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Collecting and visualizing data allows us to see critical issues: a surge in COVID cases, toxins in garden soil, where the climate is warming fastest. Tonight, we hear (and see!) three stories of how data visualization is being used to detect, inform, and spur action on COVID-19, environmental justice, and climate change.
This program features:
Hidden Complexities of Visualizing COVID-19 with Amanda Makulec
Amanda Makulek advocates for and explores ways of visualizing health data responsibly. She worked with data in global health programs for eight years before joining Excella as data visualization lead.
Visualizing and Personalizing Climate Change with Nadja Popovich
Nadja Popovich uses data and graphics to explain the science of climate change and its real-life impacts. She joined the New York Times climate desk in 2017.
Place-Based Visualizations and Information Design Efforts for and with Communities with Mónica Ramírez-Andreotta
Dr. Ramirez-Andreotta is an assistant professor of Soil, Water and Environmental Science (SWES) at the University of Arizona.
Drink Lab with Clay Reynolds
Learn to make a data-driven mashup of the world's most popular cocktails, according to ordering statistics from the last five years. Exploratorium beverage director and Seaglass Restaurant co-proprietor Clay Reynolds shows you how.
Looking for more? Dig into the resources and ideas shared by tonight’s speakers.
In her talk, Amanda Makulec refers to several different visualizations, charts, and readings. We've compiled them for you here:
Charts and Trackers
Our World in Data: Cumulative Cases
Coronavirus Charts from The Financial Times
Coronavirus Charts from The New York Times
COVID Exit Strategy
COVID Act Now
COVID Tracking Project Racial Data Dashboard
The Washington Post: Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”, by Harry Stevens
The Korean Clusters by Reuters
ProPublica: How to Understand COVID-19 Numbers, by Caroline Chen and Ash Ngu
How COVID-19 Case Data is Collected, by Amanda Makulec
Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study, by Tim K Tsang, Peng Wu, Yun Lin, Eric H Y Lau, Gabriel M Leung, Benjamin J Cowling
This program was made possible thanks to generous support from the Gordon and Betty Moore Foundation and the National Science Foundation under Grant No. 1811163. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Drink Lab Recipe
Find yourself in the middle of the Perfect Venn—a cocktail created by data.
The Perfect Venn