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Using social disorganization theory, the present study considers the concentration of racial minorities and the degree of ethnic heterogeneity to provide an estimation of homicide rates in the Northeast region of Brazil. Three hypotheses were considered: (1) there is a positive association between homicides and ethnic heterogeneity and black/indigenous populations, (2) the magnitude of ethnic heterogeneity is greater than race/minority groups on homicides, and (3) metropolitan regions will be associated with an increase in homicides. Through spatial modelling techniques, results indicate the plausibility of first and third hypotheses. Implications for homicide prevention and social disorganization theory in global south are discussed. [ABSTRACT FROM AUTHOR]

Quantifying the effects of environmental factors on soil organic carbon and spatial distribution is fundamental to soil quality regulation, restoration, and response to climate change. The present study aims to explore the spatial distribution characteristics of the soil organic carbon (SOC) contents in Anhui Province, China, based on national soil data. In addition, we used the geographically weighted regression (GWR) model to quantify the influence degrees of environmental factors on the soil organic carbon density (SOCD). The results showed that the spatial distribution of SOCD in Anhui Province in both 1985 and 2018 was characterized by high in the south and low in the north. The GWR model prediction results of the 0–30 cm SOCD showed local coefficients of determination (local R2) ranging from 0.21 to 0.96 and 0.14 to 0.96 in 1985 and 2018, respectively. Therefore, the predicted results were effective in evaluating the overall spatial distribution of the SOCD in Anhui Province. The regression coefficients of the normalized difference vegetation index (NDVI) and air temperature ranged from − 0.39 to 5.67 and − 0.17 to 3.11, respectively, demonstrating their strong controlling effects on the spatiotemporal variations in the 0–30 cm SOCD in Anhui Province. [ABSTRACT FROM AUTHOR]

The conservation and sustainable development of traditional villages have raised global attention in the context of rapid urbanization and modernization. Taking 703 traditional villages in Hunan Province as an example, this study first constructed a Traditional Village Human Settlement Suitability (TVHSS) evaluation system based on the Pressure-State-Response (PSR) model. Then, the entropy weighting method was used to assess the spatiotemporal evolution of TVHSS from 2005 to 2020, while the Geographically Weighted Regression (GWR) model was employed to analyze the influencing factors. The results indicate that the overall TVHSS score increased from 0.521 to 0.776 from 2005 to 2020, with a spatial distribution characterized by lower suitability in the northwest and higher suitability in the southeast. During this period, the pressure subsystem experienced an increase, peaking at 0.058 in 2015 before declining to 0.055 in 2020. Meanwhile, the state subsystem remained relatively stable, with scores slightly decreasing from 0.040 in 2005 to 0.033 in 2020. In contrast, the response subsystem showed a continuous upward trend, rising from 0.430 in 2005 to 0.688 in 2020. The distance to educational institutions, degree of relief, distance to water, and distance on intangible cultural heritage sites have the highest effects on TVHSS. These findings provide a scientific basis for the conservation and sustainable development of traditional villages and offer a replicable analytical framework for similar contexts globally. By addressing the complex interactions between environmental, social, and economic factors, this study contributes to the global discourse on rural sustainability, offering insights that can inform policy-making and guide the preservation of cultural heritage in the face of modernization pressures. [ABSTRACT FROM AUTHOR]

ABSTRACT In mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions. [ABSTRACT FROM AUTHOR]

Background: In large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power. Method: We propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey. Results: Our simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates. Conclusion: Our proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases. [ABSTRACT FROM AUTHOR]

We quantified the impact of transit-oriented development (TOD) on consumer expenditures in seven sectors, including retail, healthcare, culture, and education, in Seoul, South Korea, employing propensity score matching, ordinary least square regression, and geographically weighted regression (GWR). Our findings revealed that TOD significantly increases consumer expenditures, with sectoral effects ranging between 20.1 and 21.3%. Crucially, the GWR analysis highlights the spatially dependent nature of TOD impact, uncovering substantial local variations. Districts such as Gangnam, Songpa, Gangdong, and Gangseo-gu exhibit pronounced positive effects, with consumer spending increases exceeding 88.3%, indicating the potential of TOD as a catalyst for economic growth in these strategic areas. Conversely, areas such as Dobong, Seodaemun, and Geumcheon-gu show marginally negative effects, suggesting that TOD benefits are not uniformly distributed and may pose challenges in certain contexts. This study contributes to the literature by providing empirical evidence of the economic impact of TOD across diverse sectors and offering valuable insights for transportation and urban planning, emphasizing the need for context-sensitive approaches to maximize TOD outcomes. [ABSTRACT FROM AUTHOR]

Managing the amount of greenspace (i.e., increasing or decreasing greenspace coverage) and optimizing greenspace configuration (i.e., increasing or decreasing greenspace fragmentation) are cost-effective approaches to cooling the environment. The spatial variations in their impacts on the thermal environment, as well as their relative importance, are of great importance for greenspace planning and management but are far from thoroughly understood. Taking Changsha, China as an example, this study investigated the spatial variations of the impacts of greenspace amount (measured as a percent of greenspace) and greenspace fragmentation (measured by edge density of greenspace) on the Landsat-derived land surface temperature (LST) using geographically weighted regression (GWR), and also uncovered the spatial pattern of their relative importance. The results indicated that: (1) Greenspace amount showed significantly negative relationships with LST for 91.73% of the study area. (2) Both significantly positive and negative relationships were obtained between greenspace fragmentation and LST, covering 14.90% and 13.99% of the study area, respectively. (3) The negative relationship between greenspace fragmentation and LST is mainly located in the urban areas, while the positive relationship appeared in the rural areas. (4) Greenspace amount made a larger contribution to regulating LST than greenspace fragmentation in 93.31% of the study area, but the latter had stronger roles in about 6.69% of the study area, mainly in the city center. These findings suggest that spatially varied greenspace planning and management strategies should be adopted to improve the thermal environment. [ABSTRACT FROM AUTHOR]

This study investigates the roles of the socio-economic, land use, built environment, and weather factors in shaping up the demand for bicycle-sharing trips during the COVID-19 pandemic in Toronto. It uses "Bike Share Toronto" ridership data of 2019 and 2020 and a two-stage methodology. First, multilevel modelling is used to analyze how the factors affect monthly station-level trip generation during the pandemic compared to pre-pandemic period. Then, a geographically weighted regression analysis is performed to better understand how the relationships vary by communities and regions. The study results indicate that the demand of the service for commuting decreased, and the demand for recreational and maintenance trips increased significantly during the pandemic. In addition, higher-income neighborhoods are found to generate fewer weekday trips, whereas neighbourhoods with more immigrants experienced an increase in bike-share ridership during the pandemic. Moreover, the pandemic trip generation rates are more sensitive to the availability of bicycle facilities within station buffers than pre-pandemic rates. The results also suggest significant spatial heterogeneity in terms of the level of influence of the explanatory factors on the demand for bicycle-sharing during the pandemic. Based on the findings, some neighbourhood-specific policy recommendations are made, which inform decisions regarding the locations and capacity of new stations and the management of existing stations so that equity concerns about the usage of the system are adequately accounted for. [ABSTRACT FROM AUTHOR]

Vacant land stands as a ubiquitous feature within our urban landscapes, exerting significant influence on local environmental, social, and economic systems, thus affecting neighborhood sustainability. However, existing studies often produce conflicting results regarding the impact of vacant land. This inconsistency largely stems from the assumption that the associations between vacant land and neighborhood sustainability remain stationary across space, overlooking potential spatial variability. To address this gap, this study uses Chicago as the sample and investigates spatial variations in the associations between vacant land and neighborhood sustainability, with a focus on the discrepancies between disadvantaged and non-disadvantaged neighborhoods. The results reveal substantial spatial variations in these associations, primarily influenced by neighborhood socioeconomic patterns, urban structure, and local sustainability levels. Consequently, this study underscores the need for spatially adaptive strategies to address the vacant land issue and foster sustainable neighborhood development. Furthermore, this study advocates repurposing vacant land in disadvantaged neighborhoods to promote social equity and advance citywide sustainable development goals. [ABSTRACT FROM AUTHOR]

Immunization is the process of building immunity or resistance to an infectious disease, typically through administering a vaccine. It is one of the most effective strategies for lowering child morbidity and death. It protects against more than 20 potentially fatal diseases, increasing longevity and health. Despite progress, Ethiopia failed to meet its vaccination coverage target. The magnitude of full immunization is different across areas. Therefore, conducting geographically weighted regression to identify the local factors and multilevel analysis to investigate and identify factors associated with full immunization coverage among children aged 12–23 months is necessary. The study was conducted using the 2019 Ethiopian Mini Demographic Health Survey dataset. A sample of 1028 weighted children aged 12–23 months were included in the analysis. Descriptive statistics were used to describe variables. For the spatial analysis, Arc-GIS version 10.8 statistical software was used. Spatial regression (geographically weighted regression) was done to identify factors associated with the proportion of full immunization, and model comparison was based on adjusted R2 and Akaike Information Criteria (AICc). Multilevel mixed-effect binary logistic regression models were fitted to identify factors associated with full immunization. The fitted models were compared based on log-likelihood, deviance, median odds ratio, and Proportional Change in Variance. Finally, statistically significant factors were reported using an adjusted odd ratio (AOR) with a 95% Confidence Interval for fixed effect. All variables with a p-value less than 0.05 in the final model were considered statistically significant factors. In Ethiopia, the overall full immunization coverage among children aged 12–23 months was 40.58%, with spatial variation across regions in Ethiopia. The significant spatial distribution of full immunization coverage among children aged 12–23 months was detected in northern Tigray, Addis Ababa, central Oromia, and southeastern Amhara regions. The proportion of rural residents,the proportion of women aged 35–44 years, the proportion of women who had ANC 4 and above andthe proportion of women who had PNC were local factors associated with the proportion of full immunization among children aged 12–23 months. Rural residence [AOR 0.27 (95% CI 0.10, 0.70)], family size 4 and above[AOR 0.41 (95% CI 0.17, 0.96)], never breastfeed [AOR 0.026(95% CI 0.003, 0.21)], 1–3 times ANC visit [AOR 0.45 (95% CI 0.23, 0.86)], being from Oromia region [AOR 0.23 (95% CI 0.05, 0.97)], Eastern pastoralist region [AOR 0.09 (95% CI 0.023, 0.35)], age 35–44 years [(AOR 6 (95% CI 1.57, 22.9)], and PNC [AOR 2.40 (95% CI 1.24, 4.8)] were significant factors associated with fully immunization in multilevel mixed effect analysis. Full immunization coverage in Ethiopia is below the global target with significant geographical variation. The high proportion of rural residents, the high proportion of women who had ANC 4 and above, mothers who had a high proportion of PNC, and the high proportion women age 35-44 years were local geographical factors for the proportion of full immunization among children age 12–23 months in Ethiopia. Women who had PNC, ANC visits four or more times, and increased maternal age were positively associated, whereas larger family size, no breastfeeding, rural residence, and being from Oromia and eastern pastoralist region were negatively associated with full immunization. Strengthening maternal and child health services, focusing on rural areas and low-coverage regions, is essential to increase immunization coverage in Ethiopia. [ABSTRACT FROM AUTHOR]

Streets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York's Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention. [ABSTRACT FROM AUTHOR]

Determining the factors that influence poverty has been seen as one of the ways of identifying appropriate poverty alleviation strategies in cities. This has been done, in most cases, through the application of ordinary least squares (OLS), which assumes homogeneous influence without considering the spatial heterogeneity that may occur in this influence. This study aims to determine the spatial heterogeneity in the influence of public infrastructure, economic, environmental, and social factors on poverty, by carrying out comparison analysis using OLS and Geographically Weighted Regression (GWR) in ArcGIS Pro 3.0.1, with a view of proffering poverty alleviation strategies for each section of an urban area. A questionnaire in Geographic Open Data Kit (GeoODK) was administered to 366 urban residents across the twenty wards in the selected city in Nigeria. The results reveal that factors that influence income poverty are spatially varying in direction and weight across a city. Spatial heterogeneity of poverty’s determinants should be considered in the formulation and implementation of effective poverty alleviation policies and programs. [ABSTRACT FROM AUTHOR]

In survey sampling, auxiliary information is used to precisely estimate the finite population parameters. There are several approaches available in the literature that provide a practical method for incorporating auxiliary information during the estimation stage. In order to effectively utilize the auxiliary information, a geographically weighted regression (GWR) model-assisted integrated estimator of finite population total under a two-phase sampling design has been proposed in this article. Spatial simulation studies have been conducted to empirically assess the statistical properties of the proposed estimator. In the presence of spatial non-stationarity, empirical findings reveal that the proposed estimator outperforms all existing estimators such as two-phase HT, ratio, and regression estimators, demonstrating the importance of spatial information in survey sampling. [ABSTRACT FROM AUTHOR]

Identifying ecosystem service functions, clarifying the spatiotemporal trade-offs and synergies of terrestrial ecosystem services and their driving mechanisms, and exploring differentiated ecological functional zoning are crucial steps in achieving healthy regional ecosystem management, and are conducive to developing diversified ecological restoration strategies, establishing a robust cross-regional collaborative management mechanism, implementing differentiated ecological management strategies, and contributing to the construction of a Beautiful China. This paper, based on the InVEST model, Pearson correlation, GeoDetector, and Spatiotemporal Geographically Weighted Regression models, constructs a spatial quantification model of the trade-offs and synergies among five key ecosystem service functions -- habitat quality, soil retention, water conservation, food supply, and carbon sequestration -- of China's terrestrial ecosystems from 2000 to 2020. It explores the influencing factors of terrestrial ecosystem services in China and their spatiotemporal heterogeneity, thereby investigating the future strategies for ecological functional zoning and management of China's national land space. The results indicate that: (1) during 2000-2020, China's food supply and soil conservation have increased. However, the habitat quality, water conservation, and carbon sequestration have decreased. (2) Significant spatial and temporal heterogeneities exist in the key ecosystem services of China's terrestrial ecosystems. (3) Natural, economic, and social factors all impact China's terrestrial key ecosystem services. Among them, slope, annual average precipitation, land development intensity, and vegetation coverage are the main influencing factors, and different factors exhibit significant spatial heterogeneity. (4) Significant trade-offs/synergy effects among critical terrestrial ecosystem services exist in China. (5) China's national territory is divided into four ecological protection functional zones: ecological restoration areas, ecological control areas, resilient development areas, and ecological conservation areas, and explores differentiated zoning optimization control paths. [ABSTRACT FROM AUTHOR]

Background: Suicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates in South Korea and evaluated the impact of community factors on suicide. Methods: Suicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 2021 or the nearest year were collected from the Korean Statistical Information Service. The spatial autocorrelation of suicide by sex and age was examined based on Global Moran's I index. Geographically weighted regression (GWR) was used to discern the influence of community determinants on suicide. Results: Suicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial dependence, as indicated by a high global Moran's I with p < 0.001, highlighting the importance of conducting spatial analysis. In the GWR model calibration, a subset of the community's age structure, single-person household composition, access to mental healthcare centers, and unmet medical needs were selected to explain male suicide mortality. These determinants disproportionately increased the risk of male suicide, varying by region. The GWR coefficients of each variable vary widely across 249 districts: aging index (Q1:0.06–Q3:0.46), single-person households (Q1:0.22–Q3:0.35), psychiatric clinics (Q1:-0.20–Q3:-0.01), and unmet medical needs (Q1:0.09–Q3:0.14). Conclusions: Community cultural and structural factors exacerbate regional disparities in suicide among men. The influencing factors exhibit differential effects and significance depending on the community, highlighting the need for efficient resource allocation for suicide. A regionally tailored approach is crucial for the effective control of the community's mental health management system. [ABSTRACT FROM AUTHOR]

This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly and Disability, Housing Density and Vehicle Access, Youth and Mobile Housing, and Group Quarters and Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including the Mann–Whitney U test, indicated significant differences in flood risk and social vulnerability between urban and rural areas. Spatial cluster analysis using Local Indicators of Spatial Association (LISA) revealed significant high flood risk and social vulnerability clusters, particularly in urban regions along the Gulf Coast, Atlantic Seaboard, and Mississippi River. Global and local regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), highlighted social vulnerability's spatial variability and localized impacts on flood risk. The results showed substantial regional disparities, with urban areas exhibiting higher flood risks and social vulnerability, especially in southeastern urban centers. The analysis also revealed that Socioeconomic Disadvantage, Group Quarters and Unemployment, and Housing Density and Vehicle Access are closely related to flood risk in urban areas, while in rural areas, the relationship between flood risk and factors such as Elderly and Disability and Youth and Mobile Housing is more pronounced. This study underscores the necessity for targeted, region-specific strategies to mitigate flood risks and enhance resilience, particularly in areas where high flood risk and social vulnerability converge. These findings provide critical insights for policymakers and planners aiming to address environmental justice and promote equitable flood risk management across diverse geographic settings. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) has become an emerging public health problem. The impact of multiple environmental factors on the prevalence of ASD remains unclear. This study examined the association between the prevalence of ASD and the environmental quality index (EQI), an indicator of cumulative environmental quality in five major domains, including air, water, land, built and sociodemographic variables in the United States. The results from Poisson regression models show that the prevalence of ASD has a positive association with the overall EQI with a risk ratio (RR) of 1.03 and 95% confidence intervals (CI) of 1.01–1.06, indicating that children in counties with poor environmental quality might have a higher risk of ASD. Additionally, the prevalence of ASD has a positive association with the air index (RR = 1.04, 95% CI: 1.01–1.06). These associations varied in different rural–urban groups and different climate regions. This study provided evidence for adverse effects of poor environmental quality, particularly air pollutants, on children's neurodevelopment. [ABSTRACT FROM AUTHOR]

The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group. [ABSTRACT FROM AUTHOR]

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0–10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management. [ABSTRACT FROM AUTHOR]

Water supply continuity is a crucial indicator of service efficiency. Multiple South Asian cities have Intermittent Water Supply (IWS). Water supply equity in cities depends on the spatial patterns of intermittency. The present study used Kernel Density Estimation (KDE) to investigate the spatial pattern of water supply intermittency for 3071 localities of Hyderabad, India. Further, the relationship between population density and supply continuity has been examined in 146 wards of Hyderabad using Spatial Autocorrelation and Geographically Weighted Regression (GWR). Spatial Autocorrelation defines two distinct clusters of central wards for high continuity (12 wards) and population density (19 wards). However, the ward clusters are different for continuity and density, with only three central wards (2.06% of all wards) being common in both the high-high clusters. The results highlight that the water supply continuity in Hyderabad has minimal consideration for population density, resulting in a spatially inequitable water supply. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) changes driven by ecological restoration and protection projects play a pivotal role in reshaping landscape patterns. However, the specific impacts of these projects on landscape structure remain understudied. In this research, we applied geographically weighted regression (GWR) to analyze the spatial relationships between typical land use expansion and landscape pattern characteristics in the Lesser Khingan Mountains–Sanjiang Plain region between 2017 and 2022. Our results indicate three key findings: (1) Significant spatial heterogeneity exists in the relationship between landscape patterns and land use expansion, which varies across geographic locations; (2) Ecological restoration projects generally reduce fragmentation, dominance, and heterogeneity while enhancing connectivity, particularly in forest and farmland regions. However, excessive land use expansion in certain areas may reverse these positive effects; (3) Landscape complexity increases in high-altitude mountainous regions due to land use expansion but decreases in plains, particularly in forest-to-farmland conversions. These findings provide new insights into how landscape patterns respond to ecological restoration efforts and offer actionable guidance for improving future land use planning and policy decisions. Our study highlights the need to consider local geomorphological factors when designing ecological projects, ensuring that restoration efforts align with regional landscape dynamics to maintain landscape integrity. [ABSTRACT FROM AUTHOR]

Featured Application: (1) The methodological approach based on multiscale geographically weighted regression provides a nuanced understanding of how urban factors influence health disparities. (2) The findings highlight the importance of food accessibility as a determinant of health, reinforcing the need for interdisciplinary approaches that combine public health, urban planning, and nutrition science. (3) Policymakers can use this data to identify priority areas for improving food access, particularly in economically disadvantaged neighborhoods. This could involve incentivizing grocery stores and markets to establish themselves in underserved areas or enhancing public transportation links to existing food resources. (4) The call for sustainable food systems to support public health highlights the importance of long-term strategies that ensure consistent access to healthy, affordable food. This can influence agricultural policies, support for local food producers, and the promotion of urban agriculture initiatives. This study examines the link between food deserts and cardiovascular health in older adults in Santiago's Metropolitan Region, Chile. As the population ages and chronic diseases rise, understanding the impact of food accessibility on health is essential. Using multiscale geographically weighted regression, we analyzed data from the Cardiovascular Health Program, socioeconomic indicators, and food desert maps, sourced from the Chilean Ministry of Health and other databases. Spatial analysis, including Voronoi diagrams, assessed the influence of food deserts on health outcomes. Findings show a significant correlation between limited access to healthy foods and higher cardiovascular disease rates, especially in economically disadvantaged areas. The regression model is significant to contribute to the explanation of disease prevalence, emphasizing the impact of food availability on health. This study highlights the importance of considering spatial factors in urban planning and public health policies. By showcasing the role of food environments in health disparities, it advocates for integrated health interventions. Targeted urban planning to address food deserts can enhance access to healthy foods, improving cardiovascular health and well-being among Santiago's elderly. The findings provide insights for policymakers to create healthier urban environments and stress the need for sustainable food systems to support public health. [ABSTRACT FROM AUTHOR]

Although well-designed urban streets are beneficial for sustainability and livability, few studies have considered their role in housing price estimates. To fill this gap, this study conducted in Nanjing, China, aims to examine the contribution of streetscape features to housing prices. Data were collected for 2040 residential blocks within the four municipal districts in July 2021. A semantic segmentation approach was used to identify the percentage of elements in the images from Baidu Street View. Two types of streetscape related variables (Enclosure and Greenery) were calculated and added to a hedonic pricing model based on Geographically Weighted Regression. The results show that the streetscape factors all have positive effects on house prices, and the contribution to house prices from large to small is grass, plants, horizontal buildings, vertical buildings and trees. By comparing the parameters of the models, it can be concluded that the inclusion of streetscape features and consideration of spatial heterogeneity can significantly improve the accuracy of housing price estimation. The findings of the current study contribute to decision-making in housing planning and urban design and to judgments about pricing reasonableness. [ABSTRACT FROM AUTHOR]

Ecosystems offer natural resources and habitats for humans, serving as the foundation for human social development. Taking the Tianshan Mountains as the study area, this study investigated the changing trends, hot spots, and driving factors of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ), in the Tianshan region, from 1990 to 2020. To determine the trade-offs and synergies between the ESs, we employed the Spearman correlation coefficient, geographically weighted regression, the self-organizing map (SOM), and other methods. Five main results were obtained. (1) There were similar spatial distribution patterns for WY, HQ, CS, and SC, with high-value areas mainly concentrated in grassland zones, forest zones, river valleys, and the intermountain basins of the mountain range, while regions with low value were clustered in desert zones and snow/ice zones. (2) According to the hotspot analysis, areas with relatively strong ES provisioning for WY, HQ, CS, and SC, were primarily concentrated in the BoroHoro Ula Mountains and Yilianhabierga Mountains. In contrast, areas with relatively weak ES provisioning were mainly located in the Turpan Basin. (3) Precipitation was the primary explanatory factor for WY. Soil type, potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) were the primary explanatory factors for HQ. Soil type and NDVI were the primary explanatory factors for CS. PET was the primary explanatory factor for SC. (4) There were synergistic relationships between the WY, HQ, CS, and SC, with the strongest synergies found between CS–HQ, WY–HQ, and WY–SC. (5) Six ES bundles were identified through the SOM method, with their composition varying at different spatial scales, indicating the need for different ES management priorities in different regions. Our analysis of ESs, from various perspectives, offers insights to aid sustainable ecosystem management and conservation efforts in the Tianshan region and other major economic areas worldwide. [ABSTRACT FROM AUTHOR]

In the current context of urbanization, urban agglomerations face complex challenges in maintaining an ecological balance. This study uses remote sensing images of the Central Yunnan urban agglomeration from 2000 to 2020, along with socioeconomic data, to analyze the spatiotemporal characteristics of the green space evolution. Utilizing dynamic geographically weighted regression analysis based on principal components (PCA-GWR), we identify the key socioeconomic factors influencing these changes and quantitatively analyze the driving forces in each stage. Our findings reveal a continuing trend of decreasing total green space alongside increasing individual forest types and pronounced regional disparities in green space dynamics. The results indicate that socioeconomic factors exert both positive facilitative effects and negative pressures, with evident spatial and temporal variability. Urbanization and economic development promote forest expansion in certain areas, while contributing to the reduction in farmland and shrub–grass lands. Significant variations are influenced by factors such as the urbanization rate, the agricultural population, the industrial composition, and fiscal revenue. This study enhances the in-depth understanding of the relationship between the spatiotemporal dynamics of green spaces and socially driven mechanisms, offering significant insights for sustainable urban planning and landscape management and harmonizing urban agglomeration development. [ABSTRACT FROM AUTHOR]

Abstract The conservation and sustainable development of traditional villages have raised global attention in the context of rapid urbanization and modernization. Taking 703 traditional villages in Hunan Province as an example, this study first constructed a Traditional Village Human Settlement Suitability (TVHSS) evaluation system based on the Pressure-State-Response (PSR) model. Then, the entropy weighting method was used to assess the spatiotemporal evolution of TVHSS from 2005 to 2020, while the Geographically Weighted Regression (GWR) model was employed to analyze the influencing factors. The results indicate that the overall TVHSS score increased from 0.521 to 0.776 from 2005 to 2020, with a spatial distribution characterized by lower suitability in the northwest and higher suitability in the southeast. During this period, the pressure subsystem experienced an increase, peaking at 0.058 in 2015 before declining to 0.055 in 2020. Meanwhile, the state subsystem remained relatively stable, with scores slightly decreasing from 0.040 in 2005 to 0.033 in 2020. In contrast, the response subsystem showed a continuous upward trend, rising from 0.430 in 2005 to 0.688 in 2020. The distance to educational institutions, degree of relief, distance to water, and distance on intangible cultural heritage sites have the highest effects on TVHSS. These findings provide a scientific basis for the conservation and sustainable development of traditional villages and offer a replicable analytical framework for similar contexts globally. By addressing the complex interactions between environmental, social, and economic factors, this study contributes to the global discourse on rural sustainability, offering insights that can inform policy-making and guide the preservation of cultural heritage in the face of modernization pressures.

Abstract Background In large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power. Method We propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey. Results Our simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates. Conclusion Our proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.

Urban green and recreational spaces are important for residents and one of the foundations for the functioning of cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit. In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.

Abstract Immunization is the process of building immunity or resistance to an infectious disease, typically through administering a vaccine. It is one of the most effective strategies for lowering child morbidity and death. It protects against more than 20 potentially fatal diseases, increasing longevity and health. Despite progress, Ethiopia failed to meet its vaccination coverage target. The magnitude of full immunization is different across areas. Therefore, conducting geographically weighted regression to identify the local factors and multilevel analysis to investigate and identify factors associated with full immunization coverage among children aged 12–23 months is necessary. The study was conducted using the 2019 Ethiopian Mini Demographic Health Survey dataset. A sample of 1028 weighted children aged 12–23 months were included in the analysis. Descriptive statistics were used to describe variables. For the spatial analysis, Arc-GIS version 10.8 statistical software was used. Spatial regression (geographically weighted regression) was done to identify factors associated with the proportion of full immunization, and model comparison was based on adjusted R2 and Akaike Information Criteria (AICc). Multilevel mixed-effect binary logistic regression models were fitted to identify factors associated with full immunization. The fitted models were compared based on log-likelihood, deviance, median odds ratio, and Proportional Change in Variance. Finally, statistically significant factors were reported using an adjusted odd ratio (AOR) with a 95% Confidence Interval for fixed effect. All variables with a p-value less than 0.05 in the final model were considered statistically significant factors. In Ethiopia, the overall full immunization coverage among children aged 12–23 months was 40.58%, with spatial variation across regions in Ethiopia. The significant spatial distribution of full immunization coverage among children aged 12–23 months was detected in northern Tigray, Addis Ababa, central Oromia, and southeastern Amhara regions. The proportion of rural residents,the proportion of women aged 35–44 years, the proportion of women who had ANC 4 and above andthe proportion of women who had PNC were local factors associated with the proportion of full immunization among children aged 12–23 months. Rural residence [AOR 0.27 (95% CI 0.10, 0.70)], family size 4 and above[AOR 0.41 (95% CI 0.17, 0.96)], never breastfeed [AOR 0.026(95% CI 0.003, 0.21)], 1–3 times ANC visit [AOR 0.45 (95% CI 0.23, 0.86)], being from Oromia region [AOR 0.23 (95% CI 0.05, 0.97)], Eastern pastoralist region [AOR 0.09 (95% CI 0.023, 0.35)], age 35–44 years [(AOR 6 (95% CI 1.57, 22.9)], and PNC [AOR 2.40 (95% CI 1.24, 4.8)] were significant factors associated with fully immunization in multilevel mixed effect analysis. Full immunization coverage in Ethiopia is below the global target with significant geographical variation. The high proportion of rural residents, the high proportion of women who had ANC 4 and above, mothers who had a high proportion of PNC, and the high proportion women age 35-44 years were local geographical factors for the proportion of full immunization among children age 12–23 months in Ethiopia. Women who had PNC, ANC visits four or more times, and increased maternal age were positively associated, whereas larger family size, no breastfeeding, rural residence, and being from Oromia and eastern pastoralist region were negatively associated with full immunization. Strengthening maternal and child health services, focusing on rural areas and low-coverage regions, is essential to increase immunization coverage in Ethiopia.

Abstract Streets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York’s Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention.

Abstract Background Suicide mortality remains a global health concern, and community characteristics affect regional variations in suicide. This study investigated spatially clustered patterns of suicide mortality rates in South Korea and evaluated the impact of community factors on suicide. Methods Suicide mortality rates were estimated by sex, age group, and district, using the 2021 Cause of Death Statistics in South Korea from the MicroData Integrated Service. Community-determinant data for 2021 or the nearest year were collected from the Korean Statistical Information Service. The spatial autocorrelation of suicide by sex and age was examined based on Global Moran’s I index. Geographically weighted regression (GWR) was used to discern the influence of community determinants on suicide. Results Suicide mortality rates were significantly higher among men (40.64 per 100,000) and adults over the age of 65 years (43.18 per 100,000). The male suicide mortality rates exhibited strong spatial dependence, as indicated by a high global Moran’s I with p

Geographically weighted regression (GWR) and its extensions are important local modelling techniques for exploring spatial heterogeneity in regression relationships. However, when dealing with spatial data of overlapping samples – for example, when precise locational information is aggregated to a shared neighbourhood to avoid revealing the addresses of individual survey respondents – GWR-based models can encounter several problems, including obtaining reliable bandwidths. Because data with this characteristic exhibit spatial hierarchical structures, we propose combining hierarchical linear modelling (HLM) with GWR to give a hierarchical and geographically weighted regression (HGWR) model that divides coefficients into sample-level fixed effects, group-level fixed effects, sample-level random effects, and group-level spatially weighted effects. This paper presents a back-fitting likelihood estimator to fit the model, a simulation experiment that suggests that HGWR is better able to capture these effects and the spatial heterogeneity within them than are traditional HLM or GWR models, and a case study looking at predictors of housing price in Beijing, China. The ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modelling tool for handling spatio-temporal big data with spatially hierarchical structures. [ABSTRACT FROM AUTHOR]

In sample surveys, the model calibration approach is an improvement over the usual calibration approach, where the concept of the calibration approach is generalized to obtain a model-assisted estimator using more complex models based on complete auxiliary information. In many surveys, the study and auxiliary variables vary across locations and the observations tend to be similar for the nearby units than those located further apart. In such situations, a simple global model cannot explain the relationships between some sets of variables. This phenomenon is known as spatial non-stationarity which is considered by the geographically weighted regression (GWR) model. It can capture the spatially varying relationship between different variables. In the present study, GWR-based model calibration estimators of population total of the study variable were developed in the context of geo-referenced complex survey designs when complete auxiliary information along with their spatial locations is available at population level. The asymptotic properties of the developed GWR-based model calibration estimators were evaluated under a set of assumptions. Under the same set of assumptions, the variances and estimators of variances of the developed estimators were given. Through a spatial simulation study, the performance of the developed estimators was compared to that of existing estimators and found to be more efficient than the existing ones. Supplementary materials accompanying this paper appear online [ABSTRACT FROM AUTHOR]

Housing price growth resulting from rapid urbanization in China has become a major concern in sustainable research. Taking southern Jiangsu province as an example, we tried to identify urban areas in 1995, 2010, and 2020 at the block scale. Mean centers of housing prices and urban areas in different periods were used as proxies for evaluating the coordination degree. Moreover, a geographically weighted regression model (GWR) was used to discuss the impacts of location, neighborhood, urban vitality, and landscape on housing prices. Results show that: (1) housing prices in a city generally decrease from urban centers to rural areas, indicating that urbanization affects housing prices; (2) mean centers of housing prices and urban areas show a similar trajectory in the entire region, demonstrating a high quality of urban development; (3) location, landscape, and urban vitality typically positively impact home price growth, while neighborhoods negatively impact it; these relationships vary across different cities. The results provide basic knowledge for urban management. [ABSTRACT FROM AUTHOR]

Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban, suburban, and town environments. This field often entails extensive civic involvement and collaborative partnerships with institutions. Its overarching objectives span a spectrum from preserving water quality, habitat, and biodiversity to mitigating the Urban Heat Island (UHI) effect. The UHI phenomenon, characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage, serves as a focal point in this study. The study focuses on developing a methodological framework that integrates Geographically Weighted Regression (GWR), Random Forest (RF), and Suitability Analysis to assess the Urban Heat Island (UHI) effect across different urban zones, aiming to identify areas with varying levels of UHI impact. The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect. Conducted in various London areas, the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI. By mapping the spatial variability of UHI, the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies. The study’s findings are expected to provide valuable insights for urban planners and policymakers, aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management. [ABSTRACT FROM AUTHOR]

Abstract Background In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. Method This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. Results The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H–H clusters of maternal mortality and DMI were in Baluchistan, and the H–H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores. Conclusion The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.

Abstract Background Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

This research investigates the spatial correlation among per capita electricity consumption, per capita industrial electricity consumption, and economic growth by employing various regression models, including linear regression, geographically weighted regression, and multi-stage geographically weighted regression. The primary goal is to illustrate the presence of spatial effects in the connection between electricity consumption and economic growth. In this context, this study made for Turkey distinguishes itself from previous research by utilizing the multi-stage spatially weighted regression model to examine this relationship. The findings reveal that the multi-scale spatial regression model is the most effective in explaining the relation between economic growth at the provincial level and per capita electricity consumption and per capita industrial electricity consumption. Moreover, the study emphasizes that per capita Gross Domestic Product emerges as the most influential regional economic indicator when assessing its impact on per capita electricity consumption and per capita industrial electricity consumption.

Abstract It is of great practical significance to explore the relationship between urbanization and CO2 emissions for the low-carbon development of cities. However, the multiscale assessment of spatial relationship between population, land and economic urbanization and CO2 emissions is lacked. In this study, we first adopted the spatial statistical methods to evaluate the spatial pattern of China’s CO2 emissions in 2019. Then, we spatially quantified China’s urbanization of land, population and economy based on statistical data. Finally, we used the geo-weighted regression model to explore the spatial relationship between urbanization and CO2 emissions at the national-economic zone-province scale. The results displayed that there is obvious spatial heterogeneity in the relationship between China’s urbanization and CO2 emissions. The significant positive correlation between urbanization and CO2 emissions were mainly located in the northeastern, eastern and southwestern regions, consistent with the characteristics of the Heihe–Tengchong Line. The uneven development of land, population and economic urbanization would lead to more CO2 emissions. We suggest that China should attend the balanced development of urban land, population and economy, and avoid the additional carbon emissions caused by incongruence, to further the development of low-carbon cities.

The Yellow River Delta (YRD) has the world's highest land formation rate. However, soil salinization has caused severe land degradation in the region. Understanding the distribution of soil salinity and its variation is essential for saline soil management. This study combined soil salinity sampling data, remote sensing imagery, and layers of geographic environmental factors. Three different models, including Ordinary Kriging (OK), Geographically Weighted Regression (GWR), and Bayesian Maximum Entropy (BME), were used and compared to predict the soil salinity of two soil layers in 2006 and 2022. Then the spatial distribution characteristics and development trends of soil salinity in the YRD were analyzed. The results indicated that (1) The BME model is an optimal salinity prediction model that integrates soft data from multiple sources to perform nonlinear estimates. Compared to the OK and GWR models, the RMSE was reduced by up to 25% and 13%, respectively, and the greatest improvement in R2 was increased from 0.0534 and 0.2718 to 0.5569, respectively. (2) Soil salinity in the YRD shows a spatially increasing trend from the southwestern inland to the northeastern coast. Over the past 16 years, the salinization pattern has become more complex: soil salinization has been mitigated in the central and southern regions, with the average salinity decreasing from 1.03% to 0.86% and the salinization rate decreasing from 99% to 88%; and it has significantly intensified in the northern part of the study area near the old Yellow River channel and the Gubei Reservoir, and in some scattered inland areas. Continuous water and sediment regulation in the Yellow River basin and ecological management of natural reserves can generally alleviate soil salinization, but the risk of soil salinization can be increased by seawater erosion, inappropriate land use, and resource exploitation. [ABSTRACT FROM AUTHOR]

Background: In developing countries, the death probability of a child and mother is more significant than in developed countries; these inequalities in health outcomes are unfair. The present study encompasses a spatial analysis of maternal and child mortalities in Pakistan. The study aims to estimate the District Mortality Index (DMI), measure the inequality ratio and slope, and ascertain the spatial impact of numerous factors on DMI scores across Pakistani districts. Method: This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI. Results: The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H–H clusters of maternal mortality and DMI were in Baluchistan, and the H–H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores. Conclusion: The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities. [ABSTRACT FROM AUTHOR]

Urbanization-induced land use transitions (LUTs) result in a decline in ecosystem services, which has implications for regional ecological security. In order to explore the relationship between ecosystem services and land use transition, this paper utilizes the InVEST model, a geographically weighted regression (GWR) model, to examine the impact of land use transition on ecosystem services in the Dongting Lake area (DLA). The results showed that 1) with the change in urbanization development, the average values of land use transition intensity (LUI) in 2000, 2010, and 2020 are 237.99, 235.82, and 238.92, respectively. Land use dynamics (LUD) show a tendency to increase and then decrease, with average values of 5.58 and 5.62 for the periods 2000-2010 and 2010-2020, respectively, and the transformation of land use shows obvious spatio-temporal heterogeneity. 2) Habitat quality and carbon sequestration showed a downward trend. In contrast, food supply followed an upward trend; soil conservation (SC) and water yield (WY) services initially increased and decreased later. The overall spatial changes in habitat quality and carbon sequestration appear to be insignificant. Food supply shows significant differences in the plains compared to other areas, while soil conservation and water yield service show significant changes in places other than the DLA. 3) From 2000 to 2020, land use transition dynamics, population density, GDP density, night lighting, and transition intensity had mainly negative effects on ecosystem services. Only the Normalized Vegetation Index (NDVI) showed a positive effect on ecosystem services. The results of the research will provide valuable references for the development and implementation of spatial ecological restoration planning and land use policies in the national territory. [ABSTRACT FROM AUTHOR]

Spatial data is data that contains the influence of location with non-homogeneous variance at each location, or spatial heterogeneity. To address spatial heterogeneity, the Geographically Weighted Regression (GWR) model is used. However, in the GWR model, there is a phenomenon of multicollinearity, which is a strong relationship between independent variables that will reduce the accuracy of parameter estimation. To overcome multicollinearity in the GWR model, the Least Absolute Shrinkage and Selection Operator (LASSO) method is used. The LASSO method estimates the parameters of the GWR model by minimizing the sum of squared errors subject to a constraint function, which is solved using the Least Angle Regression (LARS) algorithm. This results in the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to address the problem of multicollinearity in spatial data. Based on the research results, the LASSO method can overcome multicollinearity by shrinking the coefficients of parameters that contribute less and have a strong correlation with other independent variables in the GWR model, resulting in 33 final models. One of the models is for Nias Regency, where the factors influencing the poverty rate are the open unemployment rate, life expectancy, average length of schooling, gross participation rate, and per capita income. In Nias Regency, the value of s is 0.288 with an R-squared value of 0.9403. In Nias Regency, 94.03% of the variation in the poverty rate is explained by the independent variables in the model, while the remaining 5.97% is attributed to external factors not covered by the model. Coefficient of the Human Development Index variable shrinks to exactly zero, indicating that it has no effect on the poverty rate in Nias Regency. [ABSTRACT FROM AUTHOR]

Rurality is an important indicator to reflect the development of villages and reveal internal differences in rural areas. The unbalanced development of transportation infrastructure in the current period of rapid urbanization has become one of the principal elements triggering spatial differences in rurality and changes in rural territorial characteristics. However, there are few studies on the impact of transportation infrastructure accessibility on the multidimensional characteristics of rurality from the perspective of heterogeneity. This paper analyzed the spatio-temporal characteristics of transport accessibility (TA) and the county rurality index (CRI) in Guangdong in 2005, 2010, 2015 and 2020 using an accessibility model and the rurality index and explored the clustering characteristics and interactions of TA and the CRI through exploratory spatial data analysis (ESDA) and geographic weighted regression (GWR) modeling. The findings showed that (1) TA and the CRI in Guangdong were significantly unbalanced in terms of space. The CRI showed a weakening trend in general, forming a distribution pattern of "high in the north and low in the south, high in the west and low in the east", while TA was on the rise, maintaining a stable pattern of "high in the middle and low in the periphery". (2) Both TA and the CRI in Guangdong had a Moran's I value greater than 0.6 during the study period, exhibiting strong spatial agglomeration, while the two showed a significant spatial negative correlation. (3) The influence of TA on the CRI in different dimensions showed dynamic changes in stages, with TA having a higher intensity of effect on society rurality and land rurality in 2005, while on society rurality and industry rurality in 2020. (4) This paper grouped 77 counties in Guangdong into four types of policy zonings—coordination types, lagged types of accessibility, lagged types of rurality and double lagged types—and put forward corresponding development recommendations. The study conducted in this paper contributes to an in-depth understanding of the impact of transportation infrastructure development on the multidimensional characteristics of rurality and provides a basis for policy formulation for coordinated urban–rural development and sustainable rural development. [ABSTRACT FROM AUTHOR]

Estimating tree height at the national to regional scale is crucial for assessing forest health and forest carbon storage and understanding forest ecosystem processes. It also aids in formulating forest management and restoration policies to mitigate global climate change. Extensive ground-survey data offer a valuable resource for estimating tree height. In tree height estimation modeling, a few comparative studies have examined the effectiveness of global-based versus local-based models, and the spatial heterogeneity of independent variable parameters remains insufficiently explored. This study utilized ~200,000 ground-survey data points covering the entire provincial region to compare the performance of the global-based Ordinary Least Squares (OLS) and Random Forest (RF) model, as well as local-based Geographically Weighted Regression (GWR) model, for predicting the average tree height of Chinese fir forests in Zhejiang Province China. The results showed that the GWR model outperformed both OLS and RF in terms of predictive accuracy, achieving an R-squared (R2) and adjusted R2 of 0.81 and MAE and RMSE of 0.93 and 1.28, respectively. The performance indicated that the local-based GWR held advantages over global-based models, especially in revealing the spatial non-stationarity of forests. Visualization of parameter estimates across independent variables revealed spatial non-stationarity in their impact effects. In mountainous areas with dense forest coverage, the parameter estimates for average age were notably higher, whereas in forests proximate to urban areas, the parameters were comparatively lower. This study demonstrates the effectiveness of large ground-survey data and GWR in tree height estimation modeling at a provincial scale. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing pricing in Romanian rural tourism using a hedonic pricing model through a hybrid LASSO-OLS regression and geographically weighted regression (GWR). By analyzing data from 5028 unique accommodation units across 1170 local administrative units, we identify some key pricing determinants, including accommodation size, capacity, facilities, and environmental attributes. The results reveal that larger accommodations and those with higher guest capacities command higher prices. Luxurious facilities, such as massage services, pools, and fireplaces, significantly increase pricing, although the impact of such features varies by region, as do accommodation type and natural scenery, with agritouristic boarding houses and proximity to natural attractions like water bodies and forests being more valued in certain regions. These factors can aid rural entrepreneurs in optimizing pricing to enhance competitiveness and profitability. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019. Methods: Data from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables. Results: The trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section. Conclusions: Despite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population's range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves. [ABSTRACT FROM AUTHOR]

Urban green and recreational spaces are important for residents and one of the foundations for the functioning of cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit. In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region. [ABSTRACT FROM AUTHOR]