As mentioned in attached thread, I see communicating exponential growth (virus spread) as one of the key challenges to helping people understand #COVID19.

We just don't think that way.

A couple of thoughts and critiques of #COVID19 vis ...

https://twitter.com/EvanMPeck/status/1235568532840120321?s=20

[1/x] I don't think using log scales on line charts is good for prompting public or govt action.

I suspect they give the perception containment, since they may visually level-off at moments when our actions need to be *more* agressive

See comparison below from worldometers

Creating data-driven maps of #COVID19 is hard, but I worry that a lot of people are being mislead because designers aren't externalizing the tradeoffs of their visual representations.

-- Why it's hard --
Problem 1: What do you do when your numbers probably don't reflect reality? Nearly all vis right now rely on confirmed positive tests of #COVID19 (left: CDC, right: NYTimes).

But as many experts have pointed out, these numbers dramatically underestimate reality.

I suspect that many interpret these maps as "number of people with the virus". That's bad

I couldn't be more thrilled that our paper - "Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania" - received a Best Paper Award at #chi2019! 🎉

Blog post soon, but until then:
- paper: arxiv.org/abs/1901.01920
- data: osf.io/uxwts/ If you never read our paper, read this:
- Interpreting data is *critical* in 2019
- Vis amplifies our interpretation, but probably not equally for everyone
- We need to do better understanding who does (and doesn't!) currently trust or value vis
- If we don't, we deepen divides