Urban Mobility Viewer

In the spring of 2020, Ross Purves (my former PhD supervisor at the University of Zurich) approached Jo Wood (giCentre, City University London) and me with the idea of analyzing human mobility during the COVID pandemic. The Urban Mobility Viewer is the result of the cooperation that ensued.

It seeks to answer the following questions: How are we moving around our cities while facing a global pandemic? How have shutdowns and lockdowns affected movement? How do we accommodate urban travel that requires social distancing? Using sensors from open data sources, the Urban Mobility Viewer visualizes movement of people. Visualization reveals the role of weather, day of week, leisure, exercise and utility travel needs and compliance in lockdown measures. It helps to anticipate transport planning needs for a safe, sustainable urban travel future.

The visualizations encompass dynamic heatmaps displaying traffic counts for individual days. The heatmap is linked to a geographic map displaying the counter locations as well as to a line graph showing anomalies (the so-called Chi values) in traffic volume over time.

We implemented the Urban Mobility Viewer for the following cities, transport modes and languages:

Data ingestion and processing is done in R and orchestrated using GitHub Actions. The visualizations have been implemented by Jo using Elm, Vega and Vega-Lite.