With tonight’s Mega Millions jackpot estimated at $1.28 billion, you might be wondering what the odds of winning are, even if you know the chances are super slim for an individual. (On the other hand, the more tickets purchased overall, the greater the chances that someone in the country wins.) For The Washington Post, Bonnie Berkowitz and Shelly Tan made a playful quiz to test your perception of 1 in 302.6 million.

DALL-E is an AI system from OpenAI that creates images from text. You can enter very random things and get very real-looking output. So of course someone entered “data visualization in the style of insert-anything-here” for a wide array of inspiration. I’m partial to the bar chart made out of cake.

Here’s the good stuff for July.

Christopher Flavelle, for The New York Times, reported on the lack of support from the Federal Emergency Management Agency for those who were displaced by natural disasters. Area charts by Mira Rojanasakul show how much the support has been lagging.

RStudio, the company behind the IDE of the same name, are changing their name to Posit:

Our charter defines our mission as the creation of free and open source software for data science, scientific research, and technical communication. This mission intentionally goes beyond “R for Data Science”—we hope to take the approach that’s succeeded with R and apply it more broadly. We want to build a company that is around in 100 years time that continues to have a positive impact on science and technical communication. We’ve only just started along this road: we’re experimenting with tools for Python and our new Quarto(opens in a new tab) project aims to impact scientific communication far beyond data science.

It doesn’t seem that long ago when I heard about some new IDE for R that might make programming easier. It’s been cool to see the company grow over the years, and I take it as an indicator for the growth of data work in general, which goes beyond the software or a single language.

In 2012, Thomas Davenport and DJ Patil outlined a budding career choice called “data science” where people, with a combination of programming and statistics, made sense of “big” datasets. For Harvard Business Review, Davenport and Patil revisit the career ten years later:

A decade later, the job is more in demand than ever with employers and recruiters. AI is increasingly popular in business, and companies of all sizes and locations feel they need data scientists to develop AI models. By 2019, postings for data scientists on Indeed had risen by 256%, and the U.S. Bureau of Labor Statistics, predicts data science will see more growth than almost any other field between now and 2029. The sought-after job is generally paid quite well; the median salary for an experienced data scientist in California is approaching $200,000.

So data science is looking pretty strong.

At the time of their first article, I was writing my dissertation and finishing my PhD in statistics. I wondered what I was going to do after. Statisticians, including me, were resistant to data science, or at the least had mixed feelings about it. They felt they were already doing it, so there was no need for a new field of study. Plus, statistician was already declared the “sexy” job of the decade three years prior. We still had time left.

I don’t hear those arguments anymore. There were overlapping skills to start, and the overlap seemed to increase over time. The label seemed to grow less important, as statisticians became data scientists and data scientists learned more analysis.

When people ask me what I do, I don’t say that I’m a statistician. I just say I help interpret data, and if I’m pressed, I say that I make a lot of charts.

For Scientific American, RJ Andrews looks back at the visualization work of Florence Nightingale:

Recognizing that few people actually read statistical tables, Nightingale and her team designed graphics to attract attention and engage readers in ways that other media could not. Their diagram designs evolved over two batches of publications, giving them opportunities to react to the efforts of other parties also jockeying for influence. These competitors buried stuffy graphic analysis inside thick books. In contrast, Nightingale packaged her charts in attractive slim folios, integrating diagrams with witty prose. Her charts were accessible and punchy. Instead of building complex arguments that required heavy work from the audience, she focused her narrative lens on specific claims. It was more than data visualization—it was data storytelling.

Be sure to also check out Andrews’ upcoming book on Nightingale, which is one part of a three-part series.

For Washington Post Opinion, a struggling mapmaker makes a plea to stop climate change, because there are no more suitable colors left in the spectrum to show hot:

My point is, unless you are here with some kind of innovative new color that is clearly hotter than red and won’t create these ambiguities, our only alternative is to stop climate change. If you won’t do it for the charismatic megafauna or the less charismatic fauna of normal size, or for your grandchildren, or for yourselves, do it for me, the guy who designed the heat scale for weather maps.I know this is a stupider reason than the reasons that already exist for you to take action, but people often do things for asinine reasons that they would not do for good ones, so maybe if you think about me having to color the map a confusing shade of vermilion or cochineal or, I guess, go back around? I have nothing! you will take pity in a way that you didn’t when human beings were literally dying? I don’t know, man. I’m not sure how many more heat waves like this my map can take. And that is the problem, of course. My map.

This is very important.

Delaunay triangulations have applications in computer graphics, spatial analysis, and visualization. They “maximize the minimum of all the angles of the triangles in the triangulation.” Ian Henry explains them much better with visual demos aplenty:

So those are like… some good reasons to learn about Delaunay triangulations.

But I did not learn about Delaunay triangulations for a good reason.

I learned about Delaunay triangulations for the dumbest reason you can possibly imagine.

That’s how you know it’s gonna be good.

It’s better to err on the side of obvious than leave things ambiguous.

Speaking of the heat wave in Europe, Pierre Breteau for Le Monde charted record high temperatures using a step chart for each weather station in France:

These graphs represent, for a part of the 146 stations for which Météo-France provided us with the data, the level of the most extreme temperatures ever recorded and their date.

The data are fragmentary because it is difficult to go back beyond the 1990’s, or even the August 2003 heat wave, and only those with a historical record of at least 20 years are shown below.

Many European countries are experience record high temperatures, so The Washington Post used melting popsicles to attach something relatable to the numbers and standard heatmap. But:

It turns out that it takes popsicles much longer to melt than we had expected. In this unscientific experiment, the shortest melt time was around 12 minutes, in 90 degrees Fahrenheit, under Madrid’s beating sun. It took as long as 50 minutes earlier in the day and in the shade.

I wish they’d taken it one or two steps further with a more scientific method. Try to use the same color or type of popsicle, put the popsicles out at the same time of day, or get a time-lapse of a control popsicle so that there’s a way to compare something. As it was made, the melting popsicles are just background images.

I’m sure there were time and resource constraints across countries, but it was such a good idea.

There’s a database of feathers called Featherbase, because of course there is:

Featherbase is a working group of German feather scientists and other collectors worldwide who came together with their personal collections and created the biggest and most comprehensive online feather library in the world. Using our website, it is possible to identify feathers from hundreds of different species, compare similarities between them, work out gender or age-specific characteristics and look at the statistics of countless feather measurements.

Researchers are studying the electrical rhythms in plant cells. I’m not sure what that means exactly or what they’re measuring, but it sounds fun.

Emily Badger and Eve Washington for NYT’s The Upshot show how the housing shortage, which was mostly thought of as a west and east coast problem, has grown into a country-wide problem. The tables that compare metro areas between 2012 and 2019, while the most simple, are the most informative in this piece.

Given the current restrictions in the U.S., Kendra Albert, Maggie Delano, and Emma Weil discuss data privacy for those who track their periods:

In their investigation, police try to find evidence that someone intended to miscarry, or was otherwise endangering the viability of their pregnancy. This is because a medical abortion presents the same way as a miscarriage, and prosecutors must prove intent or willful endangerment of an embryo or fetus in order to convict someone (though being arrested at all is traumatizing and can cause severe health consequences). Prosecutors must be able to prove their case beyond a reasonable doubt — data from a period tracker app is not enough on its own to prove this, even if it’s relevant.

I think there’s understandably been nervousness around tracking your period, but it seems that from a legal perspective, there’s little risk? Albert, Delano, and Weil also recommend privacy-centric apps and discuss the more technical aspects in a companion article.

As I’m sure you know, mass shootings, which gain attention because the scale of their severity is so high, make up only a fraction of total gun deaths. Several tens of thousands of people die from gun shots every year in the U.S. The Washington Post describes the full scope, covering purchases, restrictions, race, and geography.

The NRA Children’s Museum from Change the Ref is a mile-long convoy of empty school buses in memory of lives lost to guns:

Since 2020, firearms have overtaken car accidents to become the leading cause of death in children, taking over 4368 lives.

With the advent of this horrific moment, we’ve built a mobile museum made of 52 empty school buses representing 4368 victims. Some of the buses feature an exhibit of artifacts, photos, videos, audio recordings, and personal memories of these children who have lost their lives to guns.

While some parts of visualization are formulaic, outside variables require that you adjust.