DeepTrack: Monitoring and Exploring Spatio-Temporal Data - A Case of Tracking COVID-19

ABSTRACT

Spatio-temporal data analysis is very important in many time-critical applications. We take Coronavirus disease (COVID-19) as an example, and the key questions that everyone will ask every day are how does Coronavirus spread? where are the high-risk areas? where have confirmed cases around me? Interactive data analytics, which allows general users to easily monitor and explore such events, plays a key role. However, some emerging cases, such as COVID-19, bring many new challenges (C1) New information may come with different formats basic structured data such as confirmed/suspected/serious/death/recovered cases, unstructured data from newspapers for travel history of confirmed cases, and so on. (C2) Discovering new insights data visualization is widely used for storytelling;however, the challenge here is how to automatically find "interesting stories", which might be different from day to day. We propose DEEPTRACK, a system that monitors spatio-temporal data, using the case of COVID-19. For (C1), we describe (a) how we integrate and clean data from different sources by existing modules. For (C2), we discuss (b) how to build new modules for ad-hoc data sources and requirements, (c) what are the basic (or static) charts used;and (d) how to generate recommended (or dynamic) charts that are based on new incoming data. The attendees can use DeepTrack to interactively explore various COVID-19 cases.