With global aging, the burden of tuberculosis (TB) among older adults escalates, yet spatial studies on this group are scarce. In Chongqing, where 18.87% of the population are aged 65 years and older and TB burden is high, controlling older adult TB remains a major challenge. This study analyzed the spatiotemporal patterns of TB among adults aged 65 years and older in Chongqing, China, to inform local prevention and control strategies. The study data were obtained from the Tuberculosis Information Management System of China. Global and local spatial autocorrelation analyses were conducted using ArcGIS (version 10.7) to identify high-risk spatial clusters and visualize their distribution. Spatiotemporal scan statistics were performed using SaTScan (version 10.3.2) to detect clusters of TB cases among the older adult population. Statistical significance was set at P<.05. The average annual incidence of TB among older adults in Chongqing was 69.59 per 100,000 population, with peaks occurring in spring and summer. The global Moran I ranged from 0.618 to 0.756 (P<.001 in all cases), indicating significant clustering. Persistent high-risk areas were identified in the northeastern and southeastern parts of Chongqing. Spatiotemporal scan statistics detected 1 most likely cluster (relative risk=3.52, 95% CI 3.37-3.68; log-likelihood ratio=1017.43; P<.001) and 3 secondary clusters. Significant seasonal patterns of TB among older adults were observed in Chongqing. High-risk areas were predominantly concentrated in the northeastern and southeastern parts of the municipality. More targeted public health interventions are imperative.

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy.

In this study, global spatial autocorrelation, local spatial autocorrelation, Spearman correlation analysis, gray correlation analysis, entropy weight method, and the gravity model were used to analyze the spatiotemporal variation and environment-urban-economy-associated factors of air quality of 31 provinces in China during 2015~2022. From 2015 to 2022, the Air Quality Index (AQI) exhibited a downward trend in 30 out of 31 Chinese provinces, with the exception of Shaanxi Province. Concurrently, the annual average concentrations of PM2.5, PM10, SO2, NO2, and CO declined across the study period. High-high clusters and low-high outliers were observed in northern China, whereas low-low clusters and high-low outliers were distributed in southern China. Twelve provinces (38.7%) showed positive correlation (0.095~0.95), 18 provinces (58.1%) showed negative correlation (−0.76~0.095), and only Anhui showed no correlation between AQI and O3. The comprehensive AQI quality presented a dual-core model in Sichuan (in the southwest) and Henan (in the central part) of China, while the comprehensive AQI improvement rate presented a single-core model in Jiangsu in the east of China. The gravity models incorporating AQI and GDP revealed that both air quality and economic performance improved over the study period. The spatial pattern of pollution evolved from a multi-core structure to a non-core structure, whereas the pattern of economic growth transitioned from a non-core structure to a dual-core structure, with the Beijing-Tianjin-Hebei region and the Yangtze River Delta emerging as the primary urban agglomerations.

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 108 CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance.

The study conducts an assessment of the spatial distribution of cardiovascular diseases (CVD) in Kenya by integrating spatial modeling techniques and spatial autocorrelation measures. CVDs, which refer to disorders of the heart and blood vessels, have surpassed communicable diseases as the leading cause of morbidity and mortality worldwide, posing a critical public health concern, especially in low- and middle-income countries (LMICs) where resources remain limited. A growing body of global evidence has revealed marked geographical disparities in CVD incidence, prompting investigations into small-area spatial distribution patterns. This study employed both global and local spatial autocorrelation measures to analyze CVD prevalence across Kenyan counties. The Global Moran’s I statistic was used to assess the overall degree of spatial clustering, while the Local Moran’s I identified significant clusters of high and low prevalence, alongside spatial outliers. Additionally, the Getis-Ord Gi* statistic was applied to detect statistically significant hotspots and coldspots, revealing important spatial patterns in disease prevalence. Spatial regression models were compared using the Lagrange Multiplier (LM) test, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) for model selection. The Spatial Lag Model (SLM) demonstrated superior performance over the Spatial Error Model (SEM) and the Spatial Durbin Model (SDM), achieving a Rao’s score (RSlag) of 16.449 and an adjusted score (adjRSlag) of 12.181, both statistically significant at the 5% level. The SLM also recorded the lowest AIC and BIC values at -380.09 and -361.80, respectively, confirming its suitability in capturing spatial dependence in the data. The findings revealed significant spatial clustering of CVD prevalence, with distinct high-risk and low-risk regions across the country. High body mass index (HBMI), tobacco use, and poor dietary habits emerged as major risk factors driving CVD prevalence, while urbanization and economic development were associated with lower disease burdens. The study highlights the importance of incorporating spatial analysis in public health planning to inform targeted interventions, optimize resource allocation, and enhance community health education campaigns aimed at promoting heart-healthy lifestyles.

Against the backdrop of increasing resource and environmental constraints, improving the land use efficiency of state-owned agricultural land is of great significance for promoting sustainable agricultural development. This study measures the land use efficiency of state-owned agricultural land across 29 provinces in China based on data from the China State Farms Statistical Yearbook (2019–2023). The super-efficiency slack-based measure model (Super-SBM), incorporating both desirable and undesirable outputs, is employed, and global and local spatial autocorrelation methods are further applied to analyze the spatiotemporal evolution of land use efficiency. The results indicate the following: (1) from 2019 to 2023, the overall land use efficiency of state-owned agricultural land in China remained below or slightly above the efficiency frontier, exhibiting a fluctuating trend characterized by an initial increase followed by a decline; (2) significant regional disparities exist, with high-efficiency areas mainly concentrated in Northeast China and the eastern coastal regions, while low-efficiency areas are primarily distributed in western regions and parts of central China; (3) spatial autocorrelation analysis reveals that land use efficiency shows an increasingly pronounced spatial clustering pattern at the provincial scale. After 2022, high–high and low–low clustering became more evident, although a certain degree of spatial heterogeneity still persists overall. These findings provide empirical evidence for understanding the spatial differentiation and evolutionary patterns of the land use efficiency of state-owned agricultural land and offer useful insights for optimizing land resource allocation and management.

Bacillary dysentery remains a common intestinal infectious disease in China. With economic development and improvements in sanitation, the incidence rate of bacillary dysentery has changed substantially across counties in Shaanxi Province. This study aims to elucidate its epidemiological characteristics and spatio-temporal dynamics to inform prevention and control strategies. Surveillance data of confirmed bacillary dysentery cases in Shaanxi Province from 2005 to 2024 were obtained from the China Information System for Disease Control and Prevention. We analyzed epidemiological features across temporal, regional, and population distributions. Long-term incidence trends were assessed using Joinpoint regression, while spatial autocorrelation and spatio-temporal scan statistics were employed to identify clustering patterns at the county level. A total of 156,656 cases of bacillary dysentery were reported in Shaanxi Province from 2005 to 2024. The reported incidence decreased from 65.30 per 100,000 in 2005 to 3.33 per 100,000 in 2024. A unimodal seasonal pattern was observed, with 76.09% of cases occurring between May and October. Children under five years of age were the most susceptible group, particularly infants under one. The incidence was higher in males than in females. The majority of cases were farmers (27.08%), home-care children (22.35%), and students (17.71%). Joinpoint regression revealed a significant overall decreasing trend (Average Annual Percent Change, AAPC = -15.0%; 95% CI: -18.4%, -11.4%; p < 0.001). Geographically, high-incidence counties were primarily located in the northern and central-western regions before 2014. After 2015, the number of high-incidence counties markedly decreased. By 2024, the incidence in 84.07% (95/113) of counties had fallen below 5 per 100,000. Global spatial autocorrelation analysis indicated significant clustered distributions annually. Local spatial autocorrelation identified 174 "High-High" clusters. Spatio-temporal scan statistics showed that primary clusters were located in the northern and central-western regions before 2010, shifting to the central region thereafter. Both the incidence and the spatial clustering of bacillary dysentery have decreased significantly in Shaanxi Province. However, targeted interventions during the summer and autumn seasons, focusing on children under five and populations in the central region, remain crucial for future control efforts.

In sub-Saharan countries, where a large number of populations depend on unsafe water, household water treatment is the recommended means to improve and maintain the safety and quality of water. Boiling, adding bleach, filtration, solar disinfection, straining through cloth, and settling are among well-known treatment methods. However, the practice in the region is very low. The current study is intended to assess the spatial analysis and scope estimation of households’ water treatment methods in sub-Saharan African countries. The Demographic and Health Survey (DHS) data were collected from 2012 to 2024 in 34 sub-Saharan countries, encompassing 500,845 households and 20,492 clusters. Global spatial autocorrelation was performed to analyze whether the pattern of household water treatment is clustered, dispersed, or random across the study areas. Once a positive global autocorrelation was confirmed, a local spatial autocorrelation analysis (Getis-Ord Gi* statistics) was employed to detect local clusters. ArcGIS version 10.7 was used to map the clusters, and Kulldorff SaTScan version 10.0.2 software, using the Bernoulli model, was used for spatial scan statistical tests. The geo-statistical ordinary Kriging spatial interpolation technique was used to predict unsampled areas based on sampled clusters. The overall prevalence of household water treatment across sub-Saharan Africa between 2012 and 2024 was 20% (95% CI: 19.7%–20.0%). Reported treatment methods included boiling (9.04%), bleaching (6.98%), filtration (0.90%), solar disinfection (0.07%), straining through cloth (2.54%), settling (1.59%), and other methods (0.62%). Regarding treatment adequacy, 16.98% of households used adequate methods, 4.75% used inadequate methods, and 0.99% used both. Spatial analysis showed clear clustering of household water treatment practices, with hotspots identified in Madagascar, Uganda, Rwanda, Kenya, Tanzania, Burundi, Malawi, Zambia, Burkina Faso, Mali, Liberia, and Sierra Leone. SaTScan cluster analysis identified 78 significant windows across the region. The low prevalence of household water treatment across sub-Saharan Africa underscores a substantial barrier to achieving Sustainable Development Goal 6.1. To address this, efforts should focus on improving access to affordable and context-appropriate household water treatment technologies, implementing community-level health education and behavior change programs to promote correct water treatment practices, and targeting hotspot regions identified in the spatial analysis with coordinated interventions from governments, NGOs, and partners. These strategies are essential to reduce waterborne diseases and accelerate progress toward universal access to safe drinking water.

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development.

Abstract Ambient dust exposure has been associated with several adverse health outcomes. Unlike ambient air pollution, the vulnerability of subpopulations to ambient dust has not yet been explored. As the driest inhabited continent, Australia provides a natural laboratory for studying large-scale dust exposure. This study aimed to examine environmental injustice in the ambient dust of ⩽10 μ m particle size and vulnerability of subpopulations in Australia, which has not been studied previously. A nationwide cross-sectional design was employed by linking a highly spatially resolved census-derived composite measure of social vulnerability index (a wide range of factors measuring susceptibility, and a more complex capacity of individuals and society to cope with hazards and damage) to 2021 annual mean ambient dust concentrations in Australia. Global and local spatial autocorrelations, bivariate spatial correlations, and generalised additive logistic regression with spatial smoothing were applied to investigate geographic variation and the association between social vulnerability and ambient dust at the Australian Census’ most precise geographical unit. The results indicated geographical inequalities of social vulnerability and ambient dust exposure, with a positive association and more elevated in urban areas than in rural areas. Those with high social vulnerability were 13% more likely to reside in areas with the highest dust exposure (OR 1.13, 95% CI: 1.03, 1.23). High dust exposure was especially elevated in urban areas (OR 1.74, 95% CI: 1.42, 2.13) and areas with relatively high cultural and minority vulnerability (OR 3.55, 95% CI: 3.20, 3.94) and housing vulnerability (OR 2.63, 95% CI: 2.38, 2.90). Social vulnerability is associated with greater exposure to ambient dust with identified hotspots, particularly in urban areas and communities with elevated cultural and housing vulnerability. These areas could be prioritised for policies and interventions to reduce the health burden of ambient dust, as initial steps to addressing environmental injustice in Australia.

This study examines a panel of 268 Chinese cities during 2013–2023, employing patent applications in Low-carbon technologies (LCTs) as a proxy indicator for the level of LCTs. The spatiotemporal patterns of LCTs are characterized through integrated Standard Deviation Ellipses and Spatial Autocorrelation Techniques, while their driving mechanisms are investigated using Geographic Detectors. The key findings identified in this study are: (1) The advancement of LCTs exhibits a swift increasing trajectory; (2) The eastern region and provincial capital cities have relatively high levels of LCT, while western cities have lower levels. The overall trajectory of the gravity center moves southwest, and typical global and local spatial autocorrelation characteristics are observed in the cities; (3) Infrastructure construction and government R&amp;D funding significantly drive LCTs, while environmental regulations show limited predictive power.

Under a human-centered approach, accurately identifying the spatial patterns of urban vitality and revealing the mechanisms through which the built environment affects it can scientifically guide the organic cultivation of urban vitality. In light of this, the main urban area of Yantai City is taken as a case study, utilizing multi-source geographic big data to conduct both theoretical and empirical research. An index system for the urban built environment is established based on four dimensions: human perception, functional, accessibility, and building form. Advanced methods, including Deep Fully Convolutional Neural Networks (SegNet), Random Forest Regression (RFR), and Spatial Lag Regression (SLR), are employed to explore the impact of the built environment on urban vitality. The research findings indicate that: (1) Urban vitality presents a composite spatial structure that embodies both “multi-center” and “clustered” characteristics, exhibiting two primary types of local spatial autocorrelation: “high-high” clustering and “low-low” clustering. (2) The disparities in urban vitality reflect an imbalance in the distribution of functional, accessibility, building form, and human perception, with functional playing a more critical role in nighttime and daytime urban vitality than other dimensions. (3) The effects of the built environment on daytime and nighttime urban vitality show varying degrees of heterogeneity regarding significance and direction. Factors such as BPOI(Commercial Points of Interest), integration, accessibility, and vibrancy have a substantial positive impact on vitality clustering, while human perception becomes increasingly important for enhancing nighttime vitality. These results provide refined technical support for urban micro-renewal, enhancing the relevance and effectiveness of response strategies.

Under the background of the “dual carbon” strategy and regional coordinated development, the synergistic evolution of low-carbon logistics, regional economy, and ecological environment in the Yellow River Basin has become a key pathway to achieving high-quality development. Taking nine provinces (autonomous regions) within the basin as the study area, this paper constructed a coupling coordination evaluation index system for the LREES (Low-carbon Logistics–Regional Economy–Ecological Environment System), and measured the comprehensive development level of each subsystem using the entropy weight method. Based on the coupling coordination degree model, the temporal evolution of the three systems from 2010 to 2024 was systematically evaluated. In addition, global and local spatial autocorrelation models were introduced to identify spatial clustering patterns, while the obstacle degree model was used to identify key constraints at both the criterion and indicator levels. The results revealed that: the overall development level of the LREES systems steadily increased, with reduced regional disparities; the coupling coordination degree showed a trend of “fluctuating rise–gradual coordination,” with the average value increasing from 0.450 to 0.623, indicating continuously enhanced synergy; spatially, a gradient pattern of “downstream &gt; midstream &gt; upstream” emerged, accompanied by significant positive spatial autocorrelation; resource endowment and development scale were major constraints, while construction level, operational efficiency, and governance capacity were secondary. High-frequency obstacle indicators included per capita water resources, total import and export volume, and urban sewage treatment capacity. These findings offer theoretical support and policy guidance for promoting green transformation, enhancing system synergy, and advancing coordinated regional development in the Yellow River Basin.

Quantification of marine cooling effect and driving mechanism along Beibu Gulf coastal areas.

Ecological management zoning is a crucial tool for land use governance and spatial optimization， serving as a foundational strategy for implementing differentiated regional management and protection. It plays a key role in mitigating human-environment conflicts and fostering the sustainable development of both ecological and socio-economic systems. Focusing on the Three Gorges Reservoir area， an essential ecological corridor in the middle Yangtze River Basin， this study quantitatively analyzes the supply and demand of ecosystem services. Using a bivariate local spatial autocorrelation model and a coupling coordination model， the study explores the relationship between ecosystem service supply and demand and the intensity of land use. The study then delineates ecological management zones and simulates changes in zoning patterns under two distinct scenarios for 2030. The results show that： ① In 2020， both natural factors and human activities influenced the supply and demand of ecosystem services in the Three Gorges Reservoir area， resulting in significant spatial heterogeneity. Notably， the main urban area of Chongqing exhibited a severe imbalance in the supply-demand relationship of ecosystem services. ② Based on the coupling coordination analysis of ecosystem service supply-demand and land use intensity， the Three Gorges Reservoir area was categorized into four ecological zones： the ecological coordinated development zone， ecological potential development zone， ecological imbalance risk zone， and ecological strict control zone. This zoning approach aids in accurately identifying the ecological status of different regions and formulating targeted protection and development strategies， thereby promoting the sustainable integration of ecological and economic development. ③ Scenario simulations for 2030 indicated an expansion of the ecological coordinated development zone in both scenarios， particularly in certain areas of the urban core of Chongqing. This suggests that urban areas are not entirely dominated by human-environment conflicts and that regions of ecological coupling coordination still exist. Future research should focus on enhancing ecological coordination within urban areas to further optimize the relationship between ecological conservation and human activities. These findings provide valuable insights for maintaining ecological balance and advancing sustainable development in the Three Gorges Reservoir area.

BackgroundBrucellosis, a neglected zoonotic disease as defined by the World Health Organization (WHO), represents a substantial burden, causing both severe human morbidity and significant economic losses to the livestock industry. In recent years, the Ningxia Hui Autonomous Region (Ningxia) in northwest China has emerged as a region with persistently high endemicity of human brucellosis. An in-depth analysis of the epidemic’s prevalence and spatiotemporal evolution in this region is essential to inform the optimization of targeted prevention and control measures.MethodsData on all human brucellosis cases from 2010 to 2024 were collected from the China Information System for Disease Control and Prevention. Temporal trends, global and local spatial autocorrelation, and spatial scan statistics were used to explore temporal patterns, regional clustering, and high-risk areas.ResultsA total of 35 665 human brucellosis cases were reported in Ningxia, with no associated deaths, during the study period. The average annual incidence rates was 35.08/100,000, ranging from 3.31 to 84.80 per 100,000. Two incidence peaks were observed: the first in 2015 (43.66/100,000) and the second in 2022 (84.80/100,000). The human brucellosis incidence rates showed that overall increasing trend from 2010 to 2024 (average annual percentage change [AAPC]=18.31%, but this was not statistically significant (t = 1.63, P = 0.102). Male accounted for 71.01% (25 325 cases) of all cases, 2.45 times higher than females (28.99%, 10 340 cases). Cases were reported in all 22 counties of Ningxia. The top five counties with the highest average annual incidence were: Yanchi County (184.79/100,000), Tongxin County (75.91/100,000), Zhongning County (46.47/100,000), Xiji County (46.15/100,000) and Yuanzhou District (45.30/100,000), while Xingqing District had the lowest rate (5.60/100,000). The top five counties with the fastest AAPC were: Huinong District (AAPC = 140.46%), Qingtongxia City (AAPC = 127.19%), Pengyang County (AAPC = 111.19%), Haiyuan County (AAPC = 107.97%) and Lingwu County (AAPC = 102.04%). Global spatial autocorrelation analysis revealed spatial clustering of human brucellosis incidence between 2022–2024 (Moran’s I 0.266, p = 0.033; 0.394, p < 0.001; 0.353,p = 0.002, respectively). Local spatial autocorrelation identified “high-high” clusters primarily in Zhongning County, Tongxin County and Lingwu County. Additionally, spatial scanning analysis detected four spatial clusters, including one most likely cluster (LLR = 6474.66, RR = 3.71, P ( 0.001) and three secondary clusters. The primary cluster center was located at Jingyuan County (38.10°N, 106.34°E), with a cluster radius of 271.44 km.ConclusionOur findings reveal a substantial burden of human brucellosis in Ningxia. The disease exhibits distinct seasonality, peaking in summer and autumn. Priority interventions should include: (1) Enhanced health education and targeted behavioral interventions for middle-aged and elderly males to improve personal awareness and competency in prevention and control; and (2) Timely identification of key risk factors and implementation of tailored prevention strategies in regions experiencing either a rapid increase or persistently high incidence.

Urban blue-green spaces (BGS) are critical for mitigating heat islands, yet the nonlinear dynamics between cooling supply and demand remain underexplored. This study establishes a spatial identification-mechanistic diagnosis framework to investigate these dynamics in Hangzhou from 2016 to 2022. First, spatially explicit Urban Cooling Demand (CEDL) and Supply (CESL) indices were constructed using a Geographically Weighted Random Forest (GWRF) approach to capture local parameter sensitivity. Local Spatial Autocorrelation (LISA) was then applied to pinpoint supply-demand mismatched zones. Subsequently, LightGBM, SHAP, and Partial Dependence Plots (PDP) were integrated to diagnose the nonlinear drivers specifically within these mismatched regions. Results reveal a spatial polarization: high cooling demand areas expanded outward in dense urban cores, while high supply regions contracted inward near ecological barriers. The mismatch analysis identified dominant Low Supply-High Demand (LS-HD) zones in the city center, primarily driven by the ‘Distance to Nearest BGS’ and ‘Building Shape Index,’ with PDP revealing a critical cooling attenuation threshold at 200–300 m. Conversely, High Supply-Low Demand (HS-LD) zones in the periphery were sustained by landscape connectivity but faced increasing erosion from suburban sprawl. These findings move beyond global linear assumptions, providing data-driven, spatially targeted strategies—such as micro-park insertion in core zones and connectivity preservation in fringes—to alleviate urban heat inequity. • A “spatial identification-mechanistic diagnosis” framework integrates Geographically Weighted Random Forest (GWRF) and interpretable machine learning. • Spatiotemporal polarization intensifies: cooling demand expands outward in urban cores while supply contracts inward near ecological barriers. • ‘Distance to Nearest BGS’ and ‘Building Shape Index’ are identified as the dominant drivers of heat accumulation in Low Supply-High Demand zones. • Partial Dependence Plots reveal a critical nonlinear threshold where cooling benefits diminish rapidly beyond 200–300 m from blue-green spaces.

We analyzed the epidemiological trends and spatio-temporal distribution characteristics of varicella incidence in Baise City from 2010 to 2024 to provide a basis for its prevention and control. This study describes the epidemiological characteristics of varicella from population, temporal and spatial perspectives by using data from the National Infectious Disease Reporting System. Trend and spatial autocorrelation analyses and spatiotemporal scanning statistics were employed to determine the spatial clustering and spatiotemporal dynamics for the incidence of varicella. About 54,969 cases of varicella were reported during the study period, with an annual incidence rate of 102.30 per 100,000, and an annual percentage change of 10.05% (95% CI: 1.10% to 19.79%; P = 0.027).The male-to-female ratio was 1.23:1, with the peak incidence occurring among in the 5-9 age group and the 10-19 age group showed an upward trend in incidence. People under 20 years old had the highest incidence rates of varicella and students accounted for more than half of the cases (55.46%), and 45.19% of townships had an average annual incidence rate exceeding the city's average level. There was a Positive spatial autocorrelation was observed in the varicella incidence across different townships, with a pattern of high-value clustering. Local spatial autocorrelation analysis identified a total of 117hot spots. Spatiotemporal scanning identified five significant clusters, these included a Most likely cluster of 27 towns in the Youjiang River Valley and secondary clusters in the northwestern mountainous regions of Baise. The reported incidence rate of varicella in Baise was on the rise, exhibiting distinct spatio-temporal clustering characteristics. Hotspots were aligned with the spatio-temporal clustering zones. Strengthening prevention and control measures in local areas where clusters and hotspots of varicella occur can be an effective strategy to decrease its incidence in a city.

BackgroundVaricella has been subject to mandatory reporting to the China Information System for Disease Control and Prevention (CISDCP) by health agencies within 24 h of diagnosis since 2019. However, even if two-dose varicella vaccination has been recommended to be administered to children at 1 and 4 years of age in Hangzhou since 2014, emerging evidence of increasing breakthrough varicella cases in outbreaks challenges the present varicella vaccination schedule and its protective effect. We seek to identify hotspot areas and temporal trends of varicella at the township level in Hangzhou in the recent 6 years by using spatiotemporal analysis.MethodologyVaricella cases diagnosed by medical practitioners from 2019 to 2024, demographic data, and clinical data were extracted from CISDCP. Township-level population figures were estimated using a constant-share proportional allocation method based on the seventh census data in China. Global I statistics and the local index spatial autocorrelation (LISA) method were used to identify global autocorrelation and local autocorrelation, respectively. Retrospective spatial scan statistics were undertaken to explore potential spatiotemporal clusters of varicella. A harmonic regression model was used to quantify seasonality, and an age-specific trend was evaluated through the Cochrane-Armitage test.ResultA continuous decline in reported incidence of varicella in Hangzhou from 2019 to 2024 was observed, with 97.95 per 100,000 and 52.23 per 100,000 in 2019 and 2024, respectively. Seasonality of the bimodal peak was observed, with the first peak of varicella cases observed from May to July, whereas the second peak typically occurs from November to February of the following year. A pronounced reduction in varicella incidence among younger children (5–9) and a relatively slower decline in older pediatric and adolescent groups (10–19) were found. The spatial distribution pattern of varicella in Hangzhou at township levels was non-random, and hotspots tend to be more frequent in the suburbs than in downtown areas. A total of 34 significant varicella spatiotemporal clusters were identified by retrospective space–time scan statistics, the vast majority of which were located in suburban areas.ConclusionVaricella incidence has dramatically declined over the past 6 years. The 10–19-year-old age band exhibited a slower reduction than the 5–9-year-old age-band. Moreover, the tendency for varicella clusters to appear more frequently in suburban areas reflects disparities in varicella incidence geographically. Specific surveillance and control measures should be undertaken in high-incidence regions in Hangzhou.

Despite existing injury prevention initiatives, preventable unintentional home injuries remain a significant public health concern in Canada and are often influenced by the social determinants of health. This study identified census subdivision-area-level hotspots of unintentional home injuries resulting in hospitalizations across British Columbia (B.C.), Canada, from 2015 to 2019, and examined their relationship with sociodemographic factors. Unintentional home injury hospitalization data from B.C., Canada (2015-2019) were obtained from the Discharged Abstract Database. These data were then aggregated at the census subdivision (CSD) level and linked to the social profiles of the 2016 for B.C. Spatial autocorrelation and hotspot analysis were performed using Anselin Moran's I statistics. Age-standardized injury rates for each CSD were calculated. Exploratory regression was conducted in ArcGIS Pro to identify the most suitable combination of predictor variables, and the best-fitting model was subsequently retained for geographically weighted regression (GWR) analysis. Between 2015 and 2019, the average age-standardized rate of unintentional home injuries leading to hospitalization in B.C. was 166.6 per 100,000 population. Statistically significant local spatial autocorrelation in age-standardized unintentional home injury hospitalization rates across B.C. was observed, with high-high clusters concentrated in the Lower Mainland. Among five models tested, the most stable, featuring low multicollinearity (Max VIF = 1.19) and a significant Koenker (BP) p value (p < 0.001), included no formal education, after-tax annual household income, and low-rise apartment dwellings. The GWR model explained 77% of the variation in age-standardized unintentional home injury hospitalization rates across 428 CSDs in B.C., with injury risk increasing by 7.9% per 1% rise in population without formal education, decreasing by 1.5% per $1000 increase in after-tax annual household income, and increasing by 8% for every additional 100 low-rise apartment dwellings. These findings identify geographic hotspots of unintentional home injuries and a strong association with key sociodemographic factors, which may serve as a basis for further investigation and inform public health planning and place-based intervention strategies in high-risk communities.