The spatial overlay effect of urban waterlogging risk and land use value.

Urban waterlogging poses a severe threat to lives and property globally, making it crucial to identify the distribution of urban value and waterlogging risk. Previous research has overlooked the heterogeneity of value and risk in spatial distribution. To identify the overlay effect of urban land value and risk, this study employs the Entropy Weighting Method (EM) to assess urban value, Principal Component Analysis (PCA) to determine waterlogging risk and key areas (RK), local Moran's I (SC) to identify key areas (HK), and finally Bivariate local Moran's I (DC) to comprehensively evaluate urban value and waterlogging risk to delineate key areas (BH). The results indicate that waterlogging risk is primarily influenced by proximity to water systems (PCA coefficient: 0.567), population density (0.550), and rainfall (0.445). There is a positive correlation between urban value and waterlogging risk, with a global Moran's I of 0.536, indicating that areas with higher urban value also face greater waterlogging risk. The DC method improved identification precision, reducing the BH area by 6.42 and 3.51 km2 compared to RK and HK, accounting for 25.50 % and 15.76 % of the RK and HK identified areas, respectively. At present, rescue resources can access less than one-third of the area within 5 min, but with the DC method, during the centennial rainfall scenario, the accessibility rate within 5 min for the BH area reaches 63 %, and all BH key areas can be covered within 15 min. This study provides a new methodology for identifying key areas of waterlogging disasters and can be used to enhance urban rescue efficiency and the precision management of flood disasters.

Evaluating China's primary healthcare services' efficiency and spatial correlation: a three-stage DEA-Malmquist model.

Introduction: Enhancing the efficiency of primary healthcare services is essential for a populous and developing nation like China. This study offers a systematic analysis of the efficiency and spatial distribution of primary healthcare services in China. It elucidates the fundamental landscape and regional variances in efficiency, thereby furnishing a scientific foundation for enhancing service efficiency and fostering coordinated regional development. Methods: Employs a three-stage DEA-Malmquist model to assess the efficiency of primary healthcare services across 31 provincial units in mainland China from 2012 to 2020. Additionally, it examines the spatial correlation of efficiency distribution using the Moran Index. Results: The efficiency of primary healthcare services in China is generally suboptimal with a noticeable declining trend, highlighting significant potential for improvement in both pure technical efficiency and scale efficiency. There is a pronounced efficiency gap among provinces, yet a positive spatial correlation is evident. Regionally, efficiency ranks in the order of East > Central > West. Factors such as GDP per capita and population density positively influence efficiency enhancements, while urbanization levels and government health expenditures appear to have a detrimental impact. Discussion: The application of the three-stage DEA-Malmquist model and the Moran Index not only expands the methodological framework for researching primary healthcare service efficiency but also provides scientifically valuable insights for enhancing the efficiency of primary healthcare services in China and other developing nations.

Development of a "big data" groundwater microbial contamination index and spatial comparisons with enteric infection rates in southern Ontario.

Big data have become increasingly important for policymakers and scientists but have yet to be employed for the development of spatially specific groundwater contamination indices or protecting human and environmental health. The current study sought to develop a series of indices via analyses of three variables: Non-E. coli coliform (NEC) concentration, E. coli concentration, and the calculated NEC:E. coli concentration ratio. A large microbial water quality dataset comprising 1,104,094 samples collected from 292,638 Ontarian wells between 2010 and 2021 was used. Getis-Ord Gi* (Gi*), Local Moran's I (LMI), and space-time scanning were employed for index development based on identified cluster recurrence. Gi* and LMI identify hot and cold spots, i.e., spatially proximal subregions with similarly high or low contamination magnitudes. Indices were statistically compared with mapped well density and age-adjusted enteric infection rates (i.e., campylobacteriosis, cryptosporidiosis, giardiasis, verotoxigenic E. coli (VTEC) enteritis) at a subregional (N = 298) resolution for evaluation and final index selection. Findings suggest that index development via Gi* represented the most efficacious approach. Developed Gi* indices exhibited no correlation with well density, implying that indices are not biased by rural population density. Gi* indices exhibited positive correlations with mapped infection rates, and were particularly associated with higher bacterial (Campylobacter, VTEC) infection rates among younger sub-populations (p < 0.05). Conversely, no association was found between developed indices and giardiasis rates, an infection not typically associated with private groundwater contamination. Findings suggest that a notable proportion of bacterial infections are associated with groundwater and that the developed Gi* index represents an appropriate spatiotemporal reflection of long-term groundwater quality. Bacterial infection correlations with the NEC:E. coli ratio index (p < 0.001) were markedly different compared to correlations with the E. coli index, implying that the ratio may supplement E. coli monitoring as a groundwater assessment metric capable of elucidating contamination mechanisms. This study may serve as a methodological blueprint for the development of big data-based groundwater contamination indices across the globe.

Spatio-temporal differentiation characteristics and the influencing factors of PM2.5 emissions from coal consumption in Central Plains Urban Agglomeration.

Central Plains urban agglomeration (CPUA) had developed rapidly, but its air pollution was also serious. Despite advances in study on China's PM2.5 emissions from coal consumption (CC), the differentiation characteristics and the affecting variables of PM2.5 in CPUA required further investigation. This paper computed the PM2.5 emissions of each city from 2000 to 2020 using CC data from CPUA, evaluated its spatio-temporal fluctuation characteristics using the spatial autocorrelation and analyzed its influencing factors by combining various indicators through the spatial Durbin model (SDM). The results verified that: (1) There was a trend of rapid increase of PM2.5 emissions from CC; (2) The Moran's I of the PM2.5 emissions from CC showed a significant agglomeration effect; (3) PM2.5 emissions from CC had a strong spillover effect. The recommendations were in this following: (1) The urban pollution regulation and the pace of industrial green transformation should be Strengthened; (2) Close linkages between cities should be established and attention should be paid to pollution management; (3) The spillover of PM2.5 emissions from CC should be lessened and development of environmental governance technology should be enhanced.

Using a Leroux-prior-based conditional autoregression-based strategy to map the short-term association between temperature and bacillary dysentery and its attributable burden in China.

Background: In China, bacillary dysentery (BD) is the third most frequently reported infectious disease, with the greatest annual incidence rate of 38.03 cases per 10,000 person-years. It is well acknowledged that temperature is associated with BD and the previous studies of temperature-BD association in different provinces of China present a considerable heterogeneity, which may lead to an inaccurate estimation for a region-specific association and incorrect attributable burdens. Meanwhile, the common methods for multi-city studies, such as stratified strategy and meta-analysis, have their own limitations in handling the heterogeneity. Therefore, it is necessary to adopt an appropriate method considering the spatial autocorrelation to accurately characterize the spatial distribution of temperature-BD association and obtain its attributable burden in 31 provinces of China. Methods: A novel three-stage strategy was adopted. In the first stage, we used the generalized additive model (GAM) model to independently estimate the province-specific association between monthly average temperature (MAT) and BD. In the second stage, the Leroux-prior-based conditional autoregression (LCAR) was used to spatially smooth the association and characterize its spatial distribution. In the third stage, we calculate the attribute BD cases based on a more accurate estimation of association. Results: The smoothed association curves generally show a higher relative risk with a higher MAT, but some of them have an inverted "V" shape. Meanwhile, the spatial distribution of association indicates that western provinces have a higher relative risk of MAT than eastern provinces with 0.695 and 0.645 on average, respectively. The maximum and minimum total attributable number of cases are 224,257 in Beijing and 88,906 in Hainan, respectively. The average values of each province in the eastern, western, and central areas are approximately 40,991, 42,025, and 26,947, respectively. Conclusion: Based on the LCAR-based three-stage strategy, we can obtain a more accurate spatial distribution of temperature-BD association and attributable BD cases. Furthermore, the results can help relevant institutions to prevent and control the epidemic of BD efficiently.

Evaluation and comparison of administrative division-level road traffic safety indices of Egypt, England, Turkey, and the United States.

INTRODUCTION: There is regional diversity inside countries regarding road safety indices (RSIs), and countries rarely have been compared based on these indicators. Thus, regional RSIs of England, the United States, Egypt, and Turkey were evaluated. Regional data were collected from the statistical center of each country. The adopted regional RSIs include road fatalities, health risk (HR) or fatalities per population, and traffic risk (TR) or fatalities per number of vehicles. The associations between variables were examined using correlation and regression analysis. The spatial distributions of subdivisions were evaluated using Moran's I, the local Moran index. RESULTS: Considerable differences between the countries were observed, including differences in the spatial distribution of regions and associations between RSIs. Significant relationships were detected between road fatality, population, and the number of motor vehicles. Higher exposure rates mean higher fatalities in regions. A robust linear relationship between the HR and TR indices was identified in developed countries. There is a nonlinear and significant association between motorization rates and TR indices of regions, and fatality risk decreases as the motorization rate increases. There is a considerable gap between developed and developing countries regarding regional RSIs, and the transferability of road safety models from one country to another is challenging. Huge hotspots regarding RSIs were observed in Turkey and the United States. The locations of hot spots in terms of the risk indices were identical in the developed countries.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach.

A notable finding is that Kerala's capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

The biotic and abiotic contexts of ecological selection mediate the dominance of distinct dispersal strategies in competitive metacommunities.

While the influence of dispersal on ecological selection is the subject of intense research, we still lack a thorough understanding of how ecological selection operates to favour distinct dispersal strategies in metacommunities. To address this issue, we developed a model framework in which species with distinct quantitative dispersal traits that govern the three stages of dispersal-departure, movement and settlement-compete under different ecological contexts. The model identified three primary dispersal strategies (referred to as nomadic, homebody and habitat-sorting) that consistently dominated metacommunities owing to the interplay of spatiotemporal environmental variation and different types of competitive interactions. We outlined the key characteristics of each strategy and formulated theoretical predictions regarding the abiotic and biotic conditions under which each strategy is more likely to prevail in metacommunities. By presenting our results as relationships between dispersal traits and well-known ecological gradients (e.g. seasonality), we were able to contrast our theoretical findings with previous empirical research. Our model demonstrates how landscape environmental characteristics and competitive interactions at the intra- and interspecific levels can interact to favour distinct multivariate and context-dependent dispersal strategies in metacommunities. This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.

Spatial distribution patterns of human resources allocation in maternal and child healthcare institutions in China from 2016 to 2021.

BACKGROUND: In China, economic, urbanization, and policy differences between the eastern and western regions lead to uneven healthcare resources. This disparity is more pronounced in the west due to fewer healthcare personnel per thousand individuals and imbalanced doctor-to-nurse ratios, which exacerbates healthcare challenges. This study examines the spatial distribution of human resources in maternal and child healthcare from 2016 to 2021, highlighting regional disparities and offering insights for future policy development. METHODS: The data were sourced from the "China Health and Family Planning Statistical Yearbook" (2017) and the "China Health and Health Statistics Yearbook" (2018-2022). This study utilized GeoDa 1.8.6 software to conduct both global and local spatial autocorrelation analyses, using China's administrative map as the base dataset. RESULTS: From 2016 to 2021, there was an upward trend in the number of health personnel and various types of health technical personnel in Chinese maternal and child healthcare institutions. The spatial distribution of these personnel from 2016 to 2021 revealed clusters characterized as high-high, low-low, high-low and low-high. Specifically, high-high clusters were identified in Guangxi, Hunan, Jiangxi, and Guangdong provinces; low-low in Xinjiang Uygur Autonomous Region and Inner Mongolia Autonomous Region; high-low in Sichuan province; and low-high in Fujian and Anhui provinces. CONCLUSIONS: From 2016 to 2021, there was evident spatial clustering of health personnel and various health technical personnel in Chinese maternal and child healthcare institutions, indicating regional imbalances.

Interprovincial spatial distribution patterns and socioeconomic factors on traditional Chinese medicine (TCM) service utilization in China.

The traditional Chinese medicine (TCM) industry in China exhibits significant regional disparities in health service utilization, the underlying reasons for which are yet to be fully explored. This study employs Geodetector models to analyze the factors affecting TCM service utilization, providing the first examination of spatial distribution patterns and influencing factors for both TCM outpatient (TCMOSU) and inpatient services (TCMISU). The findings of this study reveal spatial disparities across China's provinces, showing a prevalence of TCMOSU in the east and TCMISU decreasing from southwest to northeast. Global Moran's I autocorrelation analysis revealed a positive spatial correlation between TCMOSU and TCMISU across Chinese provinces, suggesting spatial clustering and the potential for interregional collaboration in the development of TCM services. Local Moran's I autocorrelation analysis revealed clusters of TCMOSU in wealthier eastern provinces, such as Jiangsu and Tianjin, and clusters of TCMISU in the southwest. Factor detector analysis revealed that disposable income per capita was the most significant factor linking higher incomes with increased TCMOSU. In contrast, TCMISU was primarily influenced by demographic factors, such as the illiteracy rate and population urbanization rate, emphasizing traditional practices in lower education regions. Interaction detector analysis revealed the joint effects of these factors, demonstrating how regional economic status, health status, and healthcare resource indicators interact with other factors for TCMOSU and how demographic factors significantly influence the prevalence of TCMISU. This study highlights the importance of considering health status together with regional economic, demographic, and healthcare resources when formulating TCM healthcare policies and allocating such resources in China. Promoting the balanced and coordinated regional development of TCM services across the country requires the development of strategies that account for these varied regional characteristics.

[Exploring the Spatial and Temporal Trajectories of Land Use Carbon Emissions and Influencing Factors in the Aksu River Basin from 1990 to 2020].

Land use changes lead to changes in the functions of different types of carbon sources and sinks, which are key sources of carbon emissions. The study of carbon emissions and its influencing factors in the Aksu River Basin from the perspective of land use change is of great importance for the promotion of integrated protection and restoration of mountains, water, forests, fields, lakes, grasslands, sand, and ice in the basin and to help achieve the goal of carbon peaking and carbon neutrality. Based on four periods of land use data and socio-economic data from 1990 to 2020, the total carbon emissions from land use were measured, and the spatial and temporal trajectories of carbon emissions and their influencing factors were explored. The results showed that:① from 1990 to 2020, arable land, forest land, construction land, and unused land showed a general increasing trend, whereas grasslands and water areas showed a decreasing trend. The spatial change in land use types was mainly characterized by the conversion of grasslands and unused land into arable land, and 83.58 % of the arable land conversion areas were concentrated in the southwest of Wensu, Aksu, and the northern part of Awat. ② The total net carbon emissions in the basin showed a continuous growth trend from 1990 to 2020, with a cumulative increase of 14.78×104 t. The increase in arable land was a key factor causing an increase in net carbon emissions in the basin. ③ The spatial distribution pattern of land use carbon emissions in the basin was high in the middle and low in the fourth, with significant changes in net carbon emissions mainly in the southern part of Wensu, Aksu, Awat, and Alaer. ④ Human activities had the strongest driving effect on land use carbon emissions, with their effects gradually increasing from east to west. The contribution of average annual temperature to land use carbon emissions was mainly concentrated in the eastern part of Aksu and the northern part of Awat, whereas average annual rainfall had a strong inhibitory effect on the northern part of Wensu and the western part of Aheqi.

Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021.

China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System. Three-dimensional spatial trends, spatial autocorrelation, and spatial-temporal scan analysis were conducted to explore the spatial clustering pattern of PTB. From 2011 to 2021, a total of 347,495 newly diagnosed PTB cases were registered. The registered incidence rate of PTB decreased from 49.78/100,000 in 2011 to 26.49/100,000 in 2021, exhibiting a steady downward trend (χ2 = 414.22, P < 0.001). The average annual registered incidence rate of PTB was higher in the central and northern regions. Moran's I indices of the registered incidence of PTB were all >0 (P< 0.05) except in 2016, indicating a positive spatial correlation overall. Local autocorrelation analysis showed that 'high-high' clusters were mainly distributed in northern Jiangsu, and 'low-low' clusters were mainly concentrated in southern Jiangsu. The results of this study assist in identifying settings and locations of high TB risk and inform policy-making for PTB control and prevention.

Integrated framework to assess soil potentially toxic element contamination through 3D pollution analysis in a typical mining city.

Soil potentially toxic elements (PTEs) pollution of contaminated sites has become a global environmental issue. However, given that previous studies mostly focused on pollution assessment in surface soils, the current status and environmental risks of potentially toxic elements in deeper soils remain unclear. The present study aims to cognize distribution characteristics and spatial autocorrelation, pollution levels, and risk assessment in a stereoscopic environment for soil PTEs through 3D visualization techniques. Pollution levels were assessed in an integrated manner by combining the geoaccumulation index (Igeo), the integrated influence index of soil quality (IICQs), and potential ecological hazard index. Results showed that soil environment at the site was seriously threatened by PTEs, and Cu and Cd were ubiquitous and the predominant pollutants in the study area. The stratigraphic models and pollution plume simulation revealed that pollutants show a decreasing trend with the deepening of the soil layer. The ranking of contamination soil volume is as follows: Cu > Cd > Zn > As > Pb > Cr > Ni. According to the IICQs evaluation, this region was subject to multiple PTE contamination, with more than 60% of the area becoming seriously and highly polluted. In addition, the ecological hazard model revealed the existence of substantial ecological hazards in the soils of the site. The integrated potential ecological risk index (RI) indicated that 45.7%, 10.13%, and 4.15% of the stereoscopic areas were in considerable, high, and very high risks, respectively. The findings could be used as a theoretical reference for applying multiple methods to integrate evaluation through 3D visualization analysis in the assessment and remediation of PTE-contaminated soils.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Background: Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Methods: Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. Results: The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. Conclusion: There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

Methods for assessing spillover effects between concurrent green initiatives.

This research presents the methods that are used to examine the dynamics and potential spillover effects of various global environmental conservation programs. We specifically show the data and models that we use to analyze the interactions and mutual influences between the U.S.'s Conservation Reserve Program (CRP) and Environmental Quality Incentives Program (EQIP), as well as those between China's Grain-to-Green Program (GTGP) and Forest Ecological Benefit Compensation (FEBC). Additionally, this study illustrates information about global initiatives, their interconnected impacts, and the associated policy strategies for environmental conservation. By utilizing multivariate regression, logistic regression, eigenvector spatial filtering, and scenario modeling, the research aims to understand the collective influence of these initiatives on broader environmental objectives. The findings of this study provide valuable insights for improving conservation policy designs and effectiveness.•Multivariate and logistic regression analyses to dissect global environmental conservation program interactions and mutual influences.•Eigenvector spatial filtering to address spatial autocorrelation and enhance the accuracy of the model results and our interpretations.•Scenario modeling to project potential future outcomes and impacts.

Exploring the Dynamic Invasion Pattern of the Black-Headed Fall Webworm in China: Susceptibility to Topography, Vegetation, and Human Activities.

The fall webworm (FWW), H. cunea (Drury) (Lepidoptera: Erebidae: Arctiidae), is an extremely high-risk globally invasive pest. Understanding the invasion dynamics of invasive pests and identifying the critical factors that promote their spread is essential for devising practical and efficient strategies for their control and management. The invasion dynamics of the FWW and its influencing factors were analyzed using standard deviation ellipse and spatial autocorrelation methods. The analysis was based on statistical data on the occurrence of the FWW in China. The dissemination pattern of the FWW between 1979 and 2022 followed a sequence of "invasion-occurrence-transmission-outbreak", spreading progressively from coastal to inland regions. Furthermore, areas with high nighttime light values, abundant ports, and non-forested areas with low vegetation cover at altitudes below 500 m were more likely to be inhabited by the black-headed FWW. The dynamic invasion pattern and the driving factors associated with the fall webworm (FWW) provide critical insights for future FWW management strategies. These strategies serve not only to regulate the dissemination of insects and diminish migratory tendencies but also to guarantee the implementation of efficient early detection systems and prompt response measures.

Exploring the County Level Mortality Pattern Variations in Rural Areas of Iran (2006- 2016).

BACKGROUND: Mortality rate in rural areas is a useful measure of the health of the population and the function of the health system, which varies over space and time. The objective of this research is to explore the spatial and temporal variations in the rural mortality rate in Iran at the county level in 2006, 2011 and 2016. METHODS: data were gathered from the rural population and mortality statistics published by the Statistical Centre of Iran and the National Organization for Civil Registration. Global spatial patterns were assessed using the Global Moran's I and local clusters through the Local Moran' I. RESULTS: Spatial distribution of rural mortality rate shows that during the years under study the number of counties with a lower rate has increased. The counties with rate of less form continuous areas in the southwest, central and east regions. The excess risk map reveals significant variations in both value and extent. Also, the values of Moran's index increased from 0.1848 in 2006 to 0.4041 in 2016, which indicates the strengthening of the cluster spatial pattern of the overall rural mortality rate. Local patterns have undergone substantial changes over space and time. CONCLUSION: The findings indicate significant spatial and temporal variations in rural mortality rates in Iran. Policymakers can use this information to plan and enhance healthcare infrastructure in specific counties. The findings serve for evaluating the effectiveness of health policies, enabling policymakers to make informed decisions, allocate resources efficiently and design targeted interventions for improved public health outcomes.

Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study.

BACKGROUND: Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. OBJECTIVE: The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. METHODS: Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. RESULTS: A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. CONCLUSIONS: This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella.

Correlations between ecosystem service value and landscape ecological risk and its spatial heterogeneity in Jilin Province, China.

Exploring the correlations between ecosystem service value (ESV) and landscape ecological risk and the driving factors of their spatial variations is crucial for maintaining regional ecological security and promoting sustainable human well-being. We carried out a grid resampling size of 5 km×5 km assessment units of Jilin Pro-vince based on the remote sensing monitoring data of land use in 2000, 2005, 2010, 2015, and 2020. We quantitatively evaluated the landscape ecological risk and ESV, and analyzed their spatial-temporal variations. Employing bivariate spatial autocorrelation analysis and the geographical detector models, we examined the correlation between the landscape ecological risk and ESV and explored the driving factors for their spatial variations. The results showed that ESV in Jilin Province decreased from 385.895 billion yuan to 378.211 billion yuan during 2000-2020. The eastern region was dominated by extremely low risk, medium risk, and low risk areas. In contrast, the western region was mainly composed of extremely high risk and high risk areas. There was a significant negative correlation and spatial negative correlation between landscape ecological risk and ESV in Jilin Province. Human activity and land use type were the important driving factors for spatial differentiation in both landscape ecological risk and ESV. Our findings suggested that scientific land use regulation and appropriate control of human activities are critically needed to optimize Jilin Province's ecological environment.

Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series.

The availability of a sufficient number of annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Creating these samples is time-consuming and costly. Active Learning (AL) offers a solution by streamlining sample annotation, resulting in more efficient training with less effort. Unfortunately, most of the developed AL methods overlook spatial information inherent in remote sensing images. We propose a novel spatially explicit AL that uses the semi-variogram to identify and discard redundant, spatially adjacent samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected 97 samples achieving an overall accuracy of 80%, compared to traditional AL selecting 169 samples with 82% overall accuracy. In Belgium, spatially explicit AL selected 223 samples and obtained 60% overall accuracy, while traditional AL selected 327 samples and obtained an overall accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as 'fruits and nuts' posed, however, a challenge.