Authors: Xiu Wu*, Texas State University - San Marcos
Topics: Geographic Information Science and Systems, Health and Medical
Keywords: Geostatistical Analysis COVID-19 County Level
Session Type: Virtual Paper
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 16
Presentation File: No File Uploaded
Given coronavirus disease (COVID-19) swept through the world, everything about people’s mobilities is changed. COVID-19, as a global social, human, and economic crisis, extremely impacts on people’s daily life and change people’s routine behaviors, especially the pandemic of the U. S is rapidly spreading. it is imperative to tease out the spatial changes of the COVID-19 pandemic spread, sensitive areas of vulnerability, and most vulnerable groups based on the county level. The objective of this project is to investigate the spatial association between population (age structure, race, gender) and COVID-19 incidence rate (confirmed cases and death cases) of 254 Texas counties, in the context of considering of impacts of social-economic (annual pay, unemployment, household- income) and environmental factors, via spatial stratified heterogeneity analysis of using Ordinary Least Square (OLS) and Geographic Weighted Regression (GWR) models in order to minimize infection risk of COVID-19 and reducing COVID-19 incidence rate. Principal components analysis (PCA) is used to reduce the number of variables to explain and to interpret the results. The results are shown that people’s age of 20-59 is highly positive related cumulative confirmed cases, the elder beyond 80 is a highly positive related fatality. GWR is one of the feasible ways to exhibit spatial heterogeneity of the county level.