Spatial Data Science of COVID-19 Data
Siyuan Shang, Carson K. Leung, Yubo Chen, Adam G.M. Pazdor
Abstract
Huge amounts of big data can be generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from viral diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data via data science helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. In this paper, we present a spatial data science system for analyzing big COVID-19 epidemiological data, with focus on the spatial data analytics among different geographic locations. The system helps users to get a better understanding of information about the confirmed cases of COVID-19. Evaluation results show the benefits of our system in spatial data analytics of big COVID-19 data.