Visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity

doi:10.1007/s11442-021-1885-1

Abstract

Abstract:

Coronavirus disease 2019 (COVID-19) is continuing to spread globally and still poses a great threat to human health. Since its outbreak, it has had catastrophic effects on human society. A visual method of analyzing COVID-19 case information using spatio-temporal objects with multi-granularity is proposed based on the officially provided case information. This analysis reveals the spread of the epidemic, from the perspective of spatio-temporal objects, to provide references for related research and the formulation of epidemic prevention and control measures. The case information is abstracted, descripted, represented, and analyzed in the form of spatio-temporal objects through the construction of spatio-temporal case objects, multi-level visual expressions, and spatial correlation analysis. The rationality of the method is verified through visualization scenarios of case information statistics for China, Henan cases, and cases related to Shulan. The results show that the proposed method is helpful in the research and judgment of the development trend of the epidemic, the discovery of the transmission law, and the spatial traceability of the cases. It has a good portability and good expansion performance, so it can be used for the visual analysis of case information for other regions and can help users quickly discover the potential knowledge this information contains.

Key words: COVID-19, spatio-temporal objects, multi-granularity, case information, visualization, visual analysis, spatial correlation analysis

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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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

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

Region	Value	Date
Region	Value	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

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