Data Stories

We hope everyone is doing well! We finally decided to record an episode on visualization and covid19. It’s been a crazy several weeks and one of the most interesting developments has been to see how prominent visualization has been in the constant flux of information. Who expected visualization to be so relevant, uh?!

And when we talk about data and pandemics we could not find a better person than Carl Bergstrom, Professor of Biology at University of Washington, with a background in epidemiology but also an expert in scientific practices and communication.

You may remember Carl from an episode about three years ago (Episode 97). We interviewed him together with his colleague Jevin West to talk about their excellent “Calling Bullshit” project (and let’s face it, there is no lack of BS during these crazy times), a course (and soon to be a book) on how to spot BS in science.

Carl has been a constant source of information and reasoning on Twitter. Commenting on the science behind pandemics but also about the way science is communicated and the many possible traps you may fall into. If there is one thing we all learned is that visualization without reliable data is a mess!

In the show, we talk about a number of iconic covid19 visualizations, the “flatten the curve” ones, the tracking lines from Financial Times and several simulations. For each of these we discuss the many variations and nuances, what we have learned from them and the many intricacies of creating visualizations for such a sensitive topic with potential huge outcomes.

[Our podcast is fully listener-supported. That’s why you don’t have to listen to ads! Please consider becoming a supporter on Patreon or sending us a one-time donation through Paypal. And thank you!]

Links:

• Prof. Carl Bergstrom
• Siouxsie Wiles’ Flatten the Curve cartoon
• Medium: Don’t Flatten the Curve
• Vox: Coronavirus mitigation could kill thousands. Suppress the virus, don’t just “flatten the curve.”
• Scientific American: Leading with the Unknowns in COVID-19 Models
• WaPo: Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”
• Carl’s tweet on normalizing by population size (or not?)
• Matthew Fox’s Tweet on Uncertainty Bounds in the IHME model

Stats and Tracking:

• COVID Projections Tracker
• FT’s tracker: Coronavirus tracked: the latest figures as countries fight to contain the pandemic
• Our World in data: Coronavirus Pandemic (COVID-19) – Statistics and Research
• John Hopkins tracker: COVID-19 Map – Johns Hopkins Coronavirus Resource Center

Visual Simulations:

• People of the Pandemic | a hyperlocal cooperative simulation game
• Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”
• What Happens Next? COVID-19 Futures, Explained With Playable Simulations
• Outbreak

Related episodes

• Calling Bullshit with Carl Bergstrom and Jevin West

This is our second episode of our mini-series on data visualization agencies. For this episode we have Caroline Goulard from Dataveyes and Gabriele Rossi from Accurat. With them we talk about their quintessential projects, how to balance experimental with regular customers’ projects, and how the data visualization field changed over the years.

[Our podcast is fully listener-supported. That’s why you don’t have to listen to ads! Please consider becoming a supporter on Patreon or sending us a one-time donation through Paypal. And thank you!]

Enjoy the show!

This is the first episode of a mini-series of Data Visualization agencies. After so many years producing this podcast we realized we never really focused on agencies and we decided it’s time to rectify! Agencies have slowly become one of the most relevant realities of the data visualization ecosystem and business landscape. In this first episode, we have Thomas Clever to talk about Clever Franke and Benjamin Wiederkehr from Interactive Things. They have been around for more than 10 years and they have produced some really amazing work.

On the show we talk about their quintessential projects, what makes agencies different from individual freelancers and other organizations, the realities and challenges of running an agency and much more.

[Our podcast is fully listener-supported. That’s why you don’t have to listen to ads! Please consider becoming a supporter on Patreon or sending us a one-time donation through Paypal. And thank you!]

Enjoy the show!

Links:

• IXT: Education Inequalities + Education Progress
• CF: Chicago’s Mobility
• Paper: Data Changes Everything
• Sensor lab (https://www.sensorlab.nl/)
• Blog post on the process boards used to organize work at IXT

We have Barbara Tversky with us to talk about “spatial cognition”; the way humans perceive space and how space perception is related to the many ways we think. Barbara is a renowned cognitive scientist and a Professor of Psychology and Education at Columbia University. She has an extensive literature on spatial cognition and specific research on how people perceive and use diagrams, maps and other visual representations.

On the show we talk about her new book called “Mind in Motions” in which Barbara describes her research and the many fascinating ways space and motion play a pivotal role in the way we think. We also talk about the role of space in data visualization and the many fascinating ways in which spatial cognition can inform visualization design.

[Our podcast is fully listener-supported. That’s why you don’t have to listen to ads! Please consider becoming a supporter on Patreon or sending us a one-time donation through Paypal. And thank you!]

Enjoy the show!

Check out Barbara’s book “Mind in Motion“.

In this episode we have PhD students Yongsu Ahn and Alex Cabrera to talk about two separate data visualization systems they developed to help people analyze machine learning models in terms of potential biases they may have. The systems are called FairSight and FairVis and have slightly different goals. FairSight focuses on models that generate rankings (e.g., in school admissions) and FairVis more on comparison of fairness metrics. With them we explore the world of “machine bias” trying to understand what it is and how visualization can play a role in its detection and mitigation.

[Our podcast is fully listener-supported. That’s why you don’t have to listen to ads! Please consider becoming a supporter on Patreon or sending us a one-time donation through Paypal. And thank you!]

Enjoy the show!

Links:

• Alex Cabrera
• Yongsu Ahn
• FairSight
• FairVis
• Google: “Attacking Discrimination with Smarter Machine Learning”
• Nicky Case: “Parable of Polygons”

Related episodes

• Visualizing Fairness in Machine Learning with Yongsu Ahn and Alex Cabrera