CovidLens: Visually Understanding the Covid-19 Indicators through the Lens of Mobility Data
Mohamed A. Sharaf, Xiaozhong Zhang, Panos K. Chrysanthis, Wadima Alsaedi, Maitha Alkalbani, Heba Helal, Alyazia Aldhaheri · 2022 23rd IEEE International Conference on Mobile Data Management (MDM) · 2022
Since the onset of the Covid-19 pandemic, an over-whelming amount of related data has been released. In an attempt to gain insights from that data, multiple public data visualization dashboards have been deployed. Differently from such dashboards, which mainly support basic data filtering and visualization of separate datasets, in this work, we propose CovidLens, which: 1) integrates various Covid-19 indicators and is centred around the Google Community Mobility Report dataset, 2) supports similarity search for finding similar and correlated patterns and trends across the integrated datasets, and 3) automatically recommends insightful visualizations that unlocks valuable insights into the pandemic effects. To that end, we will be presenting the employed dataset, together with the design, implementation, and multiple usage scenarios of our proposed CovidLens.