Visualizing Internet Measurements of Covid-19 Work-from-Home

Stutz, Erica and Pradkin, Yuri and Song, Xiao and Heidemann, John

citation

Erica Stutz, Yuri Pradkin, Xiao Song and John Heidemann 2021. Visualizing Internet Measurements of Covid-19 Work-from-Home. Proceedings of the National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium) (Virtual Workshop, Dec. 2021), to appear. [DOI] [PDF]

abstract

The Covid-19 pandemic disrupted the world as businesses and schools shifted to work-from-home (WFH), and comprehensive maps have helped visualize how those policies changed over time and in different places. We recently developed algorithms that infer the onset of WFH based on changes in observed Internet usage. Measurements of WFH are important to evaluate how effectively policies are implemented and followed, or to confirm policies in countries with less transparent journalism. This paper describes a web-based visualization system for measurements of Covid-19-induced WFH\@. We build on a web-based world map, showing a geographic grid of observations about WFH\@. We extend typical map interaction (zoom and pan, plus animation over time) with two new forms of pop-up information that allow users to drill-down to investigate our underlying data. We use sparklines to show changes over the first 6 months of 2020 for a given location, supporting identification and navigation to hot spots. Alternatively, users can report particular networks (Internet Service Providers) that show WFH on a given day. We show that these tools help us relate our observations to news reports of Covid-19-induced changes and, in some cases, lockdowns due to other causes. Our visualization is publicly available at \urlhttps://covid.ant.isi.edu, as is our underlying data.

reference

@inproceedings{Stutz21a,
  author = {Stutz, Erica and Pradkin, Yuri and Song, Xiao and Heidemann, John},
  title = {Visualizing {Internet} Measurements of
                    {Covid-19} Work-from-Home},
  booktitle = {Proceedings of the National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium)},
  year = {2021},
  sortdate = {2021-12-15},
  project = {ant, minceq, eieio},
  jsubject = {topology_modeling},
  pages = {to appear},
  month = dec,
  address = {Virtual Workshop},
  publisher = {IEEE},
  location = {johnh: pafile},
  keywords = {covid-19, work-from-home, visualization, trinocular},
  doi = {tbd},
  url = {https://www.isi.edu/%7ejohnh/PAPERS/Stutz21a.html},
  pdfurl = {https://www.isi.edu/%7ejohnh/PAPERS/Stutz21a.pdf},
  conferenceurl = {https://bigdataieee.org/BigData2021/SpecialSymposium.html}
}