Journal of Geographical Sciences ›› 2021, Vol. 31 ›› Issue (7): 1059-1081.doi: 10.1007/s11442-021-1885-1

• Research Articles • Previous Articles     Next Articles

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

CHEN Yunhai(), JIANG Nan*(), CAO Yibing, YANG Zhenkai, ZHAO Xinke   

  1. Institute of Geographic Space Information, Information Engineering University, Zhengzhou 450052, China
  • Received:2020-10-21 Accepted:2021-04-13 Online:2021-07-25 Published:2021-09-25
  • Contact: JIANG Nan;
  • About author:Chen Yunhai (1987-), PhD, specialized in spatial information modeling and visualization. E-mail:
  • Supported by:
    National Key Research and Development Program of China(2016YFB0502300)


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