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Jinting Zhang
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China

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Journal article
Published: 22 May 2021 in International Journal of Environmental Research and Public Health
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As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

ACS Style

Jinting Zhang; Xiu Wu; T. Chow. Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties. International Journal of Environmental Research and Public Health 2021, 18, 5541 .

AMA Style

Jinting Zhang, Xiu Wu, T. Chow. Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties. International Journal of Environmental Research and Public Health. 2021; 18 (11):5541.

Chicago/Turabian Style

Jinting Zhang; Xiu Wu; T. Chow. 2021. "Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties." International Journal of Environmental Research and Public Health 18, no. 11: 5541.

Research article
Published: 10 April 2021 in Environmental Science and Pollution Research
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Since COVID-19 is extremely threatening to human health, it is significant to determine its impact factors to curb the virus spread. To tackle the complexity of COVID-19 expansion on a spatial–temporal scale, this research appropriately analyzed the spatial–temporal heterogeneity at the county-level in Texas. First, the impact factors of COVID-19 are captured on social, economic, and environmental multiple facets, and the communality is extracted through principal component analysis (PCA). Second, this research uses COVID-19 cumulative case as the dependent variable and the common factors as the independent variables. According to the virus prevalence hierarchy, the spatial–temporal disparity is categorized into four quarters in the GWR analysis model. The findings exhibited that GWR models provide higher fitness and more geodata-oriented information than OLS models. In El Paso, Odessa, Midland, Randall, and Potter County areas in Texas, population, hospitalization, and age structures are presented as static, positive influences on COVID-19 cumulative cases, indicating that they should adopt stringent strategies in curbing COVID-19. Winter is the most sensitive season for the virus spread, implying that the last quarter should be paid more attention to preventing the virus and taking precautions. This research is expected to provide references for the prevention and control of COVID-19 and related infectious diseases and evidence for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.

ACS Style

Xiu Wu; Jinting Zhang. Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environmental Science and Pollution Research 2021, 28, 43732 -43746.

AMA Style

Xiu Wu, Jinting Zhang. Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environmental Science and Pollution Research. 2021; 28 (32):43732-43746.

Chicago/Turabian Style

Xiu Wu; Jinting Zhang. 2021. "Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)." Environmental Science and Pollution Research 28, no. 32: 43732-43746.

Preprint content
Published: 04 March 2021
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Since COVID-19 is extremely menacing human’s health, it is a significant to expose on its fator’s impacts for curbing the virus spreading. To tackle the complexity of COVID-19 expansion in spatial-temporal scale, This research is approriatedly analyzed the spatial-temporal heterogeneity at county-level in Texas. First,factors impacts of COVID-19 are captured on social, economic, and environmental multiple-facets and the Communality is extracted through Principal Component Analysis (PCA). Second, this research is used COVID-19 CC as the dependent variable and the common factors as the independent variable. According to the virus prevailing hierarchy, spatial-temporal disparity is are categorized four quarters in the modeling GWR analysis according to the virus prevailing hierarchy. The findings are exibited that GWR models provided higher fitness, more geodata-oriented information than OLS models. In Texas El Paso, Odessa, Midland, Randall and Potter County areas, population, hospitalization, and age structure presented static, positive influences on COVID-19 cumulative casesm, indicating they should be adopt stringent stratgies in curbing COVID-19. Winter is the most sensitive season for the virus spreading, implying the last quarter should be pay more attention to prevent the virus and take pracutions. This research are expected to provide references for preventing and controlling COVID-19 and related infectious dieseaces, evidences for disease surveillance and response systems to facilitate the appropriate uptake and reuse of geographical data.

ACS Style

Xiu Wu; Jinting Zhang. Exploration of Temporal-Spatially Varying Impacts on COVID-19 Cumulative Case in Texas Using Geographically Weighted Regression (GWR). 2021, 1 .

AMA Style

Xiu Wu, Jinting Zhang. Exploration of Temporal-Spatially Varying Impacts on COVID-19 Cumulative Case in Texas Using Geographically Weighted Regression (GWR). . 2021; ():1.

Chicago/Turabian Style

Xiu Wu; Jinting Zhang. 2021. "Exploration of Temporal-Spatially Varying Impacts on COVID-19 Cumulative Case in Texas Using Geographically Weighted Regression (GWR)." , no. : 1.

Journal article
Published: 20 January 2021 in Sustainability
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A spatial-temporal panel dataset was collected from 101 countries during 2006–2016. Using partial correlation (PC) and ordinary correlation (OR) analyses, this research examines the relationship between ecological footprint (EF) and subjective well-being (SWB) to measure environmental impacts on people’s happiness. Gross domestic product (GDP), urbanization rate (UR), literacy rate (LR), youth life expectancy (YLE), wage and salaried workers (WSW), political stability (PS), voice accountability (VA) are regarded as control variables. Total bio-capacity (TBC), ecological crop-land footprints (ECL), ecological grazing-land footprint (EGL), and ecological built-up land footprint (EBL) have significant positive influences on SWB, but ecological fish-land (EFL) has significant negative influences on SWB. Ecological carbon footprint (ECF) is significantly negatively related to SWB in developed countries. An increase in the amount of EF factors is associated with a country’s degree of development. Political social–economic impacts on SWB disguised environmental contribution on SWB, especially CBF impacts on SWB. The use of PC in examining the association between SWB and EF helps bridge a knowledge gap and facilitate a better understanding of happiness.

ACS Style

Jinting Zhang; F. Zhan; Xiu Wu; Daojun Zhang. Partial Correlation Analysis of Association between Subjective Well-Being and Ecological Footprint. Sustainability 2021, 13, 1033 .

AMA Style

Jinting Zhang, F. Zhan, Xiu Wu, Daojun Zhang. Partial Correlation Analysis of Association between Subjective Well-Being and Ecological Footprint. Sustainability. 2021; 13 (3):1033.

Chicago/Turabian Style

Jinting Zhang; F. Zhan; Xiu Wu; Daojun Zhang. 2021. "Partial Correlation Analysis of Association between Subjective Well-Being and Ecological Footprint." Sustainability 13, no. 3: 1033.