The Geographical Distribution and Influencing Factors of COVID-19 in China
Abstract
:1. Introduction
1.1. Background
1.2. Literature Review
1.3. Aim and Question
2. Research Design
2.1. Study Area: China
2.2. Research Steps and Data Sources
2.3. Research Methods and Index Selection
2.3.1. Cluster Analysis and Standard Deviation Ellipse
2.3.2. Spatial Autocorrelation Analysis
2.3.3. Geodetector
3. Results
3.1. Spatial Heterogeneity and Relevance Analysis
3.2. Influencing Factors and Effect Analysis
4. Discussion
4.1. High Spatial Heterogeneity of COVID-19 in the Spatial Distribution and Spread
4.2. High Complexity of COVID-19 Spatial Differentiation Driving Factors and Their Interaction Effects
4.3. Precise Policy Design Oriented to Core Drivers and Their Interaction Effects
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Graphical Representation | Description | Interaction |
---|---|---|
| )) | non-linear Weaken |
| )) | unitary-non-linear Weaken |
| )) | bifactor enhancement |
| ) | Independent |
| ) | non-linear enhancement |
Variable | Index | Code | Type |
---|---|---|---|
Dependent variable | Number of Patients | ||
Number of Deaths | |||
Independent variable | Gross Domestic Product (GDP) | Economic driving force | |
Per-GDP | |||
Revenue | |||
Expenditure | |||
Number of Industrial Enterprises | |||
Population Density | Social driving force | ||
Area of Urban Construction Land | |||
Number of Hospital Beds | |||
Number of Licensed (Assistant) Doctors | |||
Industrial NOx Emissions | Ecological and Environmental driving force | ||
Industrial Smoke and Dust Emission | |||
Area of Green | |||
Area of Parks | |||
Number of Parks | |||
Number of Free Parks |
Global Moran’s I | Confidence Level | ||
---|---|---|---|
Number of patient and death | Patients | 0.009 | 0.008 |
Deaths | 0.002 | 0.010 | |
Proportion of patient and death per million population | Patients | 0.030 | 0.004 |
Deaths | 0.011 | 0.009 | |
Proportion of patient and death per hundred square kilometers | Patients | 0.010 | 0.013 |
Deaths | 0.003 | 0.023 |
-Statistic | Influence | -Statistic | Influence | |||
---|---|---|---|---|---|---|
economic | Gross Domestic Product (GDP) | X1 | 0.49 ** | 0.39 | 0.18 ** | 0.20 |
Per-GDP | X2 | 0.21 | 0.08 | |||
Revenue | X3 | 0.06 | 0.11 ** | |||
Expenditure | X4 | 0.49 ** | 0.32 ** | |||
Number of Industrial Enterprises | X5 | 0.19 ** | 0.05 | |||
social | Population Density | X6 | 0.09 | 0.36 | 0.02 | 0.17 |
Area of Urban Construction Land | X7 | 0.50 ** | 0.10 ** | |||
Number of Hospital Beds | X8 | 0.33 ** | 0.24 ** | |||
Number of Licensed (Assistant) Doctors | X9 | 0.25 ** | 0.07 | |||
ecological and environmental | Industrial NOx Emissions | X10 | 0.32 ** | 0.32 | 0.13 ** | 0.20 |
Industrial Smoke and Dust Emission | 0.49 ** | 0.24 ** | ||||
Area of Green | 0.33 ** | 0.24 ** | ||||
Area of Parks | 0.26 ** | 0.11 | ||||
Number of Parks | 0.32 ** | 0.07 | ||||
Number of Free Parks | 0.19 ** | 0.00 |
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Li, W.; Zhang, P.; Zhao, K.; Zhao, S. The Geographical Distribution and Influencing Factors of COVID-19 in China. Trop. Med. Infect. Dis. 2022, 7, 45. https://doi.org/10.3390/tropicalmed7030045
Li W, Zhang P, Zhao K, Zhao S. The Geographical Distribution and Influencing Factors of COVID-19 in China. Tropical Medicine and Infectious Disease. 2022; 7(3):45. https://doi.org/10.3390/tropicalmed7030045
Chicago/Turabian StyleLi, Weiwei, Ping Zhang, Kaixu Zhao, and Sidong Zhao. 2022. "The Geographical Distribution and Influencing Factors of COVID-19 in China" Tropical Medicine and Infectious Disease 7, no. 3: 45. https://doi.org/10.3390/tropicalmed7030045