The Covid-19 pandemic is generating waves of data points from around the world, recording the number of tests performed, cases confirmed, patients recovered, and people who have died from the virus. As these data are continuously updated, media outlets, government agencies, academics, and data-packaging firms are racing to make sense of the numbers, using novel design and visualization tools to chart and graph the virus many different contexts. In general, data visualizations can help people quickly distill an otherwise overwhelming flood of numbers. Catherine D’Ignazio, assistant professor of urban science and planning at MIT, says it is critical that data are visualized responsibly in a pandemic. D’Ignazio is the director of the Data and Feminism Lab, where she uses data and computational techniques to work toward gender and racial equity. MIT News spoke with her about the current boom in Covid-19 data visualizations, and how data visualizers can help us make sense of the pandemic’s uncertain numbers. Q: How have you seen data visualization of Covid-19 evolve in the last few months, since the virus began its spread? A: The first thing I’ll note is that there has been an explosion of data visualization. Since the information about the virus comes in numbers — case counts, death counts, testing rates — it lends itself easily to data visualization. Maps, bar charts, and line charts of confirmed cases predominated at first, and I would say they are still the most common forms of visualization that we are seeing in media reporting and on social media. As a person in the field, the proliferation is both exciting, because it shows the relevance of visualization, and scary, because there is definitely some irresponsible use of visualization. Many high-profile organizations are plotting case counts on graduated color maps, which is a big no-no unless you have normalized your numbers. So California, a big and densely populated state, will always appear to be worse off in absolute raw case counts. Conversely, this way of plotting could cause you to miss small states with a high rate of infection since they will be low in relative case numbers and would always show up in lighter colors on the map. Second, as the crisis has developed, media outlets are mapping things other than simply case counts or death rates. There have been many versions of the “flatten the curve” chart. This one is interesting because it’s not about plotting specific numbers, but about explaining a public health concept to a broad audience with a hypothetical chart. The best visual explanation I’ve seen of the flatten the curve concept is from The Washington Post and comes with simulations and animations that explain virus transmission. There have also been a number of visualizations of how social distancing has changed people’s mobility behavior , shifting traffic patterns , and even a global satellite map where you can see how Covid-19 has reduced urban pollution over the past three months. Finally, this crisis is posing some difficult visual communication problems: […]

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