Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (7): 1059-1081.doi: 10.1007/s11442-021-1885-1
• Research Articles • Previous Articles Next Articles
CHEN Yunhai(), JIANG Nan*(
), CAO Yibing, YANG Zhenkai, ZHAO Xinke
Received:
2020-10-21
Accepted:
2021-04-13
Online:
2021-07-25
Published:
2021-09-25
Contact:
JIANG Nan
E-mail:xwliky@qq.com;13653802609@163.com
About author:
Chen Yunhai (1987-), PhD, specialized in spatial information modeling and visualization. E-mail: xwliky@qq.com
Supported by:
CHEN Yunhai, JIANG Nan, CAO Yibing, YANG Zhenkai, ZHAO Xinke. Visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity[J].Journal of Geographical Sciences, 2021, 31(7): 1059-1081.
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Table 1
Reasoning results of the infectious relationships of the Shulan cases
Case | Source | Case | Source | Case | Source | Case | Source | Case | Source |
---|---|---|---|---|---|---|---|---|---|
1 | / | 11 | 2 | 21 | 11 | 31 | 22 | 41 | 22 |
2 | 1 | 12 | 8 | 22 | 15 | 32 | 17 | 42 | 40 |
3 | 1 | 13 | 10 | 23 | 11 | 33 | 28 | 43 | 15 |
4 | 1 | 14 | 2 | 24 | 16 | 34 | 31 | 44 | 43 |
5 | 1 | 15 | 10 | 25 | 17 | 35 | 22 | 45 | 43 |
6 | 1 | 16 | 10 | 26 | 4 | 36 | 39 | / | / |
7 | 1 | 17 | 10 | 27 | 24 | 37 | 39 | / | / |
8 | 1 | 18 | 10 | 28 | 11 | 38 | 4 | / | / |
9 | 2 | 19 | 10 | 29 | 17 | 39 | 2, 10 | / | / |
10 | 2 | 20 | 13 | 30 | 15 | 40 | 10 | / | / |
Figure 2
Prevalence of COVID-19 in China (a. Prevalence of COVID-19 in China on February 25, 2020; b. Prevalence of COVID-19 in Henan province, China, on February 20, 2020. This is based on the standard map production with map approval number GS (2019) No.1696 on the standard map service website of the Ministry of Natural Resources, China; the base map has not been modified; and the relevant source of the statistical data is the 2019 Statistical Yearbook of China (CD-ROM version).)
Figure 3
Spatial distributions of the impact factors of COVID-19 in China (a. Spatial distribution of China’s population in 2019; b. Spatial distribution of China’s number of passengers in 2019; c. Spatial distribution of China’s GDP in 2019; d. Spatial distribution of China’s number of healthcare personnel in 2019. This is based on the standard map production with map approval number GS (2019) No.1696 on the standard map service website of the Ministry of Natural Resources, China, and the base map has not been modified.)
Table 2
Classification of the impact factors of COVID-19 in China
Level | Population (104 people) | Number of passengers (108 people) | Number of healthcare personnel (104 people) | GDP (1012 yuan) |
---|---|---|---|---|
1 | < 934.0 | < 1.75 | < 13.25 | < 0.82 |
2 | < 2704.0 | < 3.20 | < 27.27 | < 2.04 |
2 | < 3941.0 | < 4.81 | < 33.09 | < 3.03 |
4 | < 4926.0 | < 7.16 | < 42.70 | < 4.11 |
5 | < 6899.0 | < 11.04 | < 62.40 | < 5.62 |
6 | < 8341.0 | < 14.21 | < 74.63 | < 7.65 |
7 | > 8341.0 | > 14.21 | > 74.63 | > 7.65 |
Table 3
Multi-level visual expressions of the case information
Expression level | Expression method |
---|---|
Macro-level | Spatio-temporal process simulation (attribute), three-dimensional dynamic thematic map |
Meso-level | Spatio-temporal process simulation (spatial form, trajectory, attribute) |
Micro-level | Spatio-temporal process simulation (spatial form, trajectory, attribute, infection link), dynamic thermodynamic map |
Figure 11
Visual analysis of the case information at the meso-level. (a. Visual analysis of cases in Henan province on January 22, 2020; b. Local amplification effect of visual analysis results of Henan cases on January 25, 2020; c. Local amplification effect of visual analysis results of Henan cases on February 2, 2020)
Table 4
Results of Global Morin’s I for the prevention of COVID-19 in China
Region | Value | Date | ||||||
---|---|---|---|---|---|---|---|---|
2020-01-20 | 2020-01-25 | 2020-01-30 | 2020-02-05 | 2020-02-10 | 2020-02-15 | 2020-02-20 | ||
China | Moran’s I | -0.06 | -0.02 | -0.03 | -0.04 | -0.05 | -0.05 | -0.05 |
Z value | -1.02 | 0.19 | -0.01 | -0.34 | -0.48 | -0.67 | -0.70 | |
P value | 0.31 | 0.85 | 1.00 | 0.73 | 0.64 | 0.50 | 0.48 | |
Henan province | Moran’s I | 0.01 | -0.05 | 0.02 | 0.14 | 0.11 | 0.12 | 0.11 |
Z value | 0.74 | 0.03 | 0.41 | 1.22 | 1.12 | 1.18 | 1.17 | |
P value | 0.46 | 0.98 | 0.68 | 0.22 | 0.26 | 0.24 | 0.24 |
Figure 13
Local spatial clustering characteristics of the prevalence of COVID-19 in China (a. Local spatial clustering characteristics of the prevalence of COVID-19 in China on February 25, 2020; b. Local spatial clustering characteristics of the prevalence of COVID-19 in Henan province, China, on February 20, 2020. This is based on the standard map production with map approval number GS (2019) No.1696 on the standard map service website of the Ministry of Natural Resources, China, and the base map has not been modified.)
Table 5
Results for the factor detector and interaction detector
Region | Impact factors | GDP | Number of passengers | Number of healthcare personnel | Population |
---|---|---|---|---|---|
China | GDP | 0.119942 | |||
Number of passengers | 0.335308↑ | 0.143713 | |||
Number of healthcare personnel | 0.505342↑ | 0.34431↑↑ | 0.238725 | ||
Population | 0.999561↑ | 0.510934↑ | 0.34469↑↑ | 0.233914 | |
Henan province | GDP | 0.272791 | |||
Number of passengers | 0.560397↑↑ | 0.387768 | |||
Number of healthcare personnel | 0.905541↑ | 0.952972↑ | 0.230124 | ||
Population | 0.762325↑ | 0.751544↑ | 0.394333↑↑ | 0.211114 |
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