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

Abstract Background: The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of health risk factors on COVID-19 rates in different places, but the problem of spatial heterogeneity has not been adequately addressed.Methods: In this paper, we developed an Eigenvector Spatial Filtering based spatially varying coefficient model (ESF-SVC) to reveal the spatially varying impact of certain health risk factors on the COVID-19 spread. The experiment was conducted during 7 weeks within two study extents (Hubei province and mainland China). Spatial varying coefficient maps were produced for spatial pattern discovery.Results: Results showed that the ESF-SVC model could take good control of over-fitting problems, with average adjusted R2 16.31% (in Hubei province) and 10.25% (in mainland China) higher than that of GWR. The cross validation RMSE of ESF-SVC model was also the lowest. In Hubei province, Population density and wind speed had a significant impact on COVID-19 infection rates and that their effect was constant across cities. While in mainland China, migration score, building density, temperature and altitude showed significant impact and their effect varies across space. The influence of migration score was less contributive and less significant in cities around Wuhan than cities farther away, while the altitude showed stronger contributions in high altitude cities.Conclusions: Our study hopes to provide not only a feasible path to solve the problem of spatial autocorrelation and spatial heterogeneity in COVID-19 characterization but also an intuitive way to discover spatial patterns in large study areas, which could help people and government awareness of the potential health risks and shed some light in COVID-19 control strategies.

Download Full-text

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010067 ◽

2022 ◽

Vol 11 (1) ◽

pp. 67

Author(s):

Meijie Chen ◽

Yumin Chen ◽

John P. Wilson ◽

Huangyuan Tan ◽

Tianyou Chu

Keyword(s):

Risk Factors ◽

Health Risk ◽

Spatial Heterogeneity ◽

Influencing Factors ◽

Mainland China ◽

Spatial Filtering ◽

Varying Coefficient Model ◽

Health Risk Factors ◽

Eigenvector Spatial Filtering ◽

Spatially Varying

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 R2 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 °C and 8.17 °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.

Download Full-text

Exploring Spatial Heterogeneity in Factors for Apartment Prices in Seoul Using Moran Eigenvector Spatial Filtering Based on Spatially Varying Coefficients

Journal of the Association of Korean Geographers ◽

10.25202/jakg.8.2.14 ◽

2019 ◽

Vol 8 (2) ◽

pp. 321-335

Author(s):

Hyeongmo Koo

Keyword(s):

Spatial Heterogeneity ◽

Spatial Filtering ◽

Varying Coefficients ◽

Eigenvector Spatial Filtering ◽

Spatially Varying

Download Full-text

An eigenvector spatial filtering based spatially varying coefficient model for PM2.5 concentration estimation: A case study in Yangtze River Delta region of China

Atmospheric Environment ◽

10.1016/j.atmosenv.2019.117205 ◽

2020 ◽

Vol 223 ◽

pp. 117205 ◽

Cited By ~ 1

Author(s):

Huangyuan Tan ◽

Yumin Chen ◽

John P. Wilson ◽

Jingyi Zhang ◽

Jiping Cao ◽

...

Keyword(s):

Yangtze River ◽

Spatial Filtering ◽

Yangtze River Delta ◽

Varying Coefficient Model ◽

Yangtze River Delta Region ◽

Concentration Estimation ◽

Eigenvector Spatial Filtering ◽

Spatially Varying ◽

Pm2.5 Concentration

Download Full-text

Analysis of Factors Influencing the Lake Area on the Tibetan Plateau Using an Eigenvector Spatial Filtering Based Spatially Varying Coefficient Model

Remote Sensing ◽

10.3390/rs13245146 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5146

Author(s):

Zhexin Xiong ◽

Yumin Chen ◽

Huangyuan Tan ◽

Qishan Cheng ◽

Annan Zhou

Keyword(s):

Influencing Factors ◽

Water Cycle ◽

Spatial Filtering ◽

Inhibitory Effect ◽

Tibet Plateau ◽

The Tibetan Plateau ◽

Lake Area ◽

Varying Coefficient ◽

Eigenvector Spatial Filtering ◽

Spatially Varying

Lakes on the Tibet Plateau (TP) have a significant impact on the water cycle and water balance, and it is important to monitor changes in lake area and identify the influencing factors. Existing research has failed to quantitatively identify the changes and influencing factors of lakes in different regions of the TP. Thus, an eigenvector spatial filtering based spatially varying coefficient (ESF-SVC) model was used to analyze the relationship between lake area and climatic and terrain factors in the inner watershed of the TP from 2000 to 2015. A comparison with ordinary regression and spatial models showed that the ESF-SVC model eliminates spatial autocorrelation and has the best model fit and complexity. The experiments demonstrated that precipitation, snow melt, and permafrost moisture release, as well as the area of vegetation and elevation difference in the watershed, can significantly promote the expansion of lakes, while evapotranspiration and days of mean daily temperature above zero have an inhibitory effect on lake area expansion. The degree of influence of each factor also differs significantly over time and across regions. Spatially quantitative modeling of lake area in the TP using the ESF-SVC method is a new attempt to provide novel ideas for lake research.

Download Full-text