The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures.

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Risk hotspots and influencing factors identification of heavy metal(loid)s in agricultural soils using spatial bivariate analysis and random forest.

Heavy metal(loid)s (HMs) in agricultural soils not only affect soil function and crop security, but also pose health risks to residents. However, previous concerns have typically focused on only one aspect, neglecting the other. This lack of a comprehensive approach challenges the identification of hotspots and the prioritization of factors for effective management. To address this gap, a novel method incorporating spatial bivariate analysis with random forest was proposed to identify high-risk hotspots and the key influencing factors. A large-scale dataset containing 2995 soil samples and soil HMs (As, Cd, Cr, Cu, Mn, Ni, Pb, Sb, and Zn) was obtained from across Henan province, central China. Spatial bivariate analysis of both health risk and ecological risks revealed risk hotspots. Positive matrix factorization model was initially used to investigate potential sources. Twenty-two environmental variables were selected and input into random forest to further identify the key influencing factors impacting soil accumulation. Results of local Moran's I index indicated high-high HM clusters at the western and northern margins of the province. Hotspots of high ecological and health risk were primarily observed in Xuchang and Nanyang due to the widespread township enterprises with outdated pollution control measures. As concentration and exposure frequency dominated the non-carcinogenic and carcinogenic risks. Anthropogenic activities, particularly vehicular traffic (contributing ∼37.8 % of the total heavy metals accumulation), were the dominant sources of HMs in agricultural soils. Random forest modeling indicated that soil type and PM2.5 concentrations were the most influencing natural and anthropogenic variables, respectively. Based on the above findings, control measures on traffic source should be formulated and implemented provincially; in Xuchang and Nanyang, scattered township enterprises with outdated pollution control measures should be integrated and upgraded to avoid further pollution from these sources.

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.

Purpose: To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities. Methods: Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen. Results: The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I = 0.237; 2019, Moran's I = 0.226; 2021, Moran's I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas. Conclusion: Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data.

Background: Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks. Objective: This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students. Methods: PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases. Results: From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions. Conclusions: Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

[Spatiotemporal clustering and hot spot analysis of visceral leishmaniasis in Gansu Province from 1993 to 2023].

OBJECTIVE: To investigate the spatiotemporal clustering characteristics of the reported incidence of visceral leishmaniasis (VL) in Gansu Province from 1993 to 2023, so as to provide insights into the containment of VL and prevention of VL recurrence. METHODS: County (district)-level epidemical data of VL in Gansu Province from 1993 to 2023 were collected, and the geographical information database of reported VL incidence in Gansu Province was created according to the county-level administrative code and electronic maps in Gansu Province. In addition, the spatial autocorrelation analysis and hot spot analysis of the reported VL incidence were performed in Gansu Province using the software ArcGIS 10.8. RESULTS: A total of 2 597 VL cases were reported in Gansu Province from 1993 to 2023, with an annual average incidence rate of 3.036/105. Spatial autocorrelation analysis showed spatial clustering of the reported VL incidence in Gansu Province (Moran's I = 0.605, Z = 5.240, P < 0.001), appearing high-high clustering features (Getis-Ord G = 0.080, Z = 4.137, P < 0.001), and high-high clustering of the reported incidence of VL was identified in Diebu County, Tanchang County, Zhouqu County and Wenxian County. Hot spot analysis showed hot-spot areas of the reported VL incidence in Tanchang County, Zhouqu County, Wudu District and Wenxian County along the Bailong River basins and cold-spot areas in Qin'an County and Gangu County. CONCLUSIONS: There was spatial clustering and hot spots of the reported VL incidence in Gansu Province from 1993 to 2023. Intensified surveillance and control is required to prevent the spread of VL.

Spatial and Temporal Analysis of Severe Fever with Thrombocytopenia Syndrome in Anhui Province from 2011 to 2023.

OBJECTIVE: To analyze the spatial autocorrelation and spatiotemporal clustering characteristics of severe fever with thrombocytopenia syndrome(SFTS) in Anhui Province from 2011 to 2023. METHODS: Data of SFTS in Anhui Province from 2011 to 2023 were collected. Spatial autocorrelation analysis was conducted using GeoDa software, while spatiotemporal scanning was performed using SaTScan 10.0.1 software to identify significant spatiotemporal clusters of SFTS. RESULTS: From 2011 to 2023, 5720 SFTS cases were reported in Anhui Province, with an average annual incidence rate of 0.7131/100,000. The incidence of SFTS in Anhui Province reached its peak mainly from April to May, with a small peak in October. The spatial autocorrelation results showed that from 2011 to 2023, there was a spatial positive correlation(P < 0.05) in the incidence of SFTS in all counties and districts of Anhui Province. Local autocorrelation high-high clustering areas are mainly located in the south of the Huaihe River. The spatiotemporal scanning results show three main clusters of SFTS in recent years: the first cluster located in the lower reaches of the Yangtze River, the eastern region of Anhui Province; the second cluster primarily focused on the region of the Dabie Mountain range, while the third cluster primarily focused on the region of the Huang Mountain range. CONCLUSIONS: The incidence of SFTS in Anhui Province in 2011-2023 was spatially clustered.

Unveiling spatial patterns of West Nile virus emergence in northern Greece, 2010-2023.

The Region of Central Macedonia (RCM) in Northern Greece recorded the highest number of human West Nile virus (WNV) infections in Greece, despite considerable local mosquito control actions. We examined spatial patterns and associations of mosquito levels, infected mosquito levels, and WNV human cases (WNVhc) across the municipalities of this region over the period 2010-2023 and linked it with climatic characteristics. We combined novel entomological and available epidemiological and climate data for the RCM, aggregated at the municipality level and used Local and Global Moran's I index to assess spatial associations of mosquito levels, infected mosquito levels, and WNVhc. We identified areas with strong interdependencies between adjacent municipalities in the Western part of the region. Furthermore, we employed a Generalized Linear Mixed Model to first, identify the factors driving the observed levels of mosquitoes, infected mosquitoes and WNVhc and second, estimate the influence of climatic features on the observed levels. This modeling approach indicates a strong dependence of the mosquito levels on the temperatures in winter and spring and the total precipitation in early spring, while virus circulation relies on the temperatures of late spring and summer. Our findings highlight the significant influence of climatic factors on mosquito populations (∼60 % explained variance) and the incidence of WNV human cases (∼40 % explained variance), while the unexplained ∼40 % of the variance suggests that targeted interventions and enhanced surveillance in identified hot-spots can enhance public health response.

Spatial effects of township health centers' health resource allocation efficiency in China.

Introduction: China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. Methods: This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. Results: The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. Discussion: To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

iDHS-RGME: Identification of DNase I hypersensitive sites by integrating information on nucleotide composition and physicochemical properties.

As pivotal markers of chromatin accessibility, DNase I hypersensitive sites (DHSs) intimately link to fundamental biological processes encompassing gene expression regulation and disease pathogenesis. Developing efficient and precise algorithms for DHSs identification holds paramount importance for unraveling genome functionality and elucidating disease mechanisms. This study innovatively presents iDHS-RGME, an Extremely Randomized Trees (Extra-Trees)-based algorithm that integrates unique feature extraction techniques for enhanced DHSs prediction. Specifically, iDHS-RGME utilizes two feature extraction approaches: Reverse Complementary Kmer (RCKmer) and Geary Spatial Autocorrelation (GSA), which comprehensively capture sequence attributes from diverse angles, bolstering information richness and accuracy. To address data imbalance, Borderline-SMOTE is employed, followed by Maximum Information Coefficient (MIC) for meticulous feature selection. Comparative evaluations underscored the superiority of the Extra-Trees classifier, which was subsequently adopted for model prediction. Through rigorous five-fold cross-validation, iDHS-RGME achieved remarkable accuracies of 94.71 % and 95.07 % on two independent datasets, outperforming previous models in terms of both precision and effectiveness.

Analysis of the epidemiological trends and spatial patterns of esophageal cancer among male populations in China's mainland from 1990 to 2021.

BACKGROUND: This study aimed to delineate the temporal patterns of esophageal cancer epidemic trends and spatial clustering patterns among male populations in China's mainland from 1990 to 2021. This analysis aimed to provide a scientific rationale and empirical data to facilitate the formulation of targeted prevention and control strategies. METHODS: Data on the number of cases and deaths, crude and age-standardized incidence and mortality rates of esophageal cancer in men were collected from the Global Burden of Disease Study and the Chinese Cancer Registry Annual Report. Global and local Moran's I spatial autocorrelation index was employed to quantify spatial clustering, and a disease map was drawn. RESULTS: From 1990 to 2021, the cumulative incidence and mortality of esophageal cancer in men were 6 100 342 and 5 972 294, respectively. The crude incidence and death rates increased in 2021, yet the age-standardized rates decreased significantly. Cixian County in Hebei Province had the highest age-standardized rates. The disease displayed spatial clustering, with relatively high rates in Shandong, Jiangsu, and Hebei Provinces. CONCLUSION: Since 1990, the incidence and mortality of esophageal cancer among men in mainland China have remained high, imposing a considerable burden. Although age-adjusted rates have declined, they are still relatively high overall, especially in Shandong, Hebei, and Jiangsu Provinces.

Spatio-temporal assessment of landscape ecological risk using spatial statistical analysis in a basin of Turkiye.

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas.

Trends and geographical variations in outpatient antimicrobial consumption in Ireland in relation to socio-economic deprivation.

Background: Different factors have been associated with changes in antimicrobial consumption rates in Ireland, however the relationship between socio-economic deprivation and antimicrobial consumption has not been explored. The presented ecological analysis explores the temporal and geographical variation in outpatient antimicrobial consumption and socio-economic deprivation in Ireland from January 2015 to March 2022. Method: Deprivation index (DI) was used as a socio-economic proxy. A multilevel mixed model was applied to explore temporal variation and analyse the longitudinal antimicrobial consumption (DID) in relation to DI. Furthermore, maps were generated based on antimicrobial consumption rates, and spatial autocorrelation analyses were carried out to study geographical variation in antimicrobial consumption rates. Results: The antimicrobial consumption rates per month varied from 26.2 DID (January 2015) to 22.1 DID (March 2022) showing an overall reduction of 16 %. Overall, total antimicrobial consumption in the multilevel model showed a consistent correlation with higher DI score (6.6 (95%CI 3.9 to 9.3)), and winter season (3.6 (95%CI 3.2 to 3.9)). In contrast, before COVID-19 showed significant lower antimicrobial consumption rates compared to during COVID-19 (-4.0 (95%CI -4.7 to -3.23)). No consistent trends were observed for geographical variation between areas. Conclusion: Antimicrobial consumption rates decreased from 2015 to 2021 in Ireland. No geographical patterns were observed in antimicrobial consumption rates but associations between deprivation and antimicrobial consumption rates were observed.

Regional coordinated development of low-carbon sports industry in China.

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial-temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry.

Unmet healthcare needs and the local extinction index: an analysis of regional disparities impacting South Korea's older adults.

Background: This study examines the factors affecting unmet healthcare experiences by integrating individual-and community-level extinction indices. Methods: Using spatial autocorrelation and multilevel modeling, the study utilizes data from the Community Health Survey and Statistics Korea for 218 local government regions from 2018 to 2019. Results: The analysis identifies significant clustering, particularly in non-metropolitan regions with a higher local extinction index. At the individual level, some factors affect unmet medical needs, and unmet healthcare needs increase as the local extinction index at the community level increases. Conclusion: The findings underscore the need for strategic efforts to enhance regional healthcare accessibility, particularly for vulnerable populations and local infrastructure development.

Spatial mapping of greenhouse gases using a UAV monitoring platform over a megacity in China.

Urban environments are recognized as main anthropogenic contributors to greenhouse gas (GHG) emissions, characterized by unevenly distributed emission sources over the urban environments. However, spatial GHG distributions in urban regions are typically obtained through monitoring at only a limited number of locations, or through model studies, which can lead to incomplete insights into the heterogeneity in the spatial distribution of GHGs. To address such information gap and to evaluate the spatial representation of a planned GHG monitoring network, a custom-developed atmospheric sampler was deployed on a UAV platform in this study to map the CO2 and CH4 mixing ratios in the atmosphere over Zhengzhou in central China, a megacity of nearly 13 million people. The aerial survey was conducted along the main roads at an altitude of 150 m above ground, covering a total distance of 170 km from the city center to the suburbs. The spatial distributions of CO2 and CH4 mixing ratios in Zhengzhou exhibited distinct heterogeneities, with average mixing ratios of CO2 and CH4 at 439.2 ± 10.8 ppm and 2.12 ± 0.04 ppm, respectively. A spatial autocorrelation analysis was performed on the measured GHG mixing ratios across the city, revealing a spatial correlation range of approximately 2 km for both CO2 and CH4 in the urban area. Such a spatial autocorrelation distance suggests that the urban GHG monitoring network designed for emission inversion purposes need to have a spatial resolution of 4 km to characterize the spatial heterogeneities in the GHGs. This UAV-based measurement approach demonstrates its capability to monitor GHG mixing ratios across urban landscapes, providing valuable insights for GHG monitoring network design.

Spatiotemporal analysis of tuberculosis in the Hunan Province, China, 2014-2022.

Background: Pulmonary tuberculosis (PTB) is a major infectious disease that threatens human health. China is a high tuberculosis-burden country and the Hunan Province has a high tuberculosis notification rate. However, no comprehensive analysis has been conducted on the spatiotemporal distribution of PTB in the Hunan Province. Therefore, this study investigated the spatiotemporal distribution of PTB in the Hunan Province to enable targeted control policies for tuberculosis. Methods: We obtained data about cases of PTB in the Hunan Province notified from January 2014 to December 2022 from the China Information System for Disease Control and Prevention. Time-series analysis was conducted to analyze the trends in PTB case notifications. Spatial autocorrelation analysis was conducted to detect the spatial distribution characteristics of PTB at a county level in Hunan Province. Space-time scan analysis was conducted to confirm specific times and locations of PTB clustering. Results: A total of 472,826 new cases of PTB were notified in the Hunan Province during the 9-year study period. The mean PTB notification rate showed a gradual, fluctuating downward trend over time. The number of PTB notifications per month showed significant seasonal variation, with an annual peak in notifications in January or March, followed by a fluctuating decline after March, reaching a trough in November or December. Moran's I index of spatial autocorrelation revealed that the notification rate of PTB by county ranged from 0.117 to 0.317 during the study period, indicating spatial clustering. The hotspot areas of PTB were mainly concentrated in the Xiangxi Autonomous Prefecture, Zhangjiajie City, and Hengyang City. The most likely clustering region was identified in the central-southern part of the province, and a secondary clustering region was identified in the northwest part of the province. Conclusion: This study identified the temporal trend and spatial distribution pattern of tuberculosis in the Hunan Province. PTB clustered mainly in the central-southern and northwestern regions of the province. Disease control programs should focus on strengthening tuberculosis control in these regions.

Spatiotemporal Evolution and Spatial Analysis of Ecological Environmental Quality in the Longyangxia to Lijiaxia Basin in China Based on GEE.

The upper reaches of the Yellow River are critical ecological barriers within the Yellow River Basin (YRB) that are crucial for source conservation. However, environmental challenges in this area, from Longyangxia to Lijiaxia, have emerged in recent years. To assess the ecological environment quality (EEQ) evolution from 1991 to 2021, we utilized remote sensing ecological indices (RSEIs) on the Google Earth Engine (GEE) platform. Spatial autocorrelation and heterogeneity impacting EEQ changes were examined. The results of this study show that the mean value of the RSEIs fluctuated over time (1991: 0.70, 1996: 0.77, 2001: 0.67, 2006: 0.71, 2011: 0.68, 2016: 0.65, and 2021: 0.66) showing an upward, downward, and then upward trend. The mean values of the overall RSEI are all at 0.65 and above. Most regions showed no significant EEQ change during 1991-2021 (68.59%, 59.23%, and 55.78%, respectively). Global Moran's I values (1991-2021) ranged from 0.627 to 0.412, indicating significant positive correlation between EEQ and spatial clustering, and the LISA clustering map (1991-2021) shows that the area near Longyangxia Reservoir shows a pattern of aggregation, dispersion, and then aggregation again. The factor detection results showed that heat was the most influential factor, and the interaction detection results showed that greenness and heat had a significant effect on regional ecosystem distribution. Our study integrates spatial autocorrelation and spatial heterogeneity and combines them with reality to provide an in-depth discussion and analysis of the Longyangxia to Lijiaxia Basin. These findings offer guidance for ecological governance, vegetation restoration, monitoring, and safeguarding the upper Yellow River's ecological integrity.

Spatial differentiation characteristics of the Hemiptera insects in China.

The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high-high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio-economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision-making basis for biodiversity conservation and efficient pest control.

Investigating the impact of urban growth on land use using spatial autocorrelation methods in Konya/Türkiye.

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.

Spatiotemporal smoothing of water quality in a complex riverine system with physical barriers.

Freshwater ecosystems offer a variety of ecosystem services, and water quality is essential information for understanding their environment, biodiversity, and functioning. Interpolation by smoothing methods is a widely used approach to obtain temporal and/or spatial patterns of water quality from sampled data. However, when these methods are applied to freshwater systems, ignoring terrestrial areas that act as physical barriers may affect the structure of spatial autocorrelation and introduce bias into the estimates. In this study, we applied stochastic partial differential equation (SPDE) smoothing methods with barriers to spatial interpolation and spatiotemporal interpolation on water quality indices (chemical oxygen demand, phosphate phosphorus, and nitrite nitrogen) in a freshwater system in Japan. Then, we compared the estimation bias and accuracy with those of conventional non-barrier models. The results showed that the estimation bias of spatial interpolations of snapshot data was improved by considering physical barriers (5.8 % for (chemical oxygen demand, 22.5 % for phosphate phosphorus, and 21.6 % for nitrite nitrogen). The prediction accuracy was comparable to that of the non-barrier model. These were consistent with the expectation that accounting for physical barriers would capture realistic spatial correlations and reduce estimation bias, but would increase the variance of the estimates due to the limited information that can be gained from the neighbourhood. On the other hand, for spatiotemporal smoothing, the barrier model was comparable to the non-barrier model in terms of both estimation bias and prediction accuracy. This may be due to the availability of information in the time direction for interpolation. These results demonstrate the advantage of considering barriers when the available data are limited, such as snapshot data. SPDE smoothing methods can be widely applied to interpolation of various environmental and biological indices in river systems and are expected to be powerful tools for studying freshwater systems spatially and temporally.