spanning 2010–2021. It measures green transport efficiency (GTE) using principal component analysis (PCA) and the undesirable Super-SBM model while constructing an economic high-quality development index (HQEDI) through an entropy-weighted CRITIC approach. The study combines coupling coordination degree modeling with spatial autocorrelation analysis (Global Moran’s I, LISA, hotspot/coldspot detection) to empirically investigate their synergistic evolution mechanism. The findings indicate the following: (1) Multidimensional policy combinations exhibit a nonlinear threshold effect on enhancing green transport efficiency, with efficiency significantly rebounding post-2015 as low-carbon policies deepened. (2) High-quality economic development displays a dual-stage ‘convergence-divergence’ pattern, where downstream regions lead in HQEDI but mid- and upstream regions show faster growth in coordination and green dimensions. (3) The coupling coordination degree exhibits pronounced spatial spillover effects, with the global Moran’s I mean reaching 0.485. High-value clusters form in downstream regions, while upstream areas predominantly exhibit low-value clusters, revealing an ‘east-high, west-low’ regional differentiation pattern. (4) The gradient divergence mechanism stems from heterogeneity in infrastructure density, industrial structure, and policy responsiveness elasticity. Accordingly, it is recommended to establish a multi-level governance mechanism to dismantle administrative barriers and to construct a tripartite policy package integrating ‘digital transport, ecological compensation, and industrial radiation’ to advance coordinated basin development. Full article

Urban flooding is shaped by urban morphology, but the inferred relationships can change with the spatial units used to represent flooding and urban form. Existing studies often report results for a selected spatial configuration, leaving unclear whether predictive performance and identified dominant predictors remain robust when analytical scale, grid placement, and spatially separated validation are varied. Using the 22 May 2020 Guangzhou storm as an event-specific case, this study evaluates the robustness of multi-scale morphology–flood associations to these spatial analytical choices. The analysis integrated 119 unique official waterlogging locations with 67 geocoded social-media locations. After removing four cross-source matches within 100 m, 182 unique observations were used to construct a kernel density response surface and examine 1–5 km analytical grids. Spatial autocorrelation, repeated nested geographic cross-validation of XGBoost, out-of-fold SHAP attribution, and accumulated local effects (ALEs) were used to quantify scale-dependent patterns. Global Moran’s I increased from 0.125 at 1 km to 0.524 at 4 km and decreased to 0.479 at 5 km (all permutation p < 0.001). Mean spatially validated R² ranged from 0.483 to 0.610, with the highest R² and lowest RMSE at 4 km, although residual spatial autocorrelation remained. Road density was the largest individual SHAP contributor at every scale (35.54–40.89%). ALE indicated broad positive associations for road density, building density, and impervious surface ratio and a negative association for elevation, without supporting universal sharp thresholds. These event-specific results show that analytical scale and grid placement should be reported explicitly when morphology-based evidence is used for flood screening and climate-resilient planning. Full article

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

Low-carbon sustainable development requires a better understanding of how urban land expansion reshapes carbon emission patterns within and across cities. Impervious surface expansion is a visible land use expression of urbanization and a key carrier of energy consumption, industrial activity, and ecological loss. Yet, its cross-city effects on carbon emissions remain insufficiently understood, particularly in highly integrated urban agglomerations. Using a balanced panel of 41 cities in the Yangtze River Delta (YRD), China, from 2008 to 2022, this study combines EDGAR gridded CO₂ emissions, annual land cover data, global and local spatial autocorrelation analysis, and a two-way fixed-effects Spatial Durbin Model (SDM) to examine the local and spillover effects of impervious surface expansion. The results show that impervious surfaces and carbon emissions both evolved from core agglomeration toward peripheral diffusion, while carbon emissions maintained significant positive spatial autocorrelation, with Moran’s I remaining positive and significant throughout the study period. SDM estimates indicate that local impervious surface expansion significantly increases local carbon emissions, whereas the estimated indirect effect on neighboring cities is significantly negative. Effect decomposition confirms a positive direct effect and a negative indirect effect under both inverse-distance and contiguity weight matrices. A rolling-window analysis further shows that the negative spillover effect strengthened over time. These findings demonstrate that urban land hardening should be evaluated not only as a local emission driver but also as a spatially embedded process within regional carbon governance. By linking impervious surface expansion with spatial carbon emission interactions, this study contributes to sustainability research by providing empirical evidence for sustainable urban agglomeration development. Full article

Background: Timely hepatitis B vaccine birth-dose (HepBV-BD) vaccination is a cornerstone strategy for preventing mother-to-child transmission of hepatitis B virus (HBV). Although hepatitis B vaccination coverage in China has improved substantially, evidence regarding long-term district-level spatiotemporal inequalities in timely birth-dose vaccination remains limited, particularly in western China. This study assessed spatiotemporal disparities in hepatitis B vaccination coverage across Chongqing, China, from 2016 to 2025. Methods: This ecological descriptive study used district- and county-level vaccination surveillance data from the Chongqing Immunization Information Management System. Temporal trends in HepBV-BD, timely birth-dose (TBD), HepBV2, and HepBV3 coverage were analyzed. Regional disparities, interregional inequalities, and spatial clustering were assessed using Global Moran’s I and Local Indicators of Spatial Association (LISA). Results: All vaccination indicators improved substantially during the study period. TBD coverage increased from 81.9% to 95.4%, whereas HepBV3 coverage increased from 83.7% to 98.7%. TBD coverage was strongly positively associated with HepBV3 coverage(r = 0.95, p < 0.001). Mountainous ethnic minority regions consistently exhibited the lowest TBD coverage despite marked improvement over time. Absolute interregional inequality decreased from 30.8 to 8.4 percentage points. Although Global Moran’s I was not statistically significant, LISA analysis identified persistent localized low-coverage clusters in southeastern mountainous regions and emerging high–high clusters in northern Chongqing. Conclusions: Hepatitis B vaccination coverage in Chongqing improved markedly between 2016 and 2025, accompanied by declining geographic inequality. Nevertheless, persistent spatial inequities in timely birth-dose vaccination remained in mountainous ethnic minority areas. Continued monitoring of spatial inequalities, together with strengthened primary healthcare capacity, culturally tailored health education, and equitable resource allocation, may help support geographically targeted immunization strategies and accelerate progress toward the WHO goal of eliminating viral hepatitis as a public health threat by 2030. Full article

Türkiye is one of the biologically richest regions of the temperate zone, yet the national-scale spatial structure of its biodiversity remains insufficiently quantified using formal spatial statistical approaches. In this study, province-level biodiversity indicators derived from the Noah’s Ark National Biodiversity Database were analysed to assess whether species richness and endemism exhibit non-random spatial patterns across the country. Global Moran’s I, Anselin Local Moran’s I, and Getis–Ord Gi* statistics were applied within a GIS framework to identify spatial clustering and biodiversity hotspots. The results indicate statistically significant positive spatial autocorrelation for seven of the nine biodiversity indicators, with the strongest values observed for the rate of endemism and the number of endemic taxa. Vascular plant richness shows marginal evidence of clustering at the 90% confidence level, whereas bird richness does not differ significantly from spatial randomness. High-value clusters are concentrated along the Mediterranean belt, the Eastern Black Sea region, and the Taurus mountain system, whereas comparatively lower biodiversity values occur in several inland provinces. These spatial patterns are interpreted in relation to Türkiye’s pronounced climatic, topographic, and land-use heterogeneity. Full article

The coexistence of undernutrition and excess weight is an important child public health challenge. This study described temporal trends and departmental spatial configurations of wasting, stunting, overweight, and obesity among children under five years of age in Peru during 2014–2024. Analyses used anthropometric assessment records from the Nutritional Status Information System (SIEN), collected in public healthcare facilities and aggregated by department and year. Annual Percentage Change summarized average temporal trends, while observed prevalence maps, Global Moran’s I, Local Indicators of Spatial Association (LISA), and a median-based bivariate classification were applied independently to 2014, 2019, and 2024. Queen contiguity was the primary spatial specification, along with KNN-4 sensitivity analysis. Wasting and obesity increased on average, stunting decreased, and overweight showed no significant long-term trend. Global autocorrelation was most consistent for wasting, whereas findings for excess-weight indicators were sensitive to the weights matrix. LISA identified local spatial classifications at nominal p < 0.05; however, these findings were exploratory because no multiple-testing correction was applied. The bivariate classification identified two, four, and three departments with concurrent high relative undernutrition and excess-weight scores in 2014, 2019, and 2024, respectively. These record-based findings show territorial heterogeneity within SIEN but do not represent population prevalence, causal geographic effects, or an integrated spatiotemporal process. Full article