Well, here I am, after three months, finally posting again.
It’s been a while, but for good reason:
🎉 🥂 I finally have a job in data visualization! 🥂 🎉
It’s very like me to run a data viz blog for years, get paid actual dollars for freelance work, and still doubt whether or not I’m qualified enough to do viz as a full-time job. It took ages for me to get off my butt and try to leave my old career in finance behind. The job search took a lot out of me, but I’m super excited it worked out!
In other good news, Quartz published an article I wrote about the ecological cost of NFTs. It’s another collaboration with Liana Sposto, who did wonderful illustrations to explain how the blockchain works and why it’s so ecologically terrible. If you liked the Pudding article we did, you’ll definitely like this one too!
And now back to my not-so-regularly-scheduled data viz content.
When it rains, does it pour?
Folk wisdom says that it does, but as people of science we demand hard DATA! We can no longer be shackled by the chains of metaphor, but must step into the light of taking things too literally.
So here we go–a lovely map that shows whether it really does pour when it rains.
I defined “pouring” as rainfall of at least 0.15 inches per hour. I set this cutoff by waiting until it was pouring (not a hard thing to do in Portland in winter) and then checking hourly accumulations.
Then I looked to see what percent of time it was pouring, given that it was already raining. (“When it rains, it pours”)
The data shown comes from the Copernicus Climate Data Store, which provides global hourly rainfall at a fairly detailed resolution. I only mapped data for the year 2020, when if ever it rained, it poured.
I did my best to research and annotate weather systems of interest. I am no meteorologist, though, so if one is reading and would like to update or correct me, please feel free!
The next question I had was so what? Is this just a map of overall rainfall, or does “pouring” follow a different pattern than “raining”?
To answer that, I turned to something I’ve never used before–a bivariate choropleth. These are maps that attempt to show two variables at once through two overlapping color schemes.
I’ve previously found them confusing and difficult to read; I’m always jumping back and forth between the map and the legend to see what a particular color actually means. To hopefully alleviate this, I included annotations to highlight areas to pay attention to. Again, not a meteorologist, just someone with Google.
In essence, this map shows that areas that receive heavy rain don’t necessarily receive a lot of it. And areas that receive a lot of rain don’t necessarily receive heavy rain.
If that’s confusing, please simply enjoy this map’s “Dixie Cup energy” (per Liana).
So now what?
IDK! I start my new job soon, and I have a few last freelance projects to wrap up this spring/summer.
I want to continue to make data viz for myself, but I’m not going to push myself to cram it in while working multiple jobs. Hopefully it won’t take three months for me to find the time, though!