Not to dwell on Musk, but I was reminded of this dot plot by the Economist that shows when he posted on Twitter/X and how much. The volume picked up a bit post-acquisition.

As you can imagine, Elon Musk, the owner of X, has a prominent influence on the social media platform. For the Washington Post, Jeremy B. Merrill, Trisha Thadani, and Kevin Schaul show the scale through view counts (on the x-axis) and time since posting (on the y-axis).

I’m not sure how much I trust view count accuracy on X, but you can at least see the relative weights of what the company wants you to see.

Mostly, I put this here for the flaming data point at the beginning of the piece and to highlight how it reminds me of the interactive by Periscopic a few years back. It starts with a single point for the anecdote and then multiplies for the patterns.

There was a brouhaha a couple months ago over research that suggested black plastic spatulas spew poison into our food. The problem is that there was a basic arithmetic mistake that made the results seem a lot worse than they actually were. For National Post, Joseph Brean reports:

The paper correctly gives the reference dose for BDE-209 as 7,000 nanograms per kilogram of body weight per day, but calculates this into a limit for a 60-kilogram adult of 42,000 nanograms per day. So, as the paper claims, the estimated actual exposure from kitchen utensils of 34,700 nanograms per day is more than 80 per cent of the EPA limit of 42,000.

That sounds bad. But 60 times 7,000 is not 42,000. It is 420,000. This is what Joe Schwarcz noticed. The estimated exposure is not even a tenth of the reference dose. That does not sound as bad.

They had a per-unit baseline they could compare against to gauge if their estimates were good, bad, or dangerous. To scale up to typical human body weight, they had to multiply, but they missed a zero somewhere.

Red flags should’ve flown immediately when they saw 80% of the safe limit. Just a quick double check to see if they made a mistake. The researchers said they made a typo, but I wonder where and when they made that typo. While writing the paper? While calculating? While recording the data?

The irony is that a bunch of people probably tossed their black kitchenware and will now go buy more plastic after finding out it wasn’t so bad after all. The researchers were trying to communicate the opposite.

For Wired, Dhruv Mehrotra and Dell Cameron, in collaboration with Bayerischer Rundfunk and Netzpolitik.org, check out location data for overseas military personnel that is probably too easy to buy. The data from a broker is primarily meant for marketing purposes so that companies can better target ads, but there isn’t exactly a strict vetting process for where the money comes from.

Sooo, this seems not very good.

For Works in Progress, Jared Hutchins explains how milk production increased per cow, a combination of artificial insemination, cryogenic preservation, and more data.

Note the dual axes in the chart above. The left y-axis shows total dairy cows, and the right shows average yield per cow. The natural next question: Did total milk production also increase with fewer cows? Yes.

In 1945, there were over 25 million dairy cattle in the United States; by 1980, the number had dropped by more than half to just over ten million. And the dairy industry as a whole became more productive. Between 1940 and 1982, the total supply of milk increased by a third, even as the number of dairy cows in the US halved.

I’m going to assume that we’d see similar patterns with other livestock and crops. Which has been moneyballed the hardest?

Usually we focus on actual data points for insights, but we can also highlight the spaces between for a flipped point of view.

For Reuters, Arathy J Aluckal, Jitesh Chowdhury, and Vijdan Mohammad Kawoosa illustrate how the mpox virus spreads, its variants, and who it affects.

The last chart in the article, shown above, struck me as familiar but took me a second. It’s a pyramid chart that puts women on the left and men on the right. The y-axis represents age groups. You can see the contrast between those infected in the African region (men and women somewhat evenly) and the rest of the world (almost all men). The variant in Africa can spread through close contact.

For NBC News, Joe Murphy shows the play rate on Last.fm for Mariah Carey’s “All I Want for Christmas Is You” as a set of calendar heatmaps, since 2006.

The song was released in October 1994. The data doesn’t go back that far, but I’d wager it played mostly in December, and then plays kept shifting earlier until leaking in to August. I’m looking forward to a couple decades from now when I can remind the young folks that this wasn’t always an Easter song.

Word clouds show key terms or phrases that appear in a body of text. Everyone loves them. Martin Wattenberg turned it around to show the words that do not appear, with words sized by how often they appear in other text. This is the anti-tag cloud for Winnie the Pooh:

Fun. I wonder if the same logic can apply to other types of missing data.

Some jobs require a lot of standing, crouching, and climbing, whereas other jobs require little movement and you sit all day, turning into a sloth-like creature that gets up sometimes to eat and go to the toilet. I’m not projecting, you are.

For the Pudding, Alvin Chang visualized the spectrum of sitting and standing jobs. The interactive version lets you zoom in to pixel art characters working their jobs within force-directed capsules. While the interactive is fun and good for exploration, the video version provides a more narrative tone with Chang narrating:

[arve url=”https://www.youtube.com/watch?v=sE_Ew0Be4qE” /]

I think I like the video version better for the clear direction. I’m a nerd and get distracted by the interaction too easily.

Check out the data yourself. It’s from the Occupational Requirements Survey by the Bureau of Labor Statistics.

Liuhuaying Yang aims to clarify the names and sounds that get lost when translating language that relies on tones to spellings that do not capture the differences:

This project explores the complexity of Chinese names and the challenges of using Pinyin romanization, focusing on how it impacts the distinction between surnames and given names. While Pinyin simplifies Chinese characters for global audiences, it can create ambiguity when different characters share the same pronunciation. Our aim is to clarify name identification, especially in official contexts, and deepen understanding of Chinese culture, language, and naming conventions.

Yang uses trees for each pinyin name at the trunk and the actual names that compressed version might represent. Click on the leaves to hear the differences.

The folks up here received one of those loud emergency alerts a few minutes ago — for a “TSUNAMI WARNING.” I think that’s a first for me.

There was a 7.0 magnitude earthquake off the coast. It still amazes me that we can go to the USGS site and see that earthquake activity in real-time.

I check out Claude, a generative AI assistant to see if, how, and when such a tool could fit into an analysis and visualization workflow.

The network that connects the world still relies on surprisingly thin wires that run miles down on the ocean floor. Sometimes those wires break. For The New York Times, James Glanz, Elian Peltier, and Pablo Robles show the repair process and what happens when the internet doesn’t work.

Add another graphic to the baby name genre of visualization. Karim Douïeb put a spin on name trends by shaping the actual name to the line. Enter your name and see how many babies were given the name over time. Sometimes parents name their kid Santa.

Only the top of the vertically-sized letters represent the trend, with the bases below the zero-axis. I’m guessing that’s to keep names readable. Fun either way.

For Bloomberg, Jeannette Neumann describes the accounting error:

For years, Macy’s Inc. touted its ability to boost profits by cutting delivery costs and trimming other expenses on calls with Wall Street analysts. Then on Monday, the department store chain surprised investors by revealing that those very costs had become the source of an internal investigation into what the company has described as a multimillion-dollar employee plot to manipulate the metrics.

The retailer said the incident involved only one former employee, who had hidden as much as $154 million of delivery expenses since 2021. Cash was not taken from the company and the amount of hidden expenses is a small portion of the $4.36 billion of overall delivery costs incurred during that time.

The purpose behind the mistake is still under investigation, but I often wonder how many rounding errors in big companies were inspired by the movie Office Space.

Many hospitals in the United States are for-profit, which itself is not a bad thing, but problems arise when patient care suffers because of profit optimizations. For Bloomberg Businessweek, Caleb Melby and Noah Buhayar turn their attention to HCA Healthcare, the country’s largest hospital chain, and how staffing choices appear to appear to over-prioritize margins.

China exported next to zero vehicles in 2010, but from 2020 to 2024, China leapfrogged all other other countries to become the leading exporter, by a lot. For The New York Times, Agnes Chang and Keith Bradsher show the rise over time and the breakdown with unit charts.

Each car represents 10,000 exported cars in the above graphic. I like the 2022 Russia comparison for scale.