Global Spatial Autocorrelation Research Articles

Coupling coordination and interactive relationship between population urbanization and land urbanization from the perspective of shrinking cities: a case study of Jiangsu province, China

Introduction: Urban contraction brings about certain impacts on the advancement of urbanization.Methods: This paper explores the coupling coordination (CCD) and geographical distribution of land urbanization (LU) and population urbanization (PU) in Jiangsu Province through a CCD model and a spatial autocorrelation model from the perspective of shrinking cities. The Panel Vector Autoregression (PVAR) model is constructed to deeply investigate the complex interaction between the PU-LU in 13 cities, 2007-2020.Results: The study found that the PU-LU CCD in each of the above-mentioned cities shows a synergistic incremental evolution during the study period in terms of time series. In addition, in terms of spatial characteristics, the CCD of PU and LU shows significant positive global spatial autocorrelation, and the CCD of cities with high population size growth is much larger than that of cities with continuous population size. Last, there is an interactive response relationship between PU and LU. PU influences LU, however, PU itself is influenced by its own system’s internal structure. The impact of PU on LU shows a positive cumulative effect of the “inverted U shape.”Discussion: Furthermore, this paper proposes that policies be created to ensure the coordinated growth of PU-LU based on the differences in resource endowments of cities with 3 types of urban shrinkage. The link between floating population and construction land for cities should be established through enhancing the extent of intensive land use and reforming household registration.

Epidemiological Characteristics and Spatiotemporal Patterns of Visceral Leishmaniasis in Xinjiang, China, during 2004–2021

The spread of visceral leishmaniasis (VL), a serious global zoonotic parasitic disease, is mostly under control; however, several cases have been reported in recent decades in Xinjiang, China. This study aimed to analyze the epidemiological status and spatiotemporal clustering characteristics of VL in Xinjiang, China, between 2004 and 2021 to provide a basis for the development and implementation of surveillance and response measures. Data on VL incidence during 2004–2021 were collected from the National Diseases Reporting Information System of China. Global spatial autocorrelation analysis, identification of local indicators of spatial association, and spatial–temporal clustering analysis were conducted to identify the distribution and high-risk areas. A total of 2034 VL cases were reported, with a mean annual incidence of 0.50 per 100,000. There was a general decreasing trend in the incidence of VL during our study period. The majority of the cases were reported from October to February of the following year, and fewer cases were reported from April to July. Spatial autocorrelation analysis revealed that the incidence of VL was spatially clustered within a few counties. Significant differences were observed during the study period (Moran’s I = 0.74, Z = 4.900, p < 0.05). The male-to-female ratio was 1.37:1, and most patients were in the age group 0–3 years. Cases were primarily distributed in seven regions and two autonomous prefectures, and Kashgar reported the highest number of cases (1688, 82.98%). Spatial analysis revealed that the aggregation of VL was predominantly observed in southwest Xinjiang. This was in alignment with the high-risk areas identified by spatiotemporal clustering analysis. The H-H clustering region was primarily observed in Gashi, Atushi, Shufu, Injisha, Kashgar, Yepuhu, and Bachu. These findings indicate that integrated control measures must be taken in different endemic areas to strengthen the VL control program in Xinjiang, China.

Detection of spatial, temporal and spatiotemporal distribution of diarrhea incidence among under-five children in Central Gondar zone, Northwest Ethiopia: a time-series study (2019–2022)

Under-five children’s diarrhea is a significant public health threat and the World Health Organization (WHO) reported it as the second leading cause of children’s death worldwide. In this study area, little is known about the spatiotemporal distribution of under-5 diarrhea incidence. This study was therefore, conducted among all districts in the Central Gondar zone to assess the spatial, temporal, and spatiotemporal variation in diarrhea incidence among under-five children in the Central Gondar zone. The data for children under 5 years of age with diarrhea was obtained from Central Gondar Zone Health Department diarrhea reports from January 2019 to December 2022. All districts were included and geo-coded. The spatial data were created in ArcGIS 10.8.1. Global and local spatial autocorrelation were used to detect hot spots and cold spots. The Poisson model was generated by applying the Kulldorff method in SaTScan™9.6 to analyse the the purely temporal, spatial, and space-time clusters. The study revealed spatial variation of under-5 diarrhea where Gondar City, Gondar Zuria, East Dembia, and Lay Armacho districts were the high-rate spatial clusters during the study period. A year search window for temporal scan statistic identified 01 January 2020-30 December 2021 as risk periods across all districts. Spatiotemporal scan statistics detected high-rate clusters at Gondar City, Gondar Zuria, East Dembia, Lay Armacho, and Alefa between 2019 and 2022. In conclusion, there has been a spatial, temporal, and spatiotemporal variability of under-5 children’s diarrhea in the Central Gondar Zone. Interventional and preventive strategies should be developed and given priority to the areas that has been detected as a hotspot in this study to reduce the mortality and morbidity of under 5 children.

A pathway to sustainable development in China: The impact of local higher education expenditure on green total factor productivity

With the global warming crisis looming, the question of how to advance green total factor productivity (GTFP) has become an important concern confronting many developing countries. Although existing studies have demonstrated that total human capital can improve GTFP, the research has neglected to consider the influence of local higher education expenditure (LHEE), and no research has examined how LHEE spatially affect GTFP. Therefore, based on spatial economics theory, this study explores the spatial autocorrelation of LHEE and GTFP in China’s 30 provinces from 2004 to 2021, employing a spatial Durbin model to analyze the spillover effect and influence mechanism of LHEE on GTFP. The results reveal that LHEE and GTFP exhibit positive global spatial autocorrelation. LHEE primarily improves GTFP and its subcomponents through spillover effects. The positive spillover effects in the three regions of China are significantly higher than the direct effects, whereas the direct effects in the eastern and central regions are positive but insignificant. Furthermore, LHEE promotes GTFP by advancing green technological innovation. The findings provide valuable insights to help policymakers address sustainable development goal 4 and develop synergistic regional GTFP growth policies to establish sustainable societies worldwide.

Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study.

The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

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

BackgroundIn 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.MethodsThe 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.ResultsFrom 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.ConclusionsFrom 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.

Spatial patterns of tuberculosis in Aceh Province during the COVID-19 pandemic: a geospatial autocorrelation assessment

Abstract Tuberculosis remains a significant global health challenge, especially during the COVID-19 pandemic. In Aceh Province, where tuberculosis incidence has reached alarming levels, understanding the spatial distribution of cases is crucial for effective interventions. This study aimed to assess the spatial autocorrelation of tuberculosis incidence during the COVID-19 pandemic in 23 districts of Aceh Province, Indonesia. Using an ecological study, we analysed secondary data of the reported tuberculosis cases in Aceh Province covering 2020 to 2022. We use the Shapiro-Wilk test to evaluate the normality and the Kruskal-Wallis test to compare the differences between study periods. Spatial autocorrelation was evaluated at both global and local levels using Moran’s I and Getis-Ord statistics, respectively. The study included 24,715 tuberculosis cases, revealing that the district with the highest incidence differed in each year. The study showed that TB case in Aceh Province was not normally distributed yet different for each study period. Surprisingly, the global spatial autocorrelation was detected only in 2022 (p-value = 0.013). However, local spatial autocorrelation identified Pidie Jaya, Lhokseumawe, Aceh Utara and Bener Meriah as statistically significant hot-spots for 3 consecutive years. It suggests that the tuberculosis incidence in most districts did not significantly influence the incidence of tuberculosis in its neighbours. These findings contribute to our understanding of tuberculosis distribution and offer valuable insights for public health planning and resource allocation.

Spatiotemporal variation and evolutionary analysis of the coupling coordination between urban social-economic development and ecological environments in the Yangtze River Delta cities

When the process of urbanization has brought economic benefits in Yangtze River Economic Belt (YREB), the environmental issues become increasingly prominent. The evolutionary coupled coordination degree (CCD) between urban social economy and ecological environments was explored in 41 cities of YREB. Results indicated that the overall CCD between urban social economy and ecological environments showed a fluctuating upward trend from 2010 to 2020 in YREB. The CCD of Shanghai was the best among these of 41 cities, while those of three provinces were lower than the average CCD of YREB. There were significant differentiated spatial clusters of high and low among these YREB cities in both the global and local spatial autocorrelation analyses during 2010–2020. The CCD estimations indicated a fitted spatial distribution of "high in the east and low in the west" in YREB. Furthermore, CCD was positively influenced by the urban density of population, economic acceleration and technical advancement (P < 0.01), while it was negatively influenced by the urban industrial structures and energy consumption (P < 0.01). Subsequently, corresponding policy implications and recommendations, including enhancing policy innovation and promoting technological progress, were proposed to facilitate the formulation of evidence-based developmental strategies and policies of sustainable cities and society in YREB.

Spatial and Temporal Heterogeneity of Eco-Environmental Quality in Yanhe Watershed (China) Using the Remote-Sensing-Based Ecological Index (RSEI)

The long-term impacts of climate change and human activities have resulted in the Yanhe watershed, a typical watershed in the Loess Plateau region, exhibiting a high degree of vulnerability and significant heterogeneity in ecological environmental quality. This has led to environmental degradation and complex socio-ecological challenges. Consequently, there is an urgent need to carry out research on the spatial and temporal differentiation patterns of ecological environment quality. By utilizing remote sensing data spanning 21 years, this study evaluated the evolutionary trends and consistency of ecological environment quality (EEQ) within the Yanhe watershed based on the remote-sensing-based ecological index (RSEI). Furthermore, it examined global and local spatial autocorrelation of the RSEI by constructing a hexagonal grid, thereby revealing the spatiotemporal characteristics of EEQ at different scales within the Yanhe watershed. The results were as follows: (1) The EEQ has exhibited an overall upward trend in the past two decades, while it has displayed significant fluctuations; (2) the Global Moran’s I values for the years 2000, 2010, and 2020 were 0.18, 0.32, and 0.21, respectively, indicating a presence of spatial autocorrelation within the RSEI; (3) the overall EEQ of the Yanhe watershed will continue to improve, although the ecological quality in certain areas remains unstable due to local natural conditions and human activities. This research not only contributes to the technical framework for analyzing the spatiotemporal heterogeneity of EEQ but also provides actionable insights for ecosystem restoration and sustainability within the Loess Plateau watershed. Our work advances the understanding of ecological dynamics in semi-arid regions and offers a model for assessing ecological quality in similar environmental contexts.

Socio-Spatial Analysis of Water Affordability at Small Scales: A Needs-Based Approach

Water affordability as a dimension of water poverty is becoming an increasing source of concern in cities of the Global North. Studies on water affordability are either based on water wants and not needs or tend to use spatial scales too large for effective analyses of local inequities that can truly guide policy actions. In this contribution, we calculate and map a Water Affordability Index (WAI) based on the minimum water requirement of 100 litres/person/day at the scale of the census tract for the Metropolitan Area of Barcelona. We also apply global and local spatial autocorrelation analyses to investigate spatial relationships between the WAI and poverty-related sociodemographic variables. Results show that, even though average WAI values are moderate, the distribution pattern of higher and lower values tends to be clustered in some districts and neighbourhoods of the study area. Bivariate correlations indicate that water affordability is not only related to poverty variables but also to the diversity of water prices. Findings exemplify how the constructed index can complement existing affordability indicators, revealing and mapping important risk groups struggling to meet the costs of essential water needs. Water affordability could be mitigated by supportive water pricing policies for vulnerable households in water poverty hotspots.

Temporal changes in spatial scale and autocorrelation structure of forest openings based on taxonomic, phylogenetic, and functional turnover

ContextDuring European settlement, Illinois grasslands were converted for agricultural purposes. Remaining natural areas in southern Illinois include natural xeric forest openings, with communities representative of remnant grasslands and adjacent hardwood forests. Previous research in these openings shows plant communities are driven by edaphic conditions. ObjectivesThe first objective aimed to characterize spatial scale and autocorrelation structure of these openings based on climatic, environmental, and diversity variables. The second objective was to predict taxonomic, phylogenetic, and functional turnover between 1988 and 2019, using climatic and environmental variables. MethodsSurveys were conducted to calculate taxonomic, phylogenetics and functional trait metrics and analyses of these dimensions of diversity. Randomization tests were used to assess phylogenetic and functional clustering and over-dispersion at each site. Spatially-explicit climatic and environmental variables were included from earlier surveys and data repositories. Global Moran's I and spatial autocorrelograms were used to assess spatial structure of climatic, environmental, and diversity variables and generalized dissimilarity modeling was used to characterize taxonomic, phylogenetic, and functional turnover based on environmental variables. ResultsSoil depth was the only environmental variable which exhibited significant global spatial autocorrelation. Overall, sandstone sites were phylogenetically over-dispersed while loess sites were phylogenetically clustered. Climate variables and diversity metrics exhibited significant spatial structure during surveys. Generalized dissimilarity models showed that geographic distance between openings was the most influential driver of turnover across surveys. ConclusionsPrevious glacial events explained the spatial structure of soil depth across sites, due to Quaternary loess deposition in loess sites. High diversity values were clustered in the southeastern portions of the study area. Functional generalized dissimilarity models best predicted turnover in these openings compared to taxonomic and phylogenetic models.

Spatial-temporal distribution characteristics of etiologically positive pulmonary tuberculosis in Jiangsu Province from 2011 to 2021

Objective: To analyze the spatial-temporal distribution of etiologically positive pulmonary tuberculosis (PTB) at the county (city, district) unit in Jiangsu Province from 2011 to 2021 to provide evidence for the implementation and adjustment of prevention and control strategies of PTB in Jiangsu Province. Methods: The registration data of etiologically positive PTB patients in Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System in the China Information System of Disease Control and Prevention. Data on the permanent population were from the statistical yearbook of each county (city, district) in Jiangsu Province. Geoda 1.18.0 software was used to analyze the global and local spatial autocorrelation and explore the spatial clustering. SaTScan 10.1 software was used to analyze the spatial-temporal clusters, and ArcGIS 10.7 software was used to visualize the spatial-temporal clusters. Results: A total of 128 240 etiological positive PTB cases were registered in Jiangsu Province from 2011 to 2021, with an average annual registration rate of 13.99/100 000. The registration rate showed an overall upward trend (trend χ2=63.49, P<0.001) after 2017, and the etiologically positive rate showed an overall upward trend (trend χ2=3 710.86, P<0.001). The annual Moran's I values ranged from 0.107 to 0.343, which showed a spatial clustering distribution. The results of local spatial autocorrelation analysis showed that there were "high-high" clustering areas in Jiangsu Province each year, showing a dynamic distribution, and most of the areas were distributed in the central and southern regions of Jiangsu Province, with the largest number (7) in 2015 and the smallest number (1) in 2011. A total of 4 spatial-temporal clustering areas were explored by spatial-temporal scanning analysis (all P<0.001), among which the first-level clustering area covered 3 counties (cities, districts), namely Changshu, Taicang, and Xiangcheng District of Suzhou, and the clustering time was from 2011 to 2015. The secondary clustering areas covered 24 counties (cities, districts), mainly covering Jiangsu's central and northern regions, such as Huai'an, Suqian, and Yancheng. The third-level clustering areas covered 26 counties (cities, districts); the fourth-level clustering area was the Gaochun District of Nanjing, with the clustering period from 2017 to 2021. Conclusions: From 2011 to 2021, the etiologically positive PTB registration rate at the county (city, district) level in Jiangsu Province had obvious spatial-temporal clustering characteristics. The clustering areas included the northern areas with relatively backward economies and the southern areas with better economic development. Multiple measures should be taken to prevent and control PTB according to the specific situation in different regions.

Industrial fluoride emissions and their spatial characteristics in the Nansi Lake Basin, Eastern China.

Excessive fluoride emissions threaten ecological stability and human health. Previous studies have noted that industrial sources could be significant. However, quantifying industrial fluoride emissions has not been yet reported. In this study, both bottom-up and top-down approaches were used to estimate the fluoride emissions in the Nansi Lake Basin. Global and local spatial autocorrelation were adopted to reveal the spatial agglomeration effects. The fluoride emissions calculated by the bottom-up approach were larger than those calculated by the top-down method. The highest fluoride input mainly occurred in Zoucheng and Mudan. The highest fluoride emissions mainly occurred in Zoucheng and Rencheng using the bottom-up approach. The highest fluoride emissions mainly occurred in Zoucheng and Yanzhou using the top-down approach. Mining and washing of bituminous coal and anthracite (BAW) was the most significant source of fluoride input and emissions. A significant spatial agglomeration effect of fluoride emissions was found. These findings could provide a method for accurate industrial fluoride emission estimation, complement the critical data on the fluoride emissions of main industrial sectors, and provide a scientific basis for tracing fluoride sources.

Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study

BackgroundDespite many efforts to control leprosy worldwide, it is still a significant public health problem in low- and middle-income regions. It has been endemic in China for thousands of years, and southwest China has the highest leprosy burden in the country.MethodsThis observational study was conducted with all newly detected leprosy cases in southwest China from 2010 to 2020. Data were extracted from the Leprosy Management Information System (LEPMIS) database in China. The Joinpoint model was used to determine the time trends in the study area. Spatial autocorrelation statistics was performed to understand spatial distribution of leprosy cases. Spatial scan statistics was applied to identify significant clusters with high rate.ResultsA total of 4801 newly detected leprosy cases were reported in southwest China over 11 years. The temporal trends declined stably. The new case detection rate (NCDR) dropped from 4.38/1,000,000 population in 2010 to 1.25/1,000,000 population in 2020, with an average decrease of 12.24% (95% CI: −14.0 to − 10.5; P < 0.001). Results of global spatial autocorrelation showed that leprosy cases presented clustering distribution in the study area. Most likely clusters were identified during the study period and were frequently located at Yunnan or the border areas between Yunnan and Guizhou Provinces. Secondary clusters were always located in the western counties, the border areas between Yunnan and Sichuan Provinces.ConclusionsGeographic regions characterized by clusters with high rates were considered as leprosy high-risk areas. The findings of this study could be used to design leprosy control measures and provide indications to strengthen the surveillance of high-risk areas. These areas should be prioritized in the allocation of resources.

Influential Factors of Tuberculosis Notification Rates in Turkey: A Provincial-Level Spatial Analysis.

The total annual count of reported tuberculosis (TB) cases continues to decline throughout Turkey. Recognizing the regions with high and low burdens and revealing the factors affecting TB notification rates may play a role in guiding national control programs. This study aimed to analyze the spatial distribution of TB notification rates from 2005 to 2018 and evaluate the factors contributing to TB rates. In this ecological study, we used freely available open data from the Internet. We employed global and local spatial autocorrelation analysis to identify the spatial distribution and the clusters with low and high burdens. We conducted an ordinary least square regression model, spatial lag model, and spatial error model. The best-fitting model was selected via model parameters. Throughout the study period, the provinces in West Marmara Region (Edirne, Kırklareli, Tekirdağ, Çanakkale) were consistently in a high-burden cluster. In univariate ordinary least square regression, population density, the proportion of contacts screened for TB, the proportion of TB contacts who received prophylaxis, TB dispensary count, mean particulate matter 10 levels, and gross domestic product were found to be positively associated with TB notification rate. The best-fitting multivariate spatial lag model revealed that the proportion of contacts screened for TB (β, z-value: 0.89, 2.21) positively affected TB notification rate. The high TB burden in West Marmara Region should warn policymakers to maintain a focused approach to controlling TB in this area. This study showed the importance of contact tracing efforts to prevent the underdetection of TB cases.

Research on the fine-scale spatial-temporal evolution characteristics of carbon emissions based on nighttime light data: A case study of Xi'an city

Carbon emissions have resulted in severe ecological damage, jeopardizing the very existence of humanity. In China, carbon emissions from urban areas contribute to 80% of the total carbon emissions, this significant contribution underscores the importance of addressing urban emissions. To gain insights into the spatial patterns of carbon emissions in urban areas at the fine-scale regional level, this study utilizes Xi'an as a case study, constructs a fine-scale regional carbon emission estimation model based on energy consumption and NPP-VIIRS-like NTL data, and quantifies multiscale carbon emissions from 2000 to 2021. Furthermore, global spatial autocorrelation, cold and hot spot analysis, and standard deviation ellipse analysis were used to investigate the spatial characteristics of carbon emissions at the street (township) level in Xi'an. This study revealed a sixfold increase in carbon emissions in Xi'an, rising from 51.35 million tons in 2000 to 281.95 million tons in 2021.The area affected by carbon emissions has expanded, with the centre of gravity of emission consistently shifting towards the south. The average street-level carbon emission density fluctuated and doubled compared to that in 2000. The total number of streets with low-carbon-emission-density decreased by 23%, indicating a shift away from low-carbon-emission-density streets. Furthermore, the global Moran's I index consistently remained within the range of 0.85–0.95, indicating significant spatial clustering, with cold spots expanding into surrounding districts and counties. Additionally, this study accurately identified carbon emissions within the city, offering the potential for the development of more precise carbon reduction strategies. Moreover, this approach can provide valuable support for the dynamic monitoring of carbon emissions in developing cities worldwide.

Spatial variation and geographical weighted regression analysis to explore open defecation practice and its determinants among households in Ethiopia.

In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia. This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran's-I, and the local spatial autocorrelation was presented by Anselin Local Moran's-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD. The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran's I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran's I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased. The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. Wealso aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

Demand Prediction and Influencing Factors Analysis of Shared Bicycles near Public Transportation Stations based on GCN

Traffic prediction is a pivotal technology in the development of intelligent transportation systems. Real-time and accurate traffic prediction holds significance in route planning, resource allocation, and improving travel efficiency. To enhance the convenience of shared bicycle travel near public transportation hubs and optimize shared bicycle deployment strategies, this paper proposes the utilization of a graph convolutional neural network prediction model. Initially, shared bicycle travel data is scrutinized for spatiotemporal characteristics. Subsequently, an adjacency matrix is constructed based on the spatial attributes of the data to establish the graph convolutional network. Lastly, global spatial autocorrelation analysis and a coupled coordination model are integrated for validation and augmentation. This model is employed to predict shared bicycle travel patterns near public transportation hubs in the Xiamen Island area. Experimental results demonstrate a high level of precision, underscoring the model's effectiveness in providing valuable guidance for shared bicycle travel. The effectiveness of this method can be further verified in subsequent empirical studies.

Epidemiological characteristics and spatio-temporal aggregation of severe fever with thrombocytopenia syndrome in Jinan City, China, 2018-2022.

Severe fever with thrombocytopenia syndrome (SFTS) has become a significant public health issue in Jinan City. However, the analysis of epidemiological characteristics and spatio-temporal clustering of SFTS in Jinan has not been studied yet. SFTS data from 2018-2022 in Jinan City were obtained from the China Information System for Disease Control and Prevention. Global spatial autocorrelation and local spatial autocorrelation analyses were performed using ArcGIS 10.2 software, and spatiotemporal hotspot area detection was carried out using SatScan 9.6 software. Between 2018 and 2022, 680 SFTS cases were reported in Jinan City, resulting in 53 deaths and an average case fatality rate of 7.8%. 99.0% of cases occurred between April and October, 91.9% individuals were over 50 years old, and 87.79% were primarily farmers. A positive spatial correlation of SFTS in Jinan was observed (Moran's I value between 0.135-0.197, P<0.001), indicating spatial aggregation, primarily in Licheng, Zhangqiu, Laiwu, and Gangcheng districts in southeastern Jinan. Spatiotemporal scanning detected one class I and two class II aggregation areas, with the class I aggregation area (RR = 5.66, LLR = 192.547, P<0.001) locating in southeastern Jinan City, comprising 31 towns/streets, and an aggregation time from 13 May 2020 to 13 October 2022. Spatial and temporal aggregation of SFTS is evident in Jinan. Based on the spatial and temporal distribution and epidemiological characteristics, prevention and control measures such as public education, monitoring, and training should target key populations in high-incidence epidemic areas.