标题: Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

作者: Chen, MJ (Chen, Meijie); Chen, YM (Chen, Yumin); Wilson, JP (Wilson, John P.); Tan, HY (Tan, Huangyuan); Chu, TY (Chu, Tianyou)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 卷: 11 期: 1 文献号: 67 DOI: 10.3390/ijgi11010067 出版年: JAN 2022

摘要: The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R-2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 degrees C and 8.17 degrees C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

作者关键词: COVID-19; spatial heterogeneity; eigenvector spatial filtering; spatially varying coefficients

地址: [Chen, Meijie; Chen, Yumin; Tan, Huangyuan; Chu, Tianyou] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wilson, John P.] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA 90089 USA.

通讯作者地址: Chen, YM (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: chen_meijie@whu.edu.cn; ymchen@whu.edu.cn; jpwilson@usc.edu; tanhuangyuan@whu.edu.cn; chutianyou@whu.edu.cn

影响因子：2.899