Abstract
The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world. In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on. The increasingly complex data poses a significant challenge to understand. A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage. Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation. Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified.
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CITATION STYLE
Zhou, Y., He, H., Rong, J., Cheng, Y., Li, Y. C., Zhong, W., & Jiang, F. (2020). Visual Analysis and Exploration of COVID-19 Based on Multi-source Heterogeneous Data. In Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020 (pp. 62–69). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00029
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