Modeling Severe Acute Respiratory Syndrome Coronavirus 2019 (SARS-CoV-19) Incidence across Conterminous US Counties: A Spatial Perspective

This study examines the spatial distribution of COVID-19 incidence and mortality rates across the counties in the conterminous US in the first 604 days of the pandemic. The dataset was acquired from Emory University, Atlanta, United States, which includes socio-economic variables and health outcomes variables (N = 3106). OLS estimates accounted for 31% of the regression plain (adjusted R2 = 0.31) with AIC value of 9263, and Breusch-Pagan test for heteroskedasticity indicated 472.4, and multicollinearity condition number of 74.25. This result necessitated spatial autoregressive models, which were performed on GeoDa 1.18 software. ArcGIS 10.7 was used to map the residuals and selected significant variables. Generally, the Spatial Lag Model (SLM) and Spatial Error Model (SEM) models accounted for substantial percentages of the regression plain. While the efficiency of the models is the order of SLM (AIC: 8264.4: BreucshPagan test: 584.4; Adj. R2 = 0.56) > SEM (AIC: 8282.0; Breucsh-Pagan test: 697.2; Adj. R2 = 0.56). In this case, the least predictive model is SEM. The significant contribution of male, black race, poverty and urban and rural dummies to the regression plain indicated that COVID-19 transmission is more of a function of socio-economic, and rural/urban conditions rather than health outcomes. Although, diabetes and obesity showed a positive relationship with COVID-19 incidence. However, the relationship was relatively low based on the dataset. This study further concludes that the policymakers and health practitioners should consider spatial peculiarities, rural-urban migration and access to resources in reducing the transmission of COVID-19 disease.

How Does Air Pollution Influence Housing Prices in the Bay Area?

In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO2, and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral’s I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air conditions, low variability in pollutant concentrations has little effect on property values. These hypotheses could be verified with more high-resolution air pollution measurements with a diversity of regions.

The Spatial Distribution and Influencing Factors of Urban Cultural and Entertainment Facilities in Beijing

Cultural and entertainment facilities are an important mainstay for urban development and the well-being of urban residents. Studying their spatial distribution is thus of great significance for improving urban functions and shaping urban characteristics. This paper uses the Simpson index, grid method, kernel density, nearest neighbor analysis and hierarchical clustering analysis to present in detail the spatial pattern, hotspot distribution and clustering characteristics of urban cultural and entertainment facilities in Beijing. With the help of the spatial lag model, the main factors affecting the spatial distribution of the facilities are explored. The results are as follows: Different types of cultural and entertainment facilities have different spatial agglomeration effects, which are closely related to the historical background of Beijing, industrial distribution, and the living needs of residents; the facilities generally present a spatial distribution with prominent centrality, strong clustering and significant heterogeneity; and financial insurance institution density, building density, securities company density, housing rent and distance to nearest scenic spot are the main factors affecting the distribution of the facilities. Analyzing the distribution characteristics and influencing factors of urban cultural and entertainment facilities in Beijing will provide typical cases and decision-making references that can underpin the informed layout and planning of urban cultural and entertainment industries and facilities.

Effects of Educational and Cultural Facilities on Housing Prices in Seoul from an Accessibility Perspective

Background and objective: A great deal of previous research has highlighted the value of educational and cultural facilities embedded in housing prices, by taking a large spatial area as the focus, such as the city or district level. However, few studies have investigated the extent to which educational and cultural facilities influence the formation of housing prices from an accessibility perspective. This study aims to identify the value of educational and cultural facilities embedded in the housing prices in Seoul Metropolitan City with a focus on the concept of the residents’ neighbourhood and accessibility. Methods: To this end, this research used a spatial regression model with educational and cultural facilities as the independent variables and housing prices as the dependent variable. The model assessed the accessibility of cultural and educational facilities by considering geographic effects. Results: The findings are as follows. First, the spatial error model was found to be the best fit for multi-unit housing, while the spatial lag model was more appropriate for single-unit housing and apartments. Second, private educational facilities and art museums had positive effects on single- and multi-unit housing prices, while historical sites had a negative effect. Finally, private educational facilities positively influenced apartment prices, whereas public libraries and urban park areas had a negative effect. Conclusion: These findings indicate that the accessibility of educational and cultural facilities reflects residents’ preferences and needs, which will ultimately influence housing prices.

Spatial Pattern and Spatial Heterogeneity of Chinese Elite Hospitals: A Country-Level Analysis

Elite hospitals represent the highest level of Chinese hospitals in medical service and management, medical quality and safety, technical level and efficiency, which are also one of the important indicators reflecting high-quality medical resources in the region, and their spatial allocation is directly related to the fairness of health resource allocation. We explored the allocation pattern of high-quality resources and its influencing factors in the development of China's health system using geographic weighted regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), GWR and MGWR with Spatial Autocorrelation(GWR-SAR and MGWR-SAR), spatial lag model (SLM), and spatial error model (SEM). The results of OLS regression showed that city level, number of medical colleges, urbanization rate, permanent population and GDP per capita were its significant variables. And spatial auto-correlation of elite hospitals in China is of great significance. Further, its spatial agglomeration phenomenon was confirmed through SLM and SEM. Among them, the city level is the most important factor affecting the spatial allocation of elite hospitals in China. Its action intensity shows a solid and weak mosaic trend in the Middle East, relatively concentrated in some areas with medium intensity and concentrated in the West China. Obviously, China's elite hospitals are unevenly distributed and have evident spatial heterogeneity. Therefore, we suggest that we should pay attention to the spatial governance of high-quality medical resources, attract medical elites in the region, increase investment in medical education in the scarce areas of elite hospitals and develop tele-medicine service.

EKC Test of the Relationship between Nitrogen Dioxide Pollution and Economic Growth—A Spatial Econometric Analysis Based on Chinese City Data

During the just concluded 13th Five-Year Plan, China continued to maintain the momentum of rapid economic development, but still faced environmental pollution problems caused by this. Finding the relationship between Nitrogen Dioxide pollution and economic development is helpful and significant in better achieving and optimizing sustainable environmental development. Taking China’s 333 prefecture-level cities as samples from 2016 to 2018, the spatial lag model (SAR) was used to study the impact of economic growth on urban Nitrogen Dioxide pollution. The results show that Nitrogen Dioxide has strong positive characteristics of spatial spillover, but there is a linear relationship between economic growth and Nitrogen Dioxide concentration that slowly rises, and there is no inverted U-shaped relationship, which does not support the Environmental Kuznets Curve (EKC) hypothesis; The results also show the impact of per capita GDP, natural gas consumption, residential natural gas consumption, industrialization, and transportation development on the increase of Nitrogen Dioxide concentration, and the impact of green coverage on the decrease of Nitrogen Dioxide concentration. However, there is no significant relationship between technological investment and Nitrogen Dioxide concentration. The above conclusions are still valid after the robustness test, and recommendations are put forward to reduce Nitrogen Dioxide pollution.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal–geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.

The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

Temporal Geospatial Analysis of COVID-19 Pre-infection Determinants of Risk in South Carolina

Introduction: Disparities and their geospatial patterns exist in coronavirus disease 2019 (COVID-19) morbidity and mortality for people who are engaged with clinical care. However, studies centered on viral infection cases are scarce. It remains unclear with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs) for people with the infection. This work aimed to assess the geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina by different timepoints during the pandemic. Method: We used global models including spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR), as well as geographically weighted regression model (GWR) as a local model to examine the associations between COVID-19 infection rate and PIDRs. The data were retrieved from multiple sources including USAFacts, US Census Bureau, and Population Estimates Program. Results: The percentage of males and the percentage of the unemployed population were statistically significant (p values < 0.05) with positive coefficients in the three global models (SEM, SLM, CAR) throughout the time. The percentage of white population and obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models consistently have a better model fit than global models, suggesting non-stationary correlations between a region and its neighbors. Conclusion: Characterized by temporal-geospatial patterns, disparities and their PIDRs exist in COVID-19 incidence at the county level in South Carolina. The temporal-geospatial structure of disparities and their PIDRs found in COVID-19 incidence are different from mortality and morbidity for patients who are connected with clinical care. Our findings provided important evidence for prioritizing different populations and developing tailored interventions at different times of the pandemic. These findings provided implications on containing early viral transmission and mitigating consequences of infectious disease outbreaks for possible future pandemics.