Is environment destiny? Spatial analysis of the relationship between geographic factors and obesity in Türkiye

This study aims to evaluate the relationship of geographical factors, including precipitation, slope, air pollution and elevation with adult obesity prevalence in Türkiye (TR) using a cross-regional study design. Ordinary least squares (OLS) and geographically weighted regression (GWR) were performed to evaluate the spatial variation in the relationship between all geographic factors and obesity prevalence. In the model, a positive relationship was found between obesity prevalence and slope, whereas a negative significant relationship was determined between obesity prevalence and elevation (p < 0.05). These results, revealing spatially varying associations, were very useful in refining the interpretations of the statistical results on adult obesity. This research suggests that geographical factors should be considered as one of the components of the obesogenic environment. In addition, it is recommended that national and international strategies to overcome obesity should be restructured by taking into account the geographical characteristics of the region.

Changing effects of energy and water on the richness distribution pattern of the Quercus genus in China

Climate varies along geographic gradients, causing spatial variations in the effects of energy and water on species richness and the explanatory power of different climatic factors. Species of the Quercus genus are important tree species in China with high ecological and socioeconomic value. To detect whether the effects of energy and water on species richness change along climatic gradients, this study built geographically weighted regression models based on species richness and climatic data. Variation partition analysis and hierarchical partitioning analysis were used to further explore the main climatic factors shaping the richness distribution pattern of Quercus in China. The results showed that Quercus species were mainly distributed in mountainous areas of southwestern China. Both energy and water were associated with species richness, with global slopes of 0.17 and 0.14, respectively. The effects of energy and water on species richness gradually increased as energy and water in the environment decreased. The interaction between energy and water altered the effect of energy, and in arid regions, the effects of energy and water were relatively stronger. Moreover, energy explained more variation in species richness in both the entire study area (11.5%) and different climate regions (up to 19.4%). The min temperature of coldest month was the main climatic variable forming the richness distribution pattern of Quercus in China. In conclusion, cold and drought are the critical climatic factors limiting the species richness of Quercus, and climate warming will have a greater impact in arid regions. These findings are important for understanding the biogeographic characteristics of Quercus and conserving biodiversity in China.

The impact of urban spatial environment on COVID-19: a case study in Beijing

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities' ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

Water availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA-GPP ) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA-GPP relationship (RWA-GPP ) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near-infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance-light-use efficiency GPP. We found that the 3- and 7-year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17- and 40-year time scales (7.28% and 8.23%). The long-term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA-GPP and RWA-GPP with different source products: In high-latitude regions, RWA-GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid-low latitudes. Forests had weak RWA-GPP at all time scales, shrublands showed negative RWA-GPP at long time scales, and grassland (GL) showed a positive RWA-GPP at short time scales. Globally, the EWA-GPP , whether positive or negative, enhanced significantly at 3-, 7-, and 17-year time scales. For arid and humid zones, the semi-arid and sub-humid zones experienced a faster increase in the positive EWA-GPP , whereas the humid zones experienced a faster increase in the negative EWA-GPP . At the ecosystem types, the positive EWA-GPP at a 3-year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA-GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA-GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

Hyperspectral inversion of soil water and salt information based on fractional order derivative technology

Accurate and efficient acquisition of soil water and salt information is a prerequisite for the improvement and sustainable utilization of saline lands. With the ground field hyperspectral reflectance and the measured soil water-salt content as data sources, we used the fractional order differentiation (FOD) technique to process hyperspectral data (with a step length of 0.25). The optimal FOD order was explored at the correlation level of spectral data and soil water-salt information. We constructed two-dimensional spectral index, support vector machine regression (SVR) and geographically weighted regression (GWR). The inverse model of soil water-salt content was finally evaluated. The results showed that FOD technique could reduce the hyperspectral noise and explore the potential spectral information to a certain extent, improve the correlation between spectrum and characteristics, with the highest correlation coefficients of 0.98, 1.35 and 0.33. The combination of characteristic bands screened by FOD and two-dimensional spectral index were more sensitive to characteristics than one-dimensional bands, with the optimal responses of order 1.5, 1.0 and 0.75. The optimal combinations of bands for maximum absolute correction coefficient of SMC were 570, 1000, 1010, 1020, 1330 and 2140 nm, pH were 550, 1000, 1380 and 2180 nm, and salt content were 600, 990, 1600 and 1710 nm, respectively. Compared with the original spectral reflectance, the validation coefficients of determination (R_p²) of the optimal order estimation models for SMC, pH, and salinity were improved by 1.87, 0.94 and 0.56, respectively. The overall GWR accuracy in the proposed model was better than SVR, where the GWR optimal order estimation models R_p² were 0.866, 0.904 and 0.647, and the relative per-centage difference were 3.54, 4.25 and 1.86, respectively. The overall spatial distribution of soil water and salt content in the study area was characterized by low in the west and high in the east, with more serious soil alkalinization problems in the northwest and less severe in the northeast. The results would provide scientific basis for the hyperspectral inversion of soil water and salt in the Yellow River Irrigation Area and a new strategy for the implementation and management of precision agriculture in saline soil areas.

Smallholder farms have and can store more carbon than previously estimated

Increasing soil organic carbon (SOC) stocks is increasingly targeted as a key strategy in climate change mitigation and improved ecosystem resiliency. Agricultural land, a dominant global land use, provides substantial challenges and opportunities for global carbon sequestration. Despite this, global estimates of soil carbon sequestration potential often exclude agricultural land and estimates are coarse for regions in the Global South. To address these discrepancies and improve estimates, we develop a hybrid, data-augmented database approach to better estimate the magnitude of SOC sequestration potential of agricultural soils. With high-resolution (30 m) soil maps of Africa developed by the International Soils Database (iSDA) and Malawi as a case study, we create a national adjustment using site-specific soil data retrieved from 1160 agricultural fields. We use a benchmark approach to estimate the amount of SOC Malawian agricultural soils can sequester, accounting for edaphic and climatic conditions, and calculate the resulting carbon gap. Field measurements of SOC stocks and sequestration potentials were consistently larger than iSDA predictions, with an average carbon gap of 4.42 ± 0.23 Mg C ha^-1 to a depth of 20 cm, with some areas exceeding 10 Mg C ha^-1 . Augmenting iSDA predictions with field data also improved sensitivity to identify areas with high SOC sequestration potential by 6%-areas that may benefit from improved management practices. Overall, we estimate that 6.8 million ha of surface soil suitable for agriculture in Malawi has the potential to store 274 ± 14 Tg SOC. Our approach illustrates how ground truthing efforts remain essential to reduce errors in continent-wide soil carbon predictions for local and regional use. This work begins efforts needed across regions to develop soil carbon benchmarks that inform policies and identify high-impact areas in the effort to increase SOC globally.

Estimation of soil organic matter content in Yinchuan Plain based on fractional derivative combined with spectral indices

Soil organic matter (SOM) is a crucial indicator of soil fertility. Field hyperspectral reflectance and laboratory SOM data of soil samples from the Yinchuan Plain were used to explore the performance of models based on fractional derivative combined with different spectral indices. Following reciprocal and logarithmic transformation, the reflectance data were processed using fractional derivative from 0 to 2 orders (interval 0.20). Then, the difference index (DI), ratio index (RI), brightness index (BI), normalized difference index (NDI), renormalized difference index (RDI), and generalized difference index (GDI) were constructed. The two-dimensional correlation between the six indices and SOM content were analyzed. The optimal spectral indices were selected to establish SOM estimation models with principal component regression (PCR), partial least square regression (PLSR), back propagation neural network (BPNN), support vector machine (SVM), and geographically weighted regression (GWR). Results showed that the maximum absolute correlation coefficient (MACC) values between DI, RI, NDI, BI, GDI, RDI, and SOM contents increased firstly and then decreased, with the highest values observed at 1.0, 0.6, 1.4, and 1.6 orders. The 0.2-2.0 order RDI under fractional derivative variation could be used for subsequent model construction, in which the optimal combinations of bands for MACC values were mainly concentrated at 400-600 nm and 1300-1700 nm. Among the different models based on the single spectral index RDI, the model based on SVM achieved the highest estimation accuracy, whose modeling determination coefficient, verification determination coefficient and relative percentage difference reached 0.86, 0.87 and 2.32. Our results would provide a scientific reference for quick and accurate SOM assessment and mapping in areas with relatively low SOM content.

Non-Iterative Multiscale Estimation for Spatial Autoregressive Geographically Weighted Regression Models

Multiscale estimation for geographically weighted regression (GWR) and the related models has attracted much attention due to their superiority. This kind of estimation method will not only improve the accuracy of the coefficient estimators but also reveal the underlying spatial scale of each explanatory variable. However, most of the existing multiscale estimation approaches are backfitting-based iterative procedures that are very time-consuming. To alleviate the computation complexity, we propose in this paper a non-iterative multiscale estimation method and its simplified scenario for spatial autoregressive geographically weighted regression (SARGWR) models, a kind of important GWR-related model that simultaneously takes into account spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. In the proposed multiscale estimation methods, the two-stage least-squares (2SLS) based GWR and the local-linear GWR estimators of the regression coefficients with a shrunk bandwidth size are respectively taken to be the initial estimators to obtain the final multiscale estimators of the coefficients without iteration. A simulation study is conducted to assess the performance of the proposed multiscale estimation methods, and the results show that the proposed methods are much more efficient than the backfitting-based estimation procedure. In addition, the proposed methods can also yield accurate coefficient estimators and such variable-specific optimal bandwidth sizes that correctly reflect the underlying spatial scales of the explanatory variables. A real-life example is further provided to demonstrate the applicability of the proposed multiscale estimation methods.

Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework

Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.

Emergency Department Visits and Summer Temperatures in Bologna, Northern Italy, 2010-2019: A Case-Crossover Study and Geographically Weighted Regression Methods

The aim of the study is to evaluate the association between summer temperatures and emergency department visits (EDVs) in Bologna (Italy) and assess whether this association varies across areas with different socioeconomic and microclimatic characteristics. We included all EDVs within Bologna residences during the summers of 2010-2019. Each subject is attributed a deprivation and a microclimatic discomfort index according to the residence. A time-stratified case-crossover design was conducted to estimate the risk of EDV associated with temperature and the effect modification of deprivation and microclimatic characteristics. In addition, a spatial analysis of data aggregated at the census block level was conducted by applying a Poisson and a geographically weighted Poisson regression model. For each unit increase in temperature above 26 °C, the risk of EDV increases by 0.4% (95%CI: 0.05-0.8). The temperature-EDV relationship is not modified by the microclimatic discomfort index but rather by the deprivation index. The spatial analysis shows that the EDV rate increases with deprivation homogeneously, while it diminishes with increases in median income and microclimatic discomfort, with differences across areas. In conclusion, in Bologna, the EDV risk associated with high temperatures is not very relevant overall, but it tends to increase in areas with a low socioeconomic level.

Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China

Accurately estimating the spatial and temporal distribution of precipitation is crucial for hydrological modeling. However, precipitation products based on a single source have their advantages and disadvantages. How to effectively combine the advantages of different precipitation datasets has become an important topic in developing high-quality precipitation products internationally in recent years. This paper uses the measured precipitation data of Multi-Source Weighted-Ensemble Precipitation (MSWEP) and in situ rainfall observation in the Taihu Lake Basin, as well as the longitude, latitude, elevation, slope, aspect, surface roughness, distance to the coastline, and land use and land cover data, and adopts a two-step method to achieve precipitation fusion: (1) downscaling the MSWEP source precipitation field using the bilinear interpolation method and (2) using the geographically weighted regression (GWR) method and tri-cube function weighting method to achieve fusion. Considering geographical and human activities factors, the spatial and temporal distribution of precipitation errors in MSWEP is detected. The fusion of MSWEP and gauge observation precipitation is realized. The results show that the method in this paper significantly improves the spatial resolution and accuracy of precipitation data in the Taihu Lake Basin.

Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic-Analysis of the Local Variations Using Geographically Weighted Regression

As the COVID-19 pandemic continues, an increasing number of different research studies focusing on various aspects of the pandemic are emerging. Most of the studies focus on the medical aspects of the pandemic, as well as on the impact of COVID-19 on various areas of life; less emphasis is put on analyzing the influence of socio-environmental factors on the spread of the pandemic. In this paper, using the geographically weighted regression method, the extent to which demographic, social, and environmental factors explain the number of cases of SARS-CoV-2 is explored. The research was performed for the case-study area of Poland, considering the administrative division of the country into counties. The results showed that the demographic factors best explained the number of cases of SARS-CoV-2; the social factors explained it to a medium degree; and the environmental factors explained it to the lowest degree. Urban population and the associated higher amount and intensity of human contact are the most influential factors in the development of the COVID-19 pandemic. The analysis of the factors related to the areas burdened by social problems resulting primarily from the economic exclusion revealed that poverty-burdened areas are highly vulnerable to the development of the COVID-19 pandemic. Using maps of the local R² it was possible to visualize how the relationships between the explanatory variables (for this research-demographic, social, and environmental factors) and the dependent variable (number of cases of SARS-CoV-2) vary across the study area. Through the GWR method, counties were identified as particularly vulnerable to the pandemic because of the problem of economic exclusion. Considering that the COVID-19 pandemic is still ongoing, the results obtained may be useful for local authorities in developing strategies to counter the pandemic.

[Urbanization and ecological effect in mid-southern Liaoning urban agglomeration, China]

China has entered the stage of rapid urbanization since the 1990s, resulting in a series of environmental problems. Based on the nighttime light remote sensing data and land use data from 1995 to 2020, we extracted the compound night light index (CNLI) to measure the urbanization level of mid-southern Liaoning urban agglomeration, evaluated habitat quality by InVEST model. We examined the relationship between urbanization level and habitat quality in mid-southern Liaoning urban agglomeration by using the correlation analysis method and the geographic weighted regression model. The results showed that CNLI increased by 0.14 from 1995 to 2020. The urbanization level increased continuously, with a pattern of "low in the east and high in the west". The habitat quality decreased by 0.005, showing a pattern of "high in the east and low in the west". The ecological environment became worse. There was a significant negative spatial correlation between urbanization level and habitat quality in mid-southern Liaoning urban agglomeration. The negative impact of urbanization level on habitat quality gradually decreased. In order to alleviate habitat degradation caused by urbanization and realize the coordinated and sustainable development of regional social economy, it was urgent to take a series of measures, such as delimiting the ecological protection red line, improving the intensive use of land, delimiting the urban boundary, promoting the coordinated development of regional integration.

Spatially varying associations between the built environment and older adults' propensity to walk

Population aging has become a severe issue facing most nations and areas worldwide-with Hong Kong being no exception. For older adults, walking is among the most well-liked travel modes, boosting their overall health and wellbeing. Some studies have confirmed that the built environment has a significant (spatially fixed) influence on older adults' walking behavior. However, little consideration has been given to the potential spatial heterogeneity in such influences. Hence, this study extracted data on older adults' (outdoor) walking behavior from the 2011 Hong Kong Travel Characteristics Survey and measured a series of built environment attributes based on geo-data (e.g., Google Street View imagery). Logistic regression and geographically weighted logistic regression models were developed to unveil the complicated (including spatially fixed and heterogeneous) association between the built environment and older adults' propensity to walk. We show that population density, land-use mix, street greenery, and access to bus stops are positively connected with the propensity to walk of older adults. Intersection density seems to impact walking propensity insignificantly. All built environment attributes have spatially heterogeneous effects on older adults' walking behavior. The percentage of deviance explained is heterogeneously distributed across space.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Spatial Associations of Long-term Exposure to Diesel Particulate Matter with Seasonal and Annual Mortality Due to COVID-19 in the Contiguous United States

Background People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality across three waves of the disease and throughout 2020. Methods We tested an ordinary least square (OLS) model, then two global models, spatial lag model (SLM) and spatial error model (SEM), designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations. Results The GWR model found that associations between COVID-19 deaths and DPM concentrations may increase up to 57, 36, 43, and 58 deaths per 100,000 people in some US counties for every 1 µg/m ³ increase in DPM concentration. Relative significant positive association are observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibit a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. Conclusions Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease, but that influence appears to have waned over time as transmission patterns evolved.

Spatial-Temporal Evolution and Driving Forces of NDVI in China's Giant Panda National Park

Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan-Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities.

Rural-Urban Differences in Non-Local Primary Care Utilization among People with Osteoarthritis: The Role of Area-Level Factors

The utilization of non-local primary care physicians (PCP) is a key primary care indicator identified by Alberta Health to support evidence-based healthcare planning. This study aims to identify area-level factors that are significantly associated with non-local PCP utilization and to examine if these associations vary between rural and urban areas. We examined rural-urban differences in the associations between non-local PCP utilization and area-level factors using multivariate linear regression and geographically weighted regression (GWR) models. Global Moran's I and Gi* hot spot analyses were applied to identify spatial autocorrelation and hot spots/cold spots of non-local PCP utilization. We observed significant rural-urban differences in the non-local PCP utilization. Both GWR and multivariate linear regression model identified two significant factors (median travel time and percentage of low-income families) with non-local PCP utilization in both rural and urban areas. Discontinuity of care was significantly associated with non-local PCP in the southwest, while the percentage of people having university degree was significant in the north of Alberta. This research will help identify gaps in the utilization of local primary care and provide evidence for health care planning by targeting policies at associated factors to reduce gaps in OA primary care provision.

An ecological study on the spatially varying association between adult obesity rates and altitude in the United States: using geographically weighted regression

In this research, we evaluated the relationship between obesity rates and altitude using a cross-county study design. We applied a geographically weighted regression (GWR) to examine the spatially varying association between adult obesity rates and altitude after adjusting for four predictor variables including physical activity. A significant negative relationship between altitude and adult obesity rates were found in the GWR model. Our GWR model fitted the data better than OLS regression (R² = 0.583), as indicated by an improved R² (average R² = 0.670; range: 0.26-0.77) and a lower Akaike Information Criteria (AIC) value (14,736.88 vs. 15,386.59 in the OLS model). These approaches, evidencing spatial varying associations, proved very useful to refine interpretations of the statistical output on adult obesity. This study underscored the geographic variation in relationships between adult obesity rates and mean county altitude in the United States. Our study confirmed a varying overall negative relationship between county-level adult obesity rates and mean county altitude after taking other confounding factors into account.

Industrial Transformation and Urban Economic Efficiency Evolution: An Empirical Study of the Yangtze River Economic Belt

Industrial transformation and high-quality urban development have become the core issues of urban-rural coordination and the leap forward in development in the new era. The research perspective of 'pattern-process-mechanism' is needed to reveal the spatiotemporal correlation characteristics of industrial transformation and urban economic efficiency evolution, and to expand its systematic, comprehensive and regional characteristics. Based on the geographical cognitive of local effects and spatial non-stationarity, we used a quantile regression model and a geographically weighted regression model to analyze the dynamic mechanism of industrial transformation and urban economic efficiency to explain the path characteristics of urban development and industrial transformation of the Yangtze River economic belt. The conclusions are as follows: (1) From 2000 to 2015, the average economic efficiency in the Yangtze River economic belt increased from 0.05 to 0.332, and the pattern gradually changed from spatial homogeneity to spatial mosaic; (2) From 2000 to 2015, the range and intensity of industrial transformation in the Yangtze River economic belt showed an increasing trend, while the speed of industrial transformation showed a downward trend, and the high-value unit of the three showed the characteristics of gradual homogenization; (3) From the perspective of the impact of industrial transformation on urban economic efficiency, the impact of the range and speed of industrial transformation on urban economic efficiency was gradually weakened, while the impact of the intensity of industrial transformation on urban economic efficiency was gradually strengthened, and the patterns of the three show the characteristics of a spatially inverted U-shaped distribution with high values in the middle reaches and low values in the upstream and downstream areas.

Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage

Dockless bike-sharing systems have become one of the important transport methods for urban residents as they can effectively expand the metro’s service area. We applied the ordinary least square (OLS) model, the geographically weighted regression (GWR) model and the multiscale geographically weighted regression (MGWR) model to capture the spatial relationship between the urban built environment and the usage of bike-sharing connected to the metro. A case study in Beijing, China, was conducted. The empirical result demonstrates that the MGWR model can explain the varieties of spatial relationship more precisely than the OLS model and the GWR model. The result also shows that, among the proposed built environment factors, the integrated usage of bike-sharing and metro is mainly affected by the distance to central business district (CBD), the Hotels-Residences points of interest (POI) density, and the road density. It is noteworthy that the effect of population density on dockless bike-sharing usage is only significant at weekends. In addition, the effects of the built environment variables on dockless bike-sharing usage also vary across space. A common feature is that most of the built environment factors have a more obvious impact on the metro-oriented dockless bike-sharing usage in the eastern part of the study area. This finding can provide support for governments and urban planners to efficiently develop a bike-sharing-friendly built environment that promotes the integration of bike-sharing and metro.

Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil

OBJECTIVES

To analyse the spatial distribution of rates of COVID-19 cases and its association with socio-economic conditions in the state of Pernambuco, Brazil.

METHODS

Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio-economic factors on rates.

RESULTS

Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID-19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID-19.

CONCLUSIONS

Our results provide important information on socio-economic factors related to the spread of COVID-19 and can serve as a basis for decision-making in similar circumstances.

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m² to reduce the mean concentration of PM_2.5 and NO₂ by 1 μg/m³, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help city policymakers formulate regional air management policies and provide support for the green and sustainable development of the real estate market in China.

Spatial Analysis and Comparison of the Economic Burden of Common Diseases: An Investigation of 5.7 Million Rural Elderly Inpatients in Southeast China, 2010-2016

Background: As China embraced an aging society, the burden of age-related diseases had increased dramatically. Knowledge about spatial distribution characteristics of disease burden and the influencing factors of medical expenditure is of great significance to the formulation of health policies. However, related research in rural China is still insufficient.

Methods: A total of 5,744,717 records of hospitalized rural elderly in southeast China were collected from 2010 to 2016. We described the temporal trends of hospitalization medical expenditure and the prevalence of catastrophic health expenses (CHE) in the rural elderly by common diseases. Then, geographical information tools were used for visualization of geographic distribution patterns of CHE, the ordinary least squares methods (OLS) and geographically weighted regression (GWR) were employed to examine the influencing factors of medical expenditure.

Results: The number of CHE hospitalizations and the total number of hospitalizations for the rural elderly people increased by 2.1 times and 2.2 times, respectively, from 2010 to 2016. Counties with a high prevalence of CHE were clustered in the eastern coastal area (Moran's I = 0.620, P < 0.001, General G < 0.001, P < 0.001). Unspecified transport accidents, cardiovascular disease, and essential hypertension were the top causes of CHE in the rural elderly. Adequate hospital beds (P < 0.05) and reasonable utilization and distribution of town-level (P < 0.001) and county-level hospitals (P < 0.001) may help reduce medical expenditures.

Conclusions: In the context of an aging society, the disease burden for the elderly in rural areas should arouse more attention. These findings highlight the importance of age-related disease prevention and the rational allocation of medical resources in rural areas.

Examining Spatial Variability in the Association Between Male Partner Alcohol Misuse and Intimate Partner Violence Against Women in Ghana: A GWR Analysis

Globally, it is estimated that about 30% of ever-partnered women have experienced some form of intimate partner violence (IPV)-physical assault, sexual assault, or emotional abuse. The prevalence of IPV in sub-Saharan Africa is considerably higher than the global estimate. In Ghana, it is estimated that 24% of women have experienced physical and/or sexual IPV in their lifetime. Studies point to the association between alcohol misuse by intimate male partners and violence against women. However, there has been no consideration for potential spatial variation or heterogeneity in this association. Using estimates from the 2008 Ghana Demographic and Health Survey Data, we employed geographically weighted regression (GWR) analysis to examine spatial variations in the relationship between male partner's alcohol misuse and IPV among women in Ghana. We fitted three models to assess the relationship using a step-wise approach. The first model has alcohol misuse as the only predictor, whereas the second model included other male partner characteristics, such as post-secondary education and employment status. The final introduced female characteristics as additional covariates. The result of the GWR analysis shows that the effect of alcohol misuse on IPV is elevated in the south-western part of Ghana. The findings suggest the potential influence of place-based or contextual factors on the association between alcohol misuse and women's exposure to IPV.

Anemia Among Children Under 5 Years in India: A District-Level Spatial Analysis Using NFHS-5 Data

The 2015/2016 National Family Health Survey (NFHS-4) revealed that the prevalence of anemia among children under 5 years is 58% in India. Lack of nutritional supplementation and lack of health care facilities are found to be important influential factors of anemia among children. We aimed to examine district-level spatial heterogeneity and clustering of associated factors with childhood anemia in India. Geographically weighted regression was applied on the NFHS-5 data for 335 districts. Factors such as prevalence of nutritional supplementation in children and mothers, birth order, antenatal care, diarrhea in children, and stunting were found to be significantly associated. Spatial scan statistics technique identified 3 significant local spatial clusters of anemia. This study provides findings based on the latest available data which can further assist in the design and execution of tailor-made policies.

Causes of Sickness Absenteeism in Europe-Analysis from an Intercountry and Gender Perspective

This study aims to extract and explain the territorially varied relation between socioeconomic factors and absence rate from work due to own illness or disability in European countries in the years 2006-2020. For this purpose, several causes were identified, depending on men and women. To explain the absenteeism and emphasize gender as well as intercountry differences, geographically weighted regression was applied. For men, there were five main variables that influenced sickness absence: body mass index, the average rating of satisfaction by job situation, employment in the manufacturing sector, social benefits by sickness/health care, and performing health-enhancing physical activity. For women, there were five main variables that increased the absence rate: the risk of poverty or social exclusion, long-standing illness or health problems, employment in the manufacturing sector, social protection benefits, and deaths due to pneumonia. Based on the conducted research, it was proven that the sickness absence observed in the analyzed countries was highly gender and spatially diverged. Understanding the multifactorial factors playing an important role in the occurrence of regional and gender-divergent sickness absence may be a good predictor of subsequent morbidity and mortality as well as be very useful to better prevent this outcome.

Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China

In recent decades, due to the effect of climate change and the interference of human activities, the species habitat index has fallen by 2%. Studying on the geographical distribution pattern and predicting the potential geographical distribution of species are of great significance for developing scientific and effective biodiversity conservation strategies. Plenty of rare and endangered species that need immediate conservation are distributed in Northwest Yunnan. In this regard, this research is conducted in the purpose of predicting the potential geographical distribution of 25 rare and endangered plant species in Northwest Yunnan and analyzing the explanation capabilities of various environmental factors on the potential geographical distribution patterns of these species. Initially, the ecological niche model MaxEnt was employed to predict the potential geographical distribution of target species. Following that, the superposition method was applied to obtain the potential geographical distribution pattern of species richness on the spatial scale of the ecological niche model with a resolution of 0.05° × 0.05°. Ultimately, geographically weighted regression (GWR) model was adopted to investigate the explanation capabilities of various environmental parameters on the potential distribution patterns. The research results showed that the average value of the area under the receiver operating curve (AUC) of each species was between 0.80 and 1.00, which indicated that the simulation accuracy of the MaxEnt model for each species was good or excellent. On the whole, the potential distribution area for each species was relatively concentrated and mainly distributed in the central-western, central-eastern and northern regions of Northwest Yunnan. In addition, the potential distribution areas of these species were between 826.33 km2 and 44,963.53 km2. In addition, the annual precipitation (Bio12), precipitation of coldest quarter (Bio19), and population density (Pop) made a greater contribution to the species distribution model, and their contribution values were 25.92%, 15.86%, and 17.95%, respectively. Moreover, the goodness-of-fit R 2 and AIC value of the water model were 0.88 and 7,703.82, respectively, which indicated the water factor largely influenced the potential distribution of these species. These results would contribute to a more comprehensive understanding of the potential geographical distribution pattern and the distribution of suitable habitats of some rare and endangered plant species in Northwest Yunnan and would be helpful for implementing long-term conservation and reintroduction for these species.

Spatial variation in direct and indirect effects of climate and productivity on species richness of terrestrial tetrapods

AIM

We aimed to dissect the spatial variation of the direct and indirect effects of climate and productivity on global species richness of terrestrial tetrapods.

LOCATION

Global.

TIME PERIOD

Present.

MAJOR TAXA STUDIED

Terrestrial tetrapods.

METHODS

We used a geographically weighted path analysis to estimate and map the direct and indirect effects of temperature, precipitation and primary productivity on species richness of terrestrial tetrapods across the globe.

RESULTS

We found that all relationships shift in magnitude, and even in direction, among taxonomic groups, geographical regions and connecting paths. Direct effects of temperature and precipitation are generally stronger than both indirect effects mediated by productivity and direct effects of productivity.

MAIN CONCLUSIONS

Richness gradients seem to be driven primarily by effects of climate on organismal physiological limits and metabolic rates rather than by the amount of productive energy. Reptiles have the most distinct relationships across tetrapods, with a clear latitudinal pattern in the importance of temperature versus water.

Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea

Invasive alien plants can severely threaten biodiversity and cause economic losses in the agricultural industry; therefore, identifying the critical environmental factors related to the distribution of alien plants plays a crucial role in ecosystem management. In this study, we applied partial least squares regression (PLSR) and geographically weighted regression (GWR) to estimate the important environmental factors affecting the spread of two invasive and expansive plants, Lactuca scariola L. and Aster pilosus Willd., across South Korea. GWR provides more accurate predictions than ordinary least squares regression, and the local coefficients of GWR allow for the determination of the spatial relationships between alien plant distributions and environmental variables. Based on the model’s results, the distributions of these alien species were significantly associated with anthropogenic effects, such as human population density, residential area, and road density. Furthermore, the two alien species can establish themselves in habitats where native plants cannot thrive, owing to their broad tolerance to temperature and drought conditions. This study suggests that urban development and expansion can facilitate the invasion of these species in metropolitan cities.

Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM2.5 Estimation

Land use regression (LUR) models are used for high-resolution air pollution assessment. These models use independent parameters based on an assumption that these parameters are accurate and invariable; however, they are observational parameters derived from measurements or modeling. Therefore, the parameters are commonly inaccurate, with nonstationary effects and variable characteristics. In this study, we propose a geographically weighted total least squares regression (GWTLSR) to model air pollution under various traffic, land use, and meteorological parameters. To improve performance, the proposed model considers the dependent and independent variables as observational parameters. The GWTLSR applies weighted total least squares in order to take into account the variable characteristics and inaccuracies of observational parameters. Moreover, the proposed model considers the nonstationary effects of parameters through geographically weighted regression (GWR). We examine the proposed model’s capabilities for predicting daily PM_2.5 concentration in Isfahan, Iran. Isfahan is a city with severe air pollution that suffers from insufficient data for modeling air pollution with conventional LUR techniques. The advantages of the model features, including consideration of the variable characteristics and inaccuracies of predictors, are precisely evaluated by comparing the GWTLSR model with ordinary least squares (OLS) and GWR models. The R2 values estimated by the GWTLSR model during the spring and autumn are 0.84 and 0.91, respectively. The corresponding average R2 values estimated by the OLS model during the spring and autumn are 0.74 and 0.69, respectively, and the R2 values estimated by the GWR model are 0.76 and 0.70, respectively. The results demonstrate that the proposed functional model efficiently described the physical nature of the relationships among air pollutants and independent variables.

Spatial and experimental analysis of peer-to-peer accommodation consumption during COVID-19

The COVID-19 pandemic has disrupted peer-to-peer (P2P) accommodation markets. However, how the interplay between tourists and destination attributes has affected P2P accommodation consumption during the pandemic has not been investigated. To address this gap, this study first explored the spatially varying relationship between destination attributes and COVID-19-disrupted Airbnb performance change across Florida counties. Subsequently, we performed two experimental studies to examine whether trip purpose and the level of perceived threat affect Airbnb use intention. The results of the spatial analysis show that, depending on the type of destination attribute, Airbnb listings experienced different revenue losses across urban and rural areas. Additionally, results of experimental studies show that business tourists with a low perceived threat of COVID-19 are more willing to consume Airbnb listings than leisure tourists. This study contributes to ascertaining the destination and behavioral heterogeneity in pandemic-induced P2P accommodation consumption using spatial analytic and experimental studies.

Unprecedented Migratory Bird Die-Off: A Citizen-Based Analysis on the Spatiotemporal Patterns of Mass Mortality Events in the Western United States

Extensive, severe wildfires, and wildfire-induced smoke occurred across the western and central United States since August 2020. Wildfires resulting in the loss of habitats and emission of particulate matter and volatile organic compounds pose serious threatens to wildlife and human populations, especially for avian species, the respiratory system of which are sensitive to air pollutions. At the same time, the extreme weather (e.g., snowstorms) in late summer may also impact bird migration by cutting off their food supply and promoting their migration before they were physiologically ready. In this study, we investigated the environmental drivers of massive bird die-offs by combining socioecological earth observations data sets with citizen science observations. We employed the geographically weighted regression models to quantitatively evaluate the effects of different environmental and climatic drivers, including wildfire, air quality, extreme weather, drought, and land cover types, on the spatial pattern of migratory bird mortality across the western and central US during August-September 2020. We found that these drivers affected the death of migratory birds in different ways, among which air quality and distance to wildfire were two major drivers. Additionally, there were more bird mortality events found in urban areas and close to wildfire in early August. However, fewer bird deaths were detected closer to wildfires in California in late August and September. Our findings highlight the important impact of extreme weather and natural disasters on bird biology, survival, and migration, which can provide significant insights into bird biodiversity, conservation, and ecosystem sustainability.

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

COVID-19 Community Incidence and Associated Neighborhood-Level Characteristics in Houston, Texas, USA

Central to developing effective control measures for the COVID-19 pandemic is understanding the epidemiology of transmission in the community. Geospatial analysis of neighborhood-level data could provide insight into drivers of infection. In the current analysis of Harris County, Texas, we used custom interpolation tools in GIS to disaggregate COVID-19 incidence estimates from the zip code to census tract estimates—a better representation of neighborhood-level estimates. We assessed the associations between 29 neighborhood-level characteristics and COVID-19 incidence using a series of aspatial and spatial models. The variables that maintained significant and positive associations with COVID-19 incidence in our final aspatial model and later represented in a geographically weighted regression model were the percentage of the Black/African American population, percentage of the foreign-born population, area derivation index (ADI), percentage of households with no vehicle, and percentage of people over 65 years old inside each census tract. Conversely, we observed negative and significant association with the percentage employed in education. Notably, the spatial models indicated that the impact of ADI was homogeneous across the study area, but other risk factors varied by neighborhood. The current findings could enhance decision making by local public health officials in responding to the COVID-19 pandemic. By understanding factors that drive community transmission, we can better target disease control measures.

GIS-Based Analysis Framework to Identify the Determinants of COVID-19 Incidence and Fatality in Africa

Corona virus diseases 2019 (COVID-19) pandemic is an extraordinary threat with significant implications in all aspects of human life; therefore, it represents the most immediate challenge for the countries all over the world. This study, hence, is intended to identify the best GIS-based model that can explore, quantify, and model the determinants of COVID-19 incidence and fatality. For this purpose, geospatial models were developed to estimate COVID-19 incidence and fatality rates in Africa, up to 16th of August 2020 at the national level. The models involved Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analysis using ArcGIS. Spatial autocorrelation analysis recorded a positive spatial autocorrelation in COVID-19 incidence (Moran index 0.16, P = 0.1) and fatality (Moran index 0.26, P = 0.01) rates within different African countries. GWR model had higher R2 than OLS for prediction of incidence and mortality (58% vs 45% and 55% vs 53%). The main predictors of COVID-19 incidence rate were overcrowding, health expenditure, HIV infections, air pollution, and BCG vaccination (mean β = 3.10, 1.66, 0.01, 3.79, and -66.60 respectively, P < 0.05). The main determinants of COVID-19 fatality were prevalence of bronchial asthma, tobacco use, poverty, aging, and cardiovascular diseases fatality (mean β = 0.00162, 0.00004, -0.00025, -0.00144, and -0.00027 respectively, P < 0.05). Application of the suggested model can assist in guiding intervention strategies, particularly at the local and community level whenever the data on COVID-19 cases and predictors variables are available.

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones

Local warming induced by rapid urbanization has been threatening residents' health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

Mental Health and Recreation Opportunities

The environment has direct and indirect effects on mental health. Previous studies acknowledge that the poor design of communities and social environments leads to increased psychological distress, but methodological issues make it difficult to draw clear conclusions. Recent public health, leisure and recreation studies have tried to determine the relationship between recreation opportunities and mental health. However, previous studies have heavily focused on individual contexts rather than national or regional levels; this is a major limitation. It is difficult to reflect the characteristics of community environments effectively with such limited studies, because social environments and infrastructure should be analyzed using a spatial perspective that goes beyond an individual’s behavioral patterns. Other limitations include lack of socioeconomic context and appropriate data to represent the characteristics of a local community and its environment. To date, very few studies have tested the spatial relationships between mental health and recreation opportunities on a national level, while controlling for a variety of competing explanations (e.g., the social determinants of mental health). To address these gaps, this study used multi-level spatial data combined with various sources to: (1) identify variables that contribute to spatial disparities of mental health; (2) examine how selected variables influence spatial mental health disparities using a generalized linear model (GLM); (3) specify the spatial variation of the relationships between recreation opportunities and mental health in the continental U.S. using geographically weighted regression (GWR). The findings suggest that multiple factors associated with poor mental health days, particularly walkable access to local parks, showed the strongest explanatory power in both the GLM and GWR models. In addition, negative relationships were found with educational attainment, racial/ethnic dynamics, and lower levels of urbanization, while positive relationships were found with poverty rate and unemployment in the GLM. Finally, the GWR model detected differences in the strength and direction of associations for 3109 counties. These results may address the gaps in previous studies that focused on individual-level scales and did not include a spatial context.

Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India's response in managing the pandemic

Due to the outbreak of Coronavirus, humans all over the world are facing several health problems. The present study has explored the spatio-temporal pattern of Coronavirus spread in India including spatial clustering, identification of hotspot, spatial heterogeneity, and homogeneity, spatial trend, and direction of COVID-19 cases using spatial statistical analysis during the period of 30 January to 20 June 2020. Besides, the polynomial regression model has been used for predictions of COVID-19 affected population and related deaths. The study found positive spatial heterogeneity in COVID-19 cases in India. The study has also identified 17 epicentres across the country with high incidence rates. The directional distribution of ellipse polygon shows that the spread of COVID-19 now trending towards the east but the concentration of cases is mainly in the western part of the country. The country's trend of COVID-19 follows a fourth-order polynomial growth and is characterized by an increasing trend. The prediction results show that as on 14 October India will reach 14,660,400 COVID-19 cases and the death toll will cross 152,945. Therefore, a "space-specific" policy strategy would be a more suitable strategy for reducing the spatial spread of the virus in India. Moreover, the study has broadly found out seven sectors, where the Government of India lacks in terms of confronting the ongoing pandemic. The study has also recommended some appropriate policies which would be immensely useful for the administration to initiate strategic planning.

Life Expectancy in Chinese Cities: Spatially Varied Role of Socioeconomic Development, Population Structure, and Natural Conditions

Improving life expectancy, as well as people's health and wellbeing, is an important goal both for the Chinese government and the United Nations. Therefore, to analyze the main factors influencing life expectancy in prefecture-level cities in China, this study uses classical ordinary least-squares regression and geographical weighted regression on the data of the latest census. Moreover, regional differences induced by each influencing factor are also depicted in this study. The results demonstrate that there is significant heterogeneity and spatial positive correlation among the distribution of life expectancy in prefecture-level cities, with a generally higher life expectancy in the provincial capitals and eastern China, and lower in western China. The geographically weighted regression analysis shows that the economic development level, medical conditions, demographic structure, natural environment, and city attributes all affect the distribution of life expectancy, but that their effects have significant spatial heterogeneity. Life expectancy of the less developed areas in Western China is affected dominantly by economic development level, whereas medical services and education are of great importance in determining the life expectancy in Northern and Southern China, respectively. Thus, it is crucial to solve health problems based on local conditions, especially focusing on the improvement of health and health care in underdeveloped areas. Meanwhile, for the eastern developed areas, special attention should be paid to environmental protection in the economic process, while striving to achieve high-quality development.

[Spatial-temporal evolution of ecological land and influence factors in Wuhan urban agglome-ration based on geographically weighted regression model]

Ecological land is essential to sustainable development of urban agglomeration. Based on the results of remote sensing image interpretation, we analyzed the spatial-temporal evolution of ecological land in 32 research units of ecological land in Wuhan urban agglomeration in 2000-2005, 2005-2010 and 2010-2015, using the land use transition matrix, exploratory regression analysis, the ordinary least squares (OLS) model, and geographically weighted regression (GWR) model. Then, the best regression model was selected after perfecting the traditional index system of influencing factors by data of the location and quantitative information of companies, enterprises and life services, etc., and conducting exploratory regression analysis. Finally, we analyzed the influencing factors and spatial differentiation rules of different research periods with GWR model. The results showed that, from 2000 to 2015, the amount of transition from ecological land use to non-ecological land use in the urban agglomeration showed an inverted U-shaped change pattern, and the space showing the expanding trend from point to surface. Land use patterns of 8.4% area had changed in the urban agglomeration, among which the conversion of cultivated land, forest land, grassland, water body and unused land to non-ecological land accounted for 41.9% of the total area. The spatial pattern gradually expanded from the central urban area of Wuhan to the periphery of the municipal sub-center and county-level towns. The total number of passing models in the three stages of exploratory regression analysis was 326. The GWR and OLS regression were used for comparative analysis of all models. The adjusted R² in the three stages of selected models were 0.83, 0.91 and 0.76, respectively. The former improved by 0.02, 0.03 and 0.02, and the AICc decreased by 2.88, 3.42 and 0.83, respectively. The results of GWR model showed substantially spatial differentiation of influencing factors of ecological land evolution in Wuhan urban agglomeration, and that the influence patterns was dominated by gradual transition in different directions in space, with other patterns such as "V" distribution. The effects of spatial factors were significant. The potential information of spatial data enhanced the interpretation of ecological land evolution within the urban agglomeration.

Sociodemographic Determinants of Acute Myocardial Infarction Hospitalization Risks in Florida

Background Identifying social determinants of myocardial infarction (MI) hospitalizations is crucial for reducing/eliminating health disparities. Therefore, our objectives were to identify sociodemographic determinants of MI hospitalization risks and to assess if the impacts of these determinants vary by geographic location in Florida. Methods and Results This is a retrospective ecologic study at the county level. We obtained data for principal and secondary MI hospitalizations for Florida residents for the 2005-2014 period and calculated age- and sex-adjusted MI hospitalization risks. We used a multivariable negative binomial model to identify sociodemographic determinants of MI hospitalization risks and a geographically weighted negative binomial model to assess if the strength of associations vary by location. There were 645 935 MI hospitalizations (median age, 72 years; 58.1%, men; 73.9%, white). Age- and sex-adjusted risks ranged from 18.49 to 69.48 cases/10 000 persons, and they were significantly higher in counties with low education levels (risk ratio [RR]=1.033, P<0.0001) and high divorce rate (RR, 0.995; P=0.018). However, they were significantly lower in counties with high proportions of rural (RR, 0.996; P<0.0001), black (RR, 1.026; P=0.032), and uninsured populations (RR, 0.983; P=0.040). Associations of MI hospitalization risks with education level and uninsured rate varied geographically (P for non-stationarity test=0.001 and 0.043, respectively), with strongest associations in southern Florida (RR for <high school education, 1.036-1.041; RR for uninsured rate, 0.971-0.976). Conclusions Black race, divorce, rural residence, low education level, and lack of health insurance were significant determinants of MI hospitalization risks, but associations with the latter 2 were stronger in southern Florida. Thus, interventions for addressing MI hospitalization risks need to prioritize these populations and allocate resources based on empirical evidence from global and local models for maximum efficiency and effectiveness.

Exploring the Spatial Patterning in Racial Differences in Cardiovascular Health Between Blacks and Whites Across the United States: The REGARDS Study

Background

Cardiovascular health (CVH) disparities between blacks and whites have persisted in the United States for some time, and although there have been remarkable improvements in addressing cardiovascular disease, it still remains the leading cause of death in the United States. In addition, well‐documented disparities are unfortunately widening incidence gaps across certain regions of the United States. Our focus was on answering the following questions: (1) How much spatial heterogeneity exists in the racial differences in CVH between blacks and whites across this country? and (2) Is the spatial heterogeneity in the racial differences significantly explained by living in the Stroke Belt?

Methods and Results

To explore the spatial patterning in the racial differences in CVH between blacks and whites across the country, we used geographically weighted regression methods, which result in local estimates of the racial differences in CVH. Using data from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) Study, we found significant spatial patterning in these racial differences, even beyond the well‐known Stroke Belt and Stroke Buckle. All of the estimated differences indicated blacks consistently having diminishing CVH compared with whites, where this difference was largely noted in pockets of the Stroke Belt and Stroke Buckle, in addition to moderate to large disparities noted in the Great Lakes region, portions of the Northeast, and along the West coast.

Conclusions

Efforts to improve CVH and ultimately reduce disparities between blacks and whites require culturally competent methods, with a strong focus on geography‐based interventions and policies.

Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality

Understanding the complex relationships between land use and stream water quality is critical for water pollution control and watershed management. This study aimed to investigate the relationship between land use types and water quality indicators at multiple spatial scales, namely, the watershed and riparian scales, using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR extended traditional regression models, such as OLS to address the spatial variations among variables. Our results indicated that the water quality indicators were significantly affected by agricultural and forested areas at both scales. We found that extensive agricultural land use had negative effects on water quality indicators, whereas, forested areas had positive effects on these indicators. The results also indicated that the watershed scale is effective for management and regulation of watershed land use, as the predictive power of the models is much greater at the watershed scale. The maps of estimated local parameters and local R² in GWR models showcased the spatially varying relationships and indicated that the effects of land use on water quality varied over space. The results of this study reinforced the importance of watershed management in the planning, restoration, and management of stream water quality. It is also suggested that planners and managers may need to adopt different strategies, considering watershed characteristics—such as topographic features and meteorological conditions—and the source of pollutants, in managing stream water quality.

Impact of High-Density Urban Built Environment on Chronic Obstructive Pulmonary Disease: A Case Study of Jing'an District, Shanghai

Respiratory health is a focus of interdisciplinary studies involving urban planning and public health. Studies have noted that urban built environments have impacts on respiratory health by influencing air quality and human behavior such as physical activity. The aim of this paper was to explore the impact of urban built environments on respiratory health, taking chronic obstructive pulmonary disease (COPD) as one of the typical respiratory diseases for study. A cross-sectional study was conducted including all cases (N = 1511) of death from COPD in the high-density Jing'an district of Shanghai from 2001 to 2010. Proxy variables were selected to measure modifiable features of urban built environments within this typical high-density district in Shanghai. A geographically weighted regression (GWR) model was used to explore the effects of the built environment on the mortality of COPD and the geographical variation in the effects. This study found that land use mix, building width-height ratio, frontal area density, and arterial road density were significantly correlated to the mortality of COPD in high-density urban area. By identifying built environment elements adjustable by urban planning and public policy, this study proposes corresponding environmental intervention for respiratory health.

Exploring Variability in Landscape Ecological Risk and Quantifying Its Driving Factors in the Amu Darya Delta

Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD's downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran's I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

Spatial Distribution and Health Risk Assessment of Potentially Toxic Elements in Surface Soils of Bosten Lake Basin, Central Asia

A geographically weighted regression and classical linear model were applied to quantitatively reveal the factors influencing the spatial distribution of potentially toxic elements of forty-eight surface soils from Bosten Lake basin in Central Asia. At the basin scale, the spatial distribution of the majority of potentially toxic elements, including: cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), thallium (Tl), vanadium (V), and zinc (Zn), had been significantly influenced by the geochemical characteristics of the soil parent material. However, the arsenic (As), cadmium (Cd), antimony (Sb), and mercury (Hg) have been influenced by the total organic matter in soils. Compared with the results of the classical linear model, the geographically weighted regression can significantly increase the level of simulation at the basin spatial scale. The fitting coefficients of the predicted values and the actual measured values significantly increased from the classical linear model (Hg: r² = 0.31; Sb: r² = 0.64; Cd: r² = 0.81; and As: r² = 0.68) to the geographically weighted regression (Hg: r² = 0.56; Sb: r² = 0.74; Cd: r² = 0.89; and As: r² = 0.85). Based on the results of the geographically weighted regression, the average values of the total organic matter for As (28.7%), Cd (39.2%), Hg (46.5%), and Sb (26.6%) were higher than those for the other potentially toxic elements: Cr (0.1%), Co (4.0%), Ni (5.3%), V (0.7%), Cu (18.0%), Pb (7.8%), Tl (14.4%), and Zn (21.4%). There were no significant non-carcinogenic risks to human health, however, the results suggested that the spatial distribution of potentially toxic elements had significant differences.

Relationship between Air Pollution and Regional Longevity in Guangxi, China

Air pollution has become a global environmental challenge and poses major threats to human health, particularly for the aging population. However, few studies have investigated the effects of air pollutants on human longevity, especially based on the total regional quantities and sources. Based on investigation of the spatiotemporal variations of three air pollutants (PM₁₀, SO₂, and NOx) and three longevity indicators (centenarian ratio, centenarity index, and aging tendency), this study aims to identify the relationship between air pollution and regional longevity in Guangxi Province. Air pollutant and population data from 109 counties and areas of Guangxi were collected from environmental research reports and statistical yearbooks. Cluster and outlier analysis was used to detect the regions with high and low clusters of the longevity indicators and air pollutants. Geographically weighted regression analyses were performed to determine the relationship between longevity and air pollutants. A negative relationship between the air pollutants PM₁₀, SO₂, and NOx on the aged population was observed. From a provincial level, industrial sources from the urban areas of cities located in the central province, including Liuzhou, Nanning, Laibing, Guigang and Yulin, were important contributors to the air pollutants PM₁₀, SO₂, and NOx, and thus could contribute to negative impacts on regional longevity. The key findings from this study will provide a case for management of air pollutants based on public health policies in China as well as other developing communities.

Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City

Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban expansion and have significant influences on human activities, the environment, and socioeconomic development. Understanding the urban road network expansion pattern and its corresponding social and environmental effects is a reasonable way to optimize comprehensive urban planning and keep the city sustainable. This paper analyzes the spatiotemporal dynamics of urban road growth and uses spatial statistic models to describe its spatial patterns in rapid developing cities through a case study of Nanjing, China. A kernel density estimation model is used to describe the spatiotemporal distribution patterns of the road network. A geographically weighted regression (GWR) is applied to generate the social and environmental variance influenced by the urban road network expansion. The results reveal that the distribution of the road network shows a morphological character of two horizontal and one vertical concentration lines. From 2012 to 2016, the density of the urban road network increased significantly and developed some obvious focus centers. The development of the urban road network had a strong correlation with socioeconomic and environmental factors, which however, influenced it at different degrees in different districts. This study enhances the understanding of the effects of socio-economic and environmental factors on urban road network expansion, a significant indicator of urban expansion, in different circumstances. The study will provide useful understanding and knowledge to planning departments and other decision makers to maintain sustainable development.