Visualizing Internet Measurements of Covid-19 Work-from-Home
2021 IEEE International Conference on Big Data, Big Data 2021
; : 5633-5638, 2021.
Article
in English
| Scopus | ID: covidwho-1730853
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 https//covid.ant.isi.edu, as is our underlying data. © 2021 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 IEEE International Conference on Big Data, Big Data 2021
Year:
2021
Document Type:
Article
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