Medicine
56 posts
Over the weekend, Georgia's State Health Department agitated a lot of people when it published the following chart: (This might have appeared a week ago as the last date on the chart is May 9 and the title refers to "past 15 days".) They could have avoided the embarrassment if they had read my article at DataJournalism.com (link). In that article, I lay out a set of the "unspoken conventions,"...
Like many others, some Floridians are questioning their state's Covid statistics. It's clear there are numerous "degrees of freedom" for politicians to manipulate the numbers. What's not clear is who's influencing these decisions. Are they public-health experts, donors, voters, or whom? A Twitter follower sent in the following chart, embedded in an informative article in Sun-Sentinel: I like the visual design. It's clean, and conveys a moderately complex concept effectively....
Been busy with an exciting project, which I might talk about one day. But I promised some people I'll follow up on Covid symptoms data visualization, so here it is. After I posted about the Venn diagram used to depict self-reported Covid-19 symptoms by users of the Covid Symptom Tracker app (reported by Nature), Xan and a few others alerted me to Twitter discussion about alternative visualizations that people have...
A twitter follower did not appeciate this chart from Nature showing the collection of flu-like symptoms that people reported they have to an UK tracking app. It's a super-complicated Venn diagram. I have written about this type of chart before (see here); it appears to be somewhat popular in the medicine/biology field. A Venn diagram is not a data visualization because it doesn't plot the data. Notice that the different...
On my sister (book) blog, I published a mega-post that examines the Oxford study that was cited two weeks ago as a counterpoint to the "doomsday" Imperial College model. These studies bring attention to the art of statistical modeling, and those six posts together are designed to give you a primer, and you don't need math to get a feel. One aspect that didn't make it to the mega-post is...
A few weeks ago, I replied to a tweet by someone who was angered by the amount of bad graphics about coronavirus. I take a glass-half-full viewpoint: it's actually heart-warming for dataviz designers to realize that their graphics are being read! When someone critiques your work, it is proof that they cared enough to look at it. Worse is when you publish something, and no one reacts to it. That...
The pie chart about COVID-19 worries illustrates why we should follow a basic rule of constructing color legends: order the categories in the way you expect readers to encounter them. Here is the chart that I discussed the other day, with the data removed since they are not of concern in this post. (link) First look at the pie chart. Like me, you probably looked at the orange or the...
The pressure of the coronavirus news cycle has gotten the better of some graphics designers. Via Twitter, Mark B sent me the following chart: I applied the self-sufficiency test to this pie chart. That's why you can't see the data which were also printed on the chart. The idea of self-sufficiency is to test how much work the visual elements of the graphic are doing to convey its message. Look...
Another day, another Twitter user sent a sloppy chart featured on TV news. This CNN graphic comes from Hugo K. by way of Kevin T. And it's another opportunity to apply the self-sufficiency test. Like before, I removed the data printed on the graphic. In reading this chart, we like to know the number of U.S. reported cases of coronavirus relative to China, and Italy relative to the U.S. So,...
As we move into the next phase of the dataviz bonanza arising from the coronavirus pandemic, we will see a shift from simple descriptive graphics of infections and deaths to bivariate explanatory graphics claiming (usually spurious) correlations. The FT is leading the way with this effort, and I hope all those who follow will make a note of several wise decisions they made. They source their data. Most of the...