Abstract
In the early months of 2020, as the novel coronavirus spread around the globe, we all turned to graphics and data visualizations in order to make sense of the unfolding catastrophe. What were the latest case counts? How many people were in ICUs? What were epidemiological models predicting the case load would be in a month? In response, journalists, academics, and amateurs generated an astonishing amount of visualizations. Novel graphical forms and approaches to the data appeared amid the more traditional maps and case count charts. This creativity was in part a result of the challenges of depicting data which was exponentially increasing, from countries and states with vast differences in population size and different dates of initial infection, resulting from an assortment of testing strategies and public health interventions. Some approaches distilled complex information down into very simple (but imprecise) representations, while others provided incredibly detailed data that obscured the messy real-world situation with precise numbers and ratios. These graphics and trade offs highlight how little we know about graphical perception and visual numeracy, and how important it is to understand the impact of graphical design choices when communicating scientific information in a visual domain. Using COVID-related graphics, this presentation will examine what we know, what we think we know, and what we still need to explore to create useful, accurate, and informative statistical graphics.
Location
SAMSI, Data Science, Statistics, and Visualization
Event Type: Conference
Location: Online