BACKGROUND: There has been extensive research conducted on open defecation in Ethiopia, but a notable gap persists in comprehensively understanding the spatial variation and predictors at the household level. This study utilizes data from the 2021 Performance Monitoring for Action Ethiopia (PMA-ET) to address this gap by identifying hotspots and predictors of open defecation. Employing geographically weighted regression analysis, it goes beyond traditional models to account for spatial heterogeneity, offering a nuanced understanding of geographical variations in open defecation prevalence and its determinants. This research pinpoints hotspot areas and significant predictors, aiding policymakers and practitioners in tailoring interventions effectively. It not only fills the knowledge gap in Ethiopia but also informs global sanitation initiatives.
METHODS: The study comprised a total weighted sample of 24,747 household participants. ArcGIS version 10.7 and SaT Scan version 9.6 were used to handle mapping, hotspots, ordinary least squares, Bernoulli model analysis, and Spatial regression. Bernoulli-based model was used to analyze the purely spatial cluster detection of open defecation at the household level in Ethiopia. Ordinary Least Square (OLS) analysis and geographically weighted regression analysis were employed to assess the association between an open defecation and explanatory variables.
RESULTS: The spatial distribution of open defecation at the household level exhibited clustering (global Moran\'s I index value of 4.540385, coupled with a p-value of less than 0.001), with significant hotspots identified in Amhara, Afar, Harari, and parts of Dire Dawa. Spatial analysis using Kuldorff\'s Scan identified six clusters, with four showing statistical significance (P-value < 0.05) in Amhara, Afar, Harari, Tigray, and southwest Ethiopia. In the geographically weighted regression model, being male [coefficient = 0.87, P-value < 0.05] and having no media exposure (not watching TV or listening to the radio) [coefficient = 0.47, P-value < 0.05] emerged as statistically significant predictors of household-level open defecation in Ethiopia.
CONCLUSIONS: The study revealed that open defecation at the household level in Ethiopia varies across the regions, with significant hotspots identified in Amhara, Afar, Harari, and parts of Dire Dawa. Geographically weighted regression analysis highlights male participants lacking media exposure as substantial predictors of open defecation. Targeted interventions in Ethiopia should improve media exposure among males in hotspot regions, tailored sanitation programs, and region-specific awareness campaigns. Collaboration with local communities is crucial.

BACKGROUND: Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales.
METHODS: Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients.
RESULTS: Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access).
CONCLUSIONS: The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations.

Exploring the role of landscape patterns in the trade-offs/synergies among ecosystem services (ESs) is helpful for understanding ES generation and transmission processes and is of great significance for multiple ES management. However, few studies have addressed the potential spatial-temporal heterogeneity in the influence of landscape patterns on trade-offs/synergies among ESs. This study assessed the landscape patterns and five typical ESs (water retention (WR), food supply (FS), habitat quality (HQ), soil retention (SR), and landscape aesthetics (LA)) on the Loess Plateau of northern Shaanxi and used the revised trade-off/synergy degree indicator to measure trade-offs/synergies among ESs. The multiscale geographically weighted regression (MGWR) model was constructed to determine the spatial-temporal heterogeneity in the influence of landscape patterns on the trade-offs/synergies. The results showed that (1) from 2000 to 2010, the increase in cultivated land and the decrease in forestland and grassland increased landscape diversity and decreased landscape heterogeneity and fragmentation. During 2010-2020, the change range decreased, the spatial distribution was homogeneous, and the landscape diversity and fragmentation in the northwestern area increased significantly. (2) The supply of the five ESs continued to increase from 2000 to 2020. During 2000-2010, FS-SR, FS-LA and SR-LA were dominated by synergies. From 2010 to 2020, the proportion of trade-off units in all relationships increased, and HQ-FS, HQ-SR and HQ-LA were dominated by trade-offs. (3) Landscape patterns had complex impacts on trade-offs/synergies, and the same landscape variable could have the opposite impact on specific trade-offs/synergies in different periods and areas. The results of this study will inform managers in developing regional sustainable ecosystem management strategies and advocating for more research to address ecological issues from a spatial-temporal perspective.

Little is known about spatial variability of hospitalisation rate (HR) of patients with rheumatoid arthritis (RA) worldwide, especially in China.
A cross-sectional study was conducted among patients with RA admitted to hospitals in Hunan Province. Global Moran\'s I and local indicators of spatial association were used to explore the geospatial pattern of the HR of patients with RA. Generalised estimating equation analysis and geographically weighted regression were used to identify the potential influencing factors of the HR of patients with RA.
There were a total of 11 599 admissions, and the average HR was 1.57 per 10 000 population in Hunan. We detected different cluster patterns of the HR among patients with RA by local indicators of spatial association. Age, ethnicity, average temperature, average temperature range, average rainfall, regions, gross domestic product per capita, and doctors and hospitals per 10 000 people were risk factors for the HR. However, only average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. The increase in hospitals increased the probability of HR from east to west in Hunan with a positive coefficient, while temperature decreases increased the risk of HR from south to north negatively. Similarly, the growth of gross domestic product per capita decreased the probability of HR from southwest to northeast.
A non-random spatial distribution of the HR of patients with RA was demonstrated in Hunan, and average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. Our study indicated that spatial and geostatistics may be useful approaches for further study among patients with RA.

OBJECTIVE: Walking practice is a representative indicator of the level of physical activity of local residents. Although the world health organization addressed reduction in prevalence of insufficient physical activity as a global target, the rate of walking practice in Korea has not improved and there are large regional disparities. Therefore, this study aimed to explore the spatial variations of walking practice and its associated factors in Korea.
METHODS: A secondary analysis was conducted using Community Health Outcome and Health Determinants Database 1.3 from Korea Centers for Disease Control and Prevention. A total of 229 districts was included in the analysis. We compared the ordinary least squares (OLS) and the geographically weighted regression (GWR) to explore the associated factors of walking practice. MGWR 2.2.1 software was used to explore the spatial distribution of walking practice and modeling the GWR.
RESULTS: Walking practice had spatial variations across the country. The results showed that the GWR model had better accommodation of spatial autocorrelation than the OLS model. The GWR results indicated that different predictors of walking practice across regions of Korea.
CONCLUSIONS: The findings of this study may provide insight to nursing researchers, health professionals, and policy makers in planning health programs to promote walking practices in their respective communities.

Boosting the coordination and symbiosis of urbanization and forest ecological security is notably critical for promoting regional green and sustainable development and achieving emission peak and carbon neutrality goals. However, there was still a lack of in-depth analysis of the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism. On the basis of the data from 844 counties in the Yangtze River Economic Belt, this paper explored the spatial differences and influencing factors of the coupling coordination degree of urbanization and forest ecological security. The results manifested that: i) There were apparent spatial disparities in the urbanization index, forest ecological security index, comprehensive index, coupling degree and coupling coordination degree of the Yangtze River Economic Belt. Among them, the spatial pattern of coupling coordination degree had a strong consistency with urbanization index, that is, areas with higher urbanization index also had higher coupling coordination degree. ii) Based on coupling feature identification, it was found that 249 \'problem areas\' were mainly located in Yunnan Province, southeastern Guizhou Province, central Anhui Province, and central and eastern Jiangsu Province. The main factor for the formation was due to the lag of urbanization in coordinated development. iii) Among the socioeconomic indicators, population structure (0.136), per capita year-end financial institutions loan balance (0.409) and per capita fixed asset investment (0.202) all had a positive impact on coupling coordination degree, while location conditions (-0.126) had a negative impact. Among the natural indicators, soil organic matter (-0.212) and temperature (-0.094) had a negative impact on coupling coordination degree. iv) During the process of coordinated development, it was necessary to increase financial investment and financial support, actively formulate policies to attract talents, enhance the education and publicity of ecological civilization, and develop a green circular economy. The above measures can promote the harmonious development of urbanization and forest ecological security in the Yangtze River Economic Belt.

Determining bike-sharing usage patterns and their explanatory factors on demand is essential for the effective and efficient operation of bike-sharing systems (BSSs). Most BSSs provide different passes that vary with the period of use. However, studies investigating the differences in usage patterns are rare compared to studies conducted at the system level, even though explanatory factors depending on the type of pass may cause different characteristics in terms of usage patterns. This study explores the differences in the usage patterns of BSSs and the impact of explanatory factors on the demand depending on the type of pass. Various machine learning techniques, including clustering, regression, and classification, are used, in addition to basic statistical analysis. As observed, long-term season passes of over six months are mainly used for transportation (especially commuting), whereas one-day or short-term season passes seem to be used more for leisure than for other purposes. Furthermore, differences in the purpose of bike rentals seem to cause differences in usage patterns and variations in demand over time and space. This study improves ther understanding of the usage patterns that appear differently for each pass type, and provides insights into the efficient operation of BSSs in urban areas.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11116-023-10371-7.

As the threat to human life and health from fine particulate matter (PM2.5) increases globally, the life and health problems caused by environmental pollution are also of increasing concern. Understanding past trends in PM2.5 and exploring the drivers of PM2.5 are important tools for addressing the life-threatening health problems caused by PM2.5. In this study, we calculated the change in annual average global PM2.5 concentrations from 2000 to 2020 using the Theil-Sen median trend analysis method and reveal spatial and temporal trends in PM2.5 concentrations over twenty-one years. The qualitative and quantitative effects of different drivers on PM2.5 concentrations in 2020 were explored from natural and socioeconomic perspectives using a multi-scale geographically weighted regression model. The results show that there is significant spatial heterogeneity in trends in PM2.5 concentration, with significant decreases in PM2.5 concentrations mainly in developed regions, such as the United States, Canada, Japan and the European Union countries, and conversely, significant increases in PM2.5 in developing regions, such as Africa, the Middle East and India. In addition, in regions with more advanced science and technology and urban management, PM2.5 concentrations are more evenly influenced by various factors, with a more negative influence. In contrast, regions at the rapid development stage usually continue their economic development at the cost of the environment, and under a high intensity of human activity. Increased temperature is known as the most important factor for the increase in PM2.5 concentration, while an increase in NDVI can play an important role in the reduction in PM2.5 concentration. This suggests that countries can achieve good air quality goals by setting a reasonable development path.

A healthy ecosystem is fundamental for sustainable urban development. Rapid urbanization has altered landscape patterns and ecological functions, resulting in disturbances to ecosystem health. Exploring the effects of urbanization on ecosystem health and the spatial relationships between them is significant for cities along the \"Belt and Road\" aiming to achieve sustainable regional development. This study took the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as an example and measured the urbanization level (UL) and ecosystem health index (EHI) from 2000 to 2020 using multisource data. We used bivariate spatial autocorrelation, the geographically weighted regression model (GWR), and the optimal parameters-based geographical detector (OPGD) model to clarify the impact of urbanization on ecosystem health and the spatial relationship between them from multiple perspectives. The major findings of this study were: (1) the EHI in the GBA decreased significantly during the study period, dropping from 0.282 to 0.255, whereas the UL increased significantly, exhibiting opposite spatial distribution features; (2) there was a significant negative spatial correlation between UL and the EHI and significant spatial heterogeneity between high-low and low-high types in the GBA; (3) the negative effects of urbanization on ecosystem health were predominant and becoming more pronounced in the central GBA. Moreover, urbanization had an increasingly significant negative effect, leading to the deterioration of ecosystem health, in the central GBA. Population urbanization drove land urbanization, which became the main factor affecting ecosystem health in the GBA. Overall, urbanization had a significant negative effect on ecosystem health, with this impact being particularly prominent in the core urban junctions of the GBA, which require urgent attention. The results of the study provide a basis for decision making in the context of the steady urbanization and ecosystem health protection of cities along the \"Belt and Road\".

Worldwide, policies to combat human immunodeficiency virus (HIV) have led to a small decrease in the acquired immunodeficiency syndrome (AIDS) mortality rate among young people. For greater policy efficacy, it is necessary to determine the influence of social determinants of health (SDHs) in each territory. The objective of this study was to spatially analyse the AIDS mortality rate among young people in a province of the Brazilian Amazon and the spatial variability of the rate promoted by SDHs. All reports of AIDS deaths between 2007 and 2018 among young people living in the state of Pará were included in the study. The mortality rate was analysed using spatial distribution and autocorrelation, spatial scanning, and geographically weighted regression (GWR). During the study period, there were 1,372 deaths from AIDS among young people with a territorial expansion. The spatial autocorrelation showed two high-high clusters in the period from 2007 to 2010, one formed by municipalities in south-eastern Pará and one in the metropolitan region of Belém, with only the latter remaining between 2011 and 2018. This region showed a higher spatial risk for AIDS mortality and was the only cluster with spatiotemporal risk in the 2013-2018 period. Spatial variability was promoted by the i) the youth homicide rate, ii) the elementary school dropout rate and iii) the number of families registered in the Unified Registry for Social Programs (CadÚnico). This study provides support for the implementation of effective focal policies to combat HIV and reduce the mortality rate among young people in Pará.