Spatial heterogeneity of the effects of river network patterns on water quality in highly urbanized city.

River water quality deterioration is a serious problem in urban water environments. River network patterns affect water quality by influencing the flow, mixing, and other processes of water bodies. However, the effects of urban river network patterns on water quality remain poorly understood, thereby hindering the urban planning and management decision-making process. In this study, the geographically weighted regression (GWR) model was used to explore the spatial heterogeneity of the relationship between river network pattern and water quality. The results showed that the river network has a complex structure, high connectivity, and relatively even distribution and morphology. Important river structure indicators affecting water quality included the water surface ratio (Wp) and multifractal features (∆α, ∆f) while important river connectivity indicators included circuitry (α) and network connectivity (γ). River structure has a more complex effect on water quality than connectivity. This study recommends that the Wp should be increased in agricultural areas and appropriately reduced in urban built-up areas, and the number of river segments and nodes should be controlled within a rational configuration. Our study provides key insights for evaluating and optimizing the river network patterns to improve water quality of urban rivers. In the future, the land use intensity, hydrological processes, and human activities should be coupled with the river network pattern to deepen our understanding of urban river environment.

[Analysis of Spatio-temporal Distribution Characteristics and Influencing Factors of PM2.5 Concentration in Urban Agglomerations on the Northern Slope of Tianshan Mountains].

Analysis of the spatial and temporal distribution characteristics and influencing factors of PM2.5 concentrations for the urban agglomeration on the northern slope of Tianshan Mountain is of positive significance for regional economic construction and environmental protection. The spatial and temporal distributions of PM2.5 concentrations in the Tianshan North Slope urban agglomeration from March to November 2015 to 2021 were obtained through the inversion of the MCD19A2 aerosol product combined with meteorological factors using a geographically weighted regression (GWR) model, followed by the analysis of change trends and influencing factors. The results were as follows:① the high PM2.5 concentrations in the study area were mainly distributed in the oasis city cluster between the northern foot of Tianshan Mountain and the Gurbantunggut Desert, showing the spatial distribution characteristics of being "low around and high in the middle" and "low in the west and high in the east." The annual average value of ρ(PM2.5) in the study area was 16.98 µg·m-3, with high values mainly concentrated in the urban part of Urumqi and decreasing towards Changji and Fukang. The monthly average ρ(PM2.5) distribution pattern was consistent with the annual average, but there were seasonal differences as follows:autumn (20.32 µg·m-3) > spring (18.25 µg·m-3) > summer (12.47 µg·m-3). The accumulation phenomenon was more pronounced in spring and winter. ② The study area's annual average PM2.5 concentration showed a decreasing trend from 2015 to 2021, and the average value from March to October also showed a decreasing trend, with only a slight increase in November. From the analysis of the spatial distribution of PM2.5 concentration trends, the decrease was concentrated in the urban parts of major cities, especially in the urban part of Urumqi and its surrounding areas, where the decrease was the largest and the change was the most drastic. ③ Temperature and air pressure were positively correlated with PM2.5 concentrations, whereas relative humidity, wind speed, atmospheric boundary layer height, and precipitation were negatively correlated with PM2.5 concentrations. The degree of influence of each factor was ranked from high to low as follows:atmospheric boundary layer height > relative humidity > air pressure > air temperature > wind speed > precipitation.

Efecto de factores sociodemográficos en la mortalidad del COVID-19 en Costa Rica: un enfoque geográfico

Introducción: La enfermedad por coronavirus (COVID-19) se ha extendido entre la población de todo el país y ha tenido un gran impacto a nivel mundial. Sin embargo, existen diferencias geográficas importantes en la mortalidad de COVID-19 entre las diferentes regiones del mundo y en Costa Rica. Objetivo: Explorar el efecto de algunos de los factores sociodemográficos en la mortalidad de COVID-19 en pequeñas divisiones geográficas o cantones de Costa Rica. Métodos: Usamos registros oficiales y aplicamos un modelo de regresión clásica de Poisson y un modelo de regresión ponderada geográficamente. Resultados: Obtuvimos un criterio de información de Akaike (AIC) más bajo con la regresión ponderada (927.1 en la regresión de Poison versus 358.4 en la regresión ponderada). Los cantones con un mayor riesgo de mortalidad por COVID-19 tuvo una población más densa; bienestar material más alto; menor proporción de cobertura de salud y están ubicadas en el área del Pacífico de Costa Rica. Conclusiones: Una estrategia de intervención de COVID-19 específica debería concentrarse en áreas de la costa pacífica con poblaciones más densas, mayor bienestar material y menor población por unidad de salud.

Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great global impact. However, there are important geographic differences in mortality from COVID-19 among world regions and within Costa Rica. Objective: To explore the effect of some sociodemographic factors on COVID-19 mortality in the small geographic divisions or cantons of Costa Rica. Methods: We used official records and applied a classical epidemiological Poisson regression model and a geographically weighted regression model. Results: We obtained a lower Akaike Information Criterion with the weighted regression (927.1 in Poisson regression versus 358.4 in weighted regression). The cantons with higher risk of mortality from COVID-19 had a denser population; higher material well-being; less population by health service units and are located near the Pacific coast. Conclusions: A specific COVID-19 intervention strategy should concentrate on Pacific coast areas with denser population, higher material well-being and less population by health service units.

Urban expansion patterns and their driving forces analysis: a comparison between Chengdu-Chongqing and Middle Reaches of Yangtze River urban agglomerations.

Urban agglomerations have emerged as the primary drivers of high-quality economic growth in China. While recent studies have examined the urban expansion patterns of individual cities, a comparative study of the urban expansion patterns of urban agglomerations at two different scales is required for a more comprehensive understanding. Thus, in this study, we conduct a two-scale comparative analysis of urban expansion patterns and their driving factors of the two largest urban agglomerations in western and central China, i.e., Chengdu-Chongqing urban agglomeration (CCUA) and the Middle Reaches of Yangtze River urban agglomerations (MRYRUA) at both the urban agglomeration and city levels. We investigate the urban expansion patterns of CCUA and MRYRUA between 2000 and 2020 using various models, including the urban expansion rate, fractal dimension, modified compactness, and gravity-center method. Then we use multiple linear regression analysis and geographically weighted regression (GWR) to explore the magnitude and geographical differentiation of influences for economic, demographic, industrial structure, environmental conditions, and neighborhood factors on urban expansion patterns. Our findings indicate that CCUA experienced significantly faster urban growth compared to MRYRUA. There is an excessive concentration of resources to megacities within the CCUA, whereas there is a lack of sufficient collaboration among the three provinces within the MRYRUA. Additionally, we identify significant differences in the impacts of driving forces of CCUA and MRYRUA, as well as spatial heterogeneity and regional aggregation in the variation of their strength. Our two-scale comparative study of urban expansion patterns will not only provide essential reference points for CCUA and MRYRUA but also serve as valuable insights for other urban agglomerations in China, enabling them to promote sustainable urban management and foster integrated regional development.

Identification of possible sources for potentially toxic elements and polycyclic aromatic hydrocarbons and their spatially varying relationships in urban soils of Dublin, Ireland.

Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) harm the ecosystem and human health, especially in urban areas. Identifying and understanding their potential sources and underlying interactions in urban soils are critical for informed management and risk assessment. This study investigated the potential sources and the spatially varying relationships between 9 PTEs and PAHs in the topsoil of Dublin by combining positive matrix factorisation (PMF) and geographically weighted regression (GWR). The PMF model allocated four possible sources based on species concentrations and uncertainties. The factor profiles indicated the associations with high-temperature combustion (PAHs), natural lithologic factors (As, Cd, Co, Cr, Ni), mineralisation and mining (Zn), as well as anthropogenic inputs (Cu, Hg, Pb), respectively. In addition, selected representative elements Cr, Zn, and Pb showed distinct spatial interactions with PAHs in the GWR model. Negative relationships between PAHs and Cr were observed in all samples, suggesting the control of Cr concentrations by natural factors. Negative relationships between PAHs and Zn in the eastern and north-eastern regions were related to mineralisation and anthropogenic Zn-Pb mining. In contrast, the surrounding regions exhibited a natural relationship between these two variables with positive coefficients. Increasing positive coefficients from west to east were observed between PAHs and Pb in the study area. This special pattern was consistent with prevailing south-westerly wind direction in Dublin, highlighting the predominant influences on PAHs and Pb concentrations from vehicle and coal combustion through atmospheric deposition. Our results provided a better understanding of geochemical features for PTEs and PAHs in the topsoil of Dublin, demonstrating the efficiency of combined approaches of receptor models and spatial analysis in environmental studies.

Determinants of quantification of tourism waste in the hilly terrain of Himalayas: A spatial approach.

The rapid economic development in environmentally sensitive zone of Himalayas resulted in the increased generation of tourism waste. However, the accounting methodology for accumulation of tourism waste in the hilly terrain was found to be missing. Accordingly, the socio-economic factors influencing the tourism waste generation were identified, and their correlation analysis was performed. The tourism waste generated within and outside an urban local body was quantified over a period of 12 years (2008-2019) using a novel methodology, considering the socioeconomic factors, such as economic significance, geographical terrain, location of tourist destinations and tourism-related activities. The spatial dependency of tourism waste accumulation in the Himalayan state of Himachal Pradesh, India was analyzed using the geographically weighted regression. Furthermore, the air pollutants' emission (PM2.5, PM10, CO, SO2 and NOx) from the open burning of neglected tourism waste were also quantified and compared with the existing literature.

Understanding the shared working spaces: a Geography of Co-working supply in Delhi.

The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended.

Consumption-based emissions at city level in China and the spatial heterogeneity analysis of the influential factors.

It is of great significance to identify the critical influential factors of pollutant emissions for emission mitigation. However, city disparity implies different priorities for regional mitigation. This study aims to estimate the consumption-based emissions of 309 prefecture-level cities in China based on the multi-region input-output table and the sectoral NOx emission inventory and investigate the emission transfer phenomenon among cities and sectors. In addition, a geographically weighted regression method is used to analyze the spatial heterogeneity in the driving factors of regional consumption-based emissions. The results reveal that the top 10 cities in consumption-based emissions account for 25.2% of emissions and contribute 22.6% to GDP. The consumption-based emissions are mainly driven by local demand (72.79%) at the regional level and by construction activities (94.43%) at the sectoral level. Besides, the results also show the spatial variances in contributions of driving forces to consumption-based emissions. Economic growth has been identified as the most important factor which promotes consumption-based emissions. However, disposable personal income, per capita road area, urbanization, and percentage of tertiary industry GDP are conducive to reduce consumption-based emissions in some cities of China. It could be concluded that policies without consideration of the emissions from a consumption perspective are difficult to achieve effective emission reduction.

Identifying geographical inequalities of maternal care utilization in Ethiopia: a Spatio-temporal analysis from 2005 to 2019.

INTRODUCTION: Inequalities in maternal care utilization pose a significant threat to maternal health programs. This study aimed to describe and explain the spatial variation in maternal care utilization among pregnant women in Ethiopia. Accordingly, this study focuses on identifying hotspots of underutilization and mapping maternal care utilization, as well as identifying predictors of spatial clustering in maternal care utilization. METHODS: We evaluated three key indicators of maternal care utilization: pregnant women who received no antenatal care (ANC) service from a skilled provider, utilization of four or more ANC visits, and births attended in a health facility, based the Ethiopian National Demographic and Health Survey (EDHS5) to 2019. Spatial autocorrelation analysis was used to measure whether maternal care utilization was dispersed, clustered, or randomly distributed in the study area. Getis-Ord Gi statistics examined how Spatio-temporal variations differed through the study location and ordinary Kriging interpolation predicted maternal care utilization in the unsampled areas. Ordinary least squares (OLS) regression was used to identify predictors of geographic variation, and geographically weighted regression (GWR) examined the spatial variability relationships between maternal care utilization and selected predictors. RESULT: A total of 26,702 pregnant women were included, maternal care utilization varies geographically across surveys. Overall, statistically significant low maternal care utilization hotspots were identified in the Somali region. Low hotspot areas were also identified in northern Ethiopia, stretching into the Amhara, Afar, and Beneshangul-Gumuz regions; and the southern part of Ethiopia and the Gambella region. Spatial regression analysis revealed that geographical variations in maternal care utilization indicators were commonly explained by the number of under-five children, the wealth index, and media access. In addition, the mother's educational status significantly explained pregnant women, received no ANC service and utilized ANC service four or more times. Whereas, the age of a mother at first birth was a spatial predictor of pregnant who received no ANC service from a skilled provider. CONCLUSION: In Ethiopia, it is vital to plan to combat maternal care inequalities in a manner suitable for the district-specific variations. Predictors of geographical variation identified during spatial regression analysis can inform efforts to achieve geographical equity in maternal care utilization.

Elucidating the spatial determinants of heavy metals pollution in different agricultural soils using geographically weighted regression.

Intensive human activities caused massive socio-economic and land-use changes that directly or indirectly resulted in excessive accumulation of heavy metals in agricultural soils. The goal of our study was to explore the spatial determinants of heavy metals pollution for agricultural soil environment in Sunan economic region of China. We applied geographically weighted regressions (GWR) to measure the spatially varying relationship as well as conducted principal component analysis (PCA) to incorporate multiple variables. The results indicated that our GWR models performed well to identify the determinants of heavy metal pollution in different agricultural soils with relatively high values of local R2. Heavy metal pollution in Sunan economic region was crucially determined by accessibility, varying agricultural inputs as well as the composition and configuration of agricultural landscape, and such impacts exhibited significantly heterogeneity over space and farming practices. For the both agricultural soils, the major variance proportion for our determinants can be grouped into the first four factors (82.64 % for cash-crop soils and 73.065 for cereal-crop soils), indicating the incorporation and interactions between variables determining agricultural soil environment. Our findings yielded valuable insights into understanding the spatially varying 'human-land interrelationship' in rapidly developing areas. Methodologically, our study highlighted the applicability of geographically weighted regression to explore the spatial determinants associated with unwanted environmental outcomes in large areas.

Spatial trends and projections of chronic malnutrition among children under 5 years of age in Ethiopia from 2011 to 2019: a geographically weighted regression analysis.

INTRODUCTION: Undernutrition is a serious global health issue, and stunting is a key indicator of children's nutritional status which results from long-term deprivation of basic needs. Ethiopia, the largest and most populous country in Sub-Saharan Africa, has the greatest rate of stunting among children under the age of five, yet the problem is unevenly distributed across the country. Thus, we investigate spatial heterogeneity and explore spatial projection of stunting among under-five children. Further, spatial predictors of stunting were assessed using geospatial regression models. METHODS: The Ethiopia Demographic and Health Surveys (EDHS) data from 2011, 2016, and 2019 were examined using a geostatistical technique that took into account spatial autocorrelation. Ordinary kriging was used to interpolate stunting data, and Kulldorff spatial scan statistics were used to identify spatial clusters with high and low stunting prevalence. In spatial regression modeling, the ordinary least square (OLS) model was employed to investigate spatial predictors of stunting and to examine local spatial variations geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models were employed. RESULTS: Overall, stunting prevalence was decreased from 44.42% [95%, CI: 0.425-0.444] in 2011 to 36.77% [95%, CI: 0.349-0.375] in 2019. Across three waves of EDHS, clusters with a high prevalence of stunting in children under 5 years were consistently observed in northern Ethiopia stretching in Tigray, Amhara, Afar, and Benishangul-Gumuz. Another area of very high stunting incidence was observed in the Southern parts of Ethiopia and the Somali region of Ethiopia. Our spatial regression analysis revealed that the observed geographical variation of under-five stunting significantly correlated with poor sanitation, poor wealth index, inadequate diet, residency, and mothers' education. CONCLUSIONS: In Ethiopia, substantial progress has been made in decreasing stunting among children under the age of 5 years; although disparities varied in some areas and districts between surveys, the pattern generally remained constant over time. These findings suggest a need for region and district-specific policies where priority should be given to children in areas where most likely to exhibit high-risk stunting.

Dynamic of land use, landscape, and their impact on ecological quality in the northern sand-prevention belt of China.

Changes in land use and landscapes have a direct impact on the regional eco-environment. It is of great importance to understand the change pattern of land use, landscapes, and their mechanism on the ecological quality, especially ecologically fragile areas. The northern sand-prevention belt (NSPB) is an important ecologically fragile area in China, which has a large influence on the ecological security of the entire country. Based on the land use data of the NSPB in 2000, 2010, and 2018, we studied the spatio-temporal characteristics of land-use change and change in landscape patterns. The ecological quality represented by the remote sensing-based desertification index (RSDI) was calculated using satellite images. The effects of land use and landscape patterns on RSDI were analyzed by geographic detector and geographically weighted regression. Important results include the following: (1) Land-use change in the study area was high during 2000-2010 but slower in 2010-2018. Grassland was the largest land-use type in the NSPB, and varied greatly in terms of total change and spatial location. The major change was the conversion between dense and moderate grass, with 64,860 km2 of dense grass turning into moderate grass, and 48,505 km2 changing the other way. (2) Among the four landscape metrics, patch density, area-weighted mean fractal dimension, and edge density increased, whereas the aggregation index decreased, which indicated that the landscape was developing towards heterogeneity, fragmentation, complexity, and aggregation. Spatially, the landscape metrics presented a strip distribution in the east of the NSPB. (3) The effects of various land-use types on ecological quality, from high to low, were unused land, woodland, dense grass, cropland, moderate grass, built-up land, sparse grass, and waterbody. The areas where the ecological quality was greatly affected by the landscape patterns were concentrated in the agro-pastoral ecotone and the forest-steppe ecotone. The results of this study reveal the trends of land use and landscape patterns in the NSPB over 18 years and can help to understand their mechanism on ecological quality, which is of significance for the management of this area.

Coupling coordination degree and driving factors of new-type urbanization and low-carbon development in the Yangtze River Delta: based on nighttime light data.

The coordination relationship between new-type urbanization and urban low-carbon development under the goal of carbon neutrality has become a hot issue that needs to be focused on when formulating policies. Based on the estimation of urban CO2 emissions by night light data, this study used spatial autocorrelation, spatial Markov chain and geographically weighted regression model to measure the spatial correlation and spillover effects of the coupling coordination degree of two systems in the Yangtze River Delta urban agglomeration from 2005 to 2018 and analyzed the influencing factors. The results showed that (1) the coupling coordination degree showed an increasing trend, but the club effect was quite obvious, and the regional pattern was higher in southeast and lower in northwest; (2) the spatial spillover effect of coupling coordination degree is significant, which aggravates the long-term persistence of the imbalance pattern; (3) regional economic level, government fiscal regulation, and industrial upgrading are the main driving forces for the increase of coupling coordination degree, while over-concentration of population and low energy efficiency are the main obstacles. Finally, on the basis of these conclusions, we provide targeted policy planning suggestions for policy makers.

Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China.

This paper explores the spatial relationship between urbanization and urban household carbon emissions at the prefectural level and above cities in China and uses Exploratory Spatial Data Analysis (ESDA) and Geographically Weighted Regression (GWR) to reveal the extent of the impact of urbanization on urban household carbon emissions and the spatial and temporal variation characteristics. The results show that: Overall carbon emissions of urban households in cities of China showed a decreasing trend during the study period, but there were significant differences in the carbon emissions of urban households in the four major regions. In terms of the spatial and temporal characteristics of urban household carbon emissions, the urban "head effect" of urban household carbon emissions is obvious. The high-high clustering of urban household carbon emissions is characterized by a huge triangular spatial distribution of "Beijing-Tianjin-Hebei, Chengdu-Chongqing, and Shanghai". The level of urbanization in Chinese cities at the prefecture level and above shows a spatial pattern of decreasing levels of urbanization in the east, middle, and west. The four subsystems of urbanization are positively correlated with urban household carbon emissions in the same direction. The urbanization factors have a contributory effect on some cities' carbon emissions of urban households, but there are significant regional differences in the impact of urbanization factors on urban household carbon emissions in the eastern, central, and western regions of China, as they are at different stages of rapid urbanization development.

Spatiotemporal Differentiation and Driving Force Analysis of the High-Quality Development of Urban Agglomerations along the Yellow River Basin.

The ecological protection and high-quality development (HQD) of the Yellow River Basin (YRB) have been promoted as national strategies. An urban agglomeration is the basic unit of the YRB used to participate in international competitions. Taking seven urban agglomerations covering 70 cities along the YRB as the sample, this paper establishes a high-quality evaluation system and uses the entropy method and exploratory spatial data analysis (ESDA) to analyze the HQD levels of the seven urban agglomerations along the YRB from 2009 to 2018. In addition, geographically-weighted regression (GWR) is adopted to analyze the influencing factors. The results show that: (1) the gap in the HQD of the seven urban agglomerations gradually narrows, showing a spatial pattern of "high in the east, low in the west, and depression in the middle"; (2) the HQD levels of the seven urban agglomerations have a strong spatial correlation, and the patterns of cold and hot spots have not changed substantially, showing the spatial distribution of "hot in the east, cold in the west"; (3) the degree of influence of each driving factor on the HQD differs among the seven urban agglomerations. The order is as follows: industrial structure upgrading index > proportion of R&D expenditure > urbanization rate > internet penetration rate > proportion of urban construction area > proportion of days reaching the air standard. These findings show that advanced industrial structure and technology are the two core driving forces for the HQD of the urban agglomerations along the YRB.

Quantifying the human disturbance intensity of ecosystems and its natural and socioeconomic driving factors in urban agglomeration in South China.

The impact of human activities on terrestrial ecosystems is becoming more intense than ever in history. Human disturbance analyses play important roles in appropriately managing the human-environment relationship. In this study, a human disturbance index (HDI) that uses land use and land cover data from 1980, 2000, 2010, and 2018 is proposed to assess the human disturbance of ecosystems in the Guangdong-Hong Kong-Macao Greater Bay Area. The HDI is first calculated by classifying the human disturbance intensity into seven levels and 13 categories from weak to strong in ecosystems. Then the driving factors of the HDI spatial pattern change are explored using a geographically weighted regression (GWR) model. The results showed that the spatial pattern of the HDI was high in the middle and low in the surrounding areas. The intensity of human disturbance increased, and the medium and high disturbance areas expanded during 1980-2018, especially in Guangzhou, Foshan, Shenzhen, and Dongguan. Human disturbance displayed an obvious spatial heterogeneity. The GWR model had a better explanation effect of the analysis of the HDI change drivers. The driving effect of the socioeconomic conditions was significantly stronger than that of the natural environmental. This study assists in understanding the distribution and change characteristics of the ecological environment in areas with strong human activities and provides a reference for related studies.

Spatial disparities of self-reported COVID-19 cases and influencing factors in Wuhan, China.

The lack of detailed COVID-19 cases at a fine spatial resolution restricts the investigation of spatial disparities of its attack rate. Here, we collected nearly one thousand self-reported cases from a social media platform during the early stage of COVID-19 epidemic in Wuhan, China. We used kernel density estimation (KDE) to explore spatial disparities of epidemic intensity and adopted geographically weighted regression (GWR) model to quantify influences of population dynamics, transportation, and social interactions on COVID-19 epidemic. Results show that self-reported COVID-19 cases concentrated in commercial centers and populous residential areas. Blocks with higher population density, higher aging rate, more metro stations, more main roads, and more commercial point-of-interests (POIs) have a higher density of COVID-19 cases. These five explanatory variables explain 76% variance of self-reported cases using an OLS model. Commercial POIs have the strongest influence, which increase COVID-19 cases by 28% with one standard deviation increase. The GWR model performs better than OLS model with the adjusted R 2 of 0.96. Spatial heterogeneities of coefficients in the GWR model show that influencing factors play different roles in diverse communities. We further discussed potential implications for the healthy city and urban planning for the sustainable development of cities.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach.

BACKGROUND: Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques. METHOD: Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing. RESULTS: The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method. CONCLUSION: This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.

Using Spatial Pattern Analysis to Explore the Relationship between Vulnerability and Resilience to Natural Hazards.

This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural hazards, including flood and debris flow events, which are the most common natural hazards in our case study area due to the topography and precipitation features. In order to provide a concise result, we apply the principal component analysis (PCA) to aggregate the correlated variables. Moreover, we use the spatial autocorrelation analysis to analyze the spatial pattern and spatial difference. We also adopt the geographically weighted regression (GWR) to validate the effectiveness of SERV. The result of GWR shows that SERV is valid and unbiased. Moreover, the result of spatial autocorrelation analysis shows that the mountain areas are extremely vulnerable and lack enough resilience. In contrast, the urban regions in plain areas show low vulnerability and high resilience. The spatial difference between the mountain and plain areas is significant. The topography is the most significant factor for the spatial difference. The high elevation and steep slopes in mountain areas are significant obstacles for socioeconomic development. This situation causes consequences of high vulnerability and low resilience. The other regions, the urban regions in the plain areas, have favorable topography for socioeconomic development. Eventually, it forms a scenario of low vulnerability and high resilience.

Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR).

Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the complexity of COVID-19 expansion on a spatial-temporal scale, this research appropriately analyzed the spatial-temporal heterogeneity at the county-level in Texas. First, the impact factors of COVID-19 are captured on social, economic, and environmental multiple facets, and the communality is extracted through principal component analysis (PCA). Second, this research uses COVID-19 cumulative case as the dependent variable and the common factors as the independent variables. According to the virus prevalence hierarchy, the spatial-temporal disparity is categorized into four quarters in the GWR analysis model. The findings exhibited that GWR models provide higher fitness and more geodata-oriented information than OLS models. In El Paso, Odessa, Midland, Randall, and Potter County areas in Texas, population, hospitalization, and age structures are presented as static, positive influences on COVID-19 cumulative cases, indicating that they should adopt stringent strategies in curbing COVID-19. Winter is the most sensitive season for the virus spread, implying that the last quarter should be paid more attention to preventing the virus and taking precautions. This research is expected to provide references for the prevention and control of COVID-19 and related infectious diseases and evidence for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.