Welcome to whose bar chart is it anyway: where the geometries are made up and the numbers don’t matter. [via @dannypage]

Kurzgesagt, in collaboration with Our World in Data, tackle the question of who is responsible for climate change and who should fix it. As you might imagine, the answer is not always straightforward.

Maybe you’ve seen a chart and wished you could look at the data yourself. Maybe you want to see it from a different angle. But the underlying dataset is nowhere to be found. The WebPlotDigitizer by Ankit Rohatgi lets you load an image and it will attempt to pull out the dataset. Amazing.

I can’t believe this has been around since 2010, and I’m just now hearing about it. [via @jburnmurdoch]

New York Times Opinion compared several demographics, such as unemployment and income, between majority-black and majority-white neighborhoods in the United States.

They come back to the zipper chart technique where the dots start together and then separate to emphasize the gaps. Horizontally, dots are sorted by smallest to largest difference.

Dylan Tallchief recreated “Take On Me” by a-ha in Excel.

It’s not the tools. It’s how you use them. Something something blah blah. It’s in Excel!

If you’re looking for visual inspiration, one or two centuries back is a good place to start.

The name Karen. It’s not a common baby name these days. It peaked in the 1960s. The Pudding looked for other names in US history that followed similar trends:

To put this question to the test, we checked baby names from the last 100 years and eliminated those that: 1) never made it into the Top 20 most popular names in any year and 2) were not present in the top list for at least 50 out of 100 years. That left us with 129 female names and 76 male names (yes, we’re going there too!). We tested each of these names, looking for the ones that most closely matched Karen’s rise and fall in popularity.

You can also search for your own name to see if it’s a “future Karen.”

All you need is an old table, gift wrapping paper, and some varnish. I’m gonna have to do this. [via @datavisFriendly]

As a 100-day project, Alli Torban has been imagining what a data visualization designer’s wallpaper might look like through the years. She started in 1920, and with one design per year, she’s up to 1989.

The focus on aesthetics shows slow shifts in colors and patterns through time. Although I feel like the early 1980s, when The Visual Display of Quantitative Information was first published, should look super minimalist with a lot of space.

Good stuff.

Robert Hodgin built a procedural system he calls Meander to generate the beauty above, among several others:

My all-time favorite map-based data visualization was created in 1944. Harold Fisk, working with the US Army Corp. of Engineers, mapped the length of the Mississippi River. What sets his visualization apart from others is that he maps the river through time, and manages to do so in a way that is both beautiful and surprisingly effective. I want to pay homage to his series of maps by creating my own system for procedurally generating maps of meandering rivers.

Great.

Not only is the winding path imaginary, but so is the terrain, the place names, and the built-up lakes.

See also: the 1944 Fisk map that inspired Hodgin’s work, which is an interesting contrast against modern satellite imagery animations.

The right baseline provides a way to compare everything else in a useful way. The wrong baseline makes the rest of the data useless.

The Economist launched their 2020 elections forecast. Right now a part of my brain is telling me to avoid election forecasts this year, but the other part of me is like, don’t fight it, you know you’re going to look.

At least The Economist put their modeling code up on GitHub (implemented in R and Stan) and is publishing their polling data (linked at the bottom of the forecast page as a Google sheet).

As we know, it typically takes years to develop a vaccine that is approved for wide scale use. For the coronavirus, researchers are trying to speed up that timeline. Jonathan Corum and Carl Zimmer for The New York Times have started a vaccine tracker to keep watch.

They’ve categorized the vaccines by phase and those that are part of Operation Warp Speed. (Earmarked for later: a closer look at government program names.)

I know it seems like ages ago when we were talking about flattening the curve, but it was a rallying cry at some point. The charts that started it all weren’t particularly fancy or something to admire. For Mother Jones, Abigail Weinberg wondered why it still worked:

There were axes and legends, and Drew Harris, a professor of population health, would later add a line representing the capacity of the health care system, but in truth there was nothing particularly rigorous about the chart. It was a work of the imagination, too artless to be art but lacking the hard empiricism we expect of science. That in-betweenness is what made it so effective.

At the time, there were so many unknowns that projections seemed hard to grasp onto. But the visual concreteness of a chart, even though it was abstract and not based on actual data, seemed to be just enough certainty.

To reopen safely, meatpacking plants have to take precautions to provide space and separation for workers. But the process typically involves a lot of people working close together. The New York Times illustrates the process and the challenges moving forward.

The numbers are from a survey by the Pew Research Center conducted in 2016. I suspect the percentages are higher right now, but the gaps between police and public perception seem to say a lot. It’s easy to see where “one bad apple” comes from.

A large proportion of those who died from Covid-19 had pre-existing medical conditions. The percentage of those who have pre-existing medical conditions changes a lot by income group. Based on estimates from the Centers for Disease Control and Prevention, we can see by how much. For New York Times Opinion, Yaryna Serkez charted the difference for the largest cities in the United States.