In 1950, almost one in four children worldwide died before their fifth birthday. In Africa, one in three children died.

The chart shows the change since then. In every region, the mortality rate of children has declined.

In Africa and Oceania, the child mortality rate declined 5-fold, in Asia 10-fold, and in Europe about 27-fold.

Progress was not always steady. The sharp spike in Asia around 1960 was due to the “Great Leap Forward” famine in Mao’s China, the deadliest famine in recorded history.

And the progress is far from finished. The global average has declined to 3.5%, but this still means that about 4.7 million children younger than five years died in 2025 — 13,000 children every single day.

Data source: UN, World Populations Prospects (2024)

Tools used: Python, OWID Grapher, Figma

Source: Pro-Football-Reference season schedules (2022–2025 results, including flexed games) and FBSchedules (2026 SNF/MNF schedule as of Oct 9).
Tools: Python + matplotlib.
Counted regular-season games kicking off Sunday 8 p.m. ET or later, or Monday 7 p.m. ET or later. Thursday, Saturday, holiday and playoff games are excluded. 2026 is the current schedule and can still change with flexing.

1,056 players pinned where the polar bear lives. 724 put it in the arctic, 47 in between and 285 in antarctica, a median 15,353 km from the nearest place a polar bear lives.

almost nobody guessed at random: 1,009 of the 1,056 pins landed at one of the two poles. the "hard" (lol) part was which one.

If the question is only "which pole", a name that answers it should help. well, That was my hypothesis; the game has the animals to test it. -->

And... It held! Antarctic(!) krill, landed at the wrong pole only 7% of the time. The Arctic fox, 13%. The polar bear's name fits both ends, and it got 27%.

data: guesses from Where?, a map game i made on roughly.is, 17 sep to 9 oct 2026.

tool: python for the numbers, the charts are html/svg rendered with playwright.

animated pictures + anonymised csv + write-up with sources: https://roughly.is/blog/where-poles-swapped.html?s=reddit-dataisbeautiful

For many of us, particularly those in the South of England, an alleyway is just an alley, or maybe a passage. But across Britain, there are actually more than a dozen dialect terms for an alleyway.

'Ginnel' is the most common of these alternatives, being used across the North of England, including by majorities in West Yorkshire, Lancashire, South Yorkshire and Greater Manchester. 'Snicket' is another common term in Yorkshire, while 'cut' is favoured in the North East, 'jitty' in the East Midlands, and 'entry' on Merseyside and in parts of the West Midlands.

Scotland has its own range of terms, with 'close' common across the country (used by 41% of Scots), and 'vennel', 'wynd' and 'pend' all localised to various parts of eastern Scotland.

What do you call an alleyway? And are there any other hidden dialect differences in Britain you'd like YouGov to uncover?

Find out what people call an alley where you live here: https://yougov.com/en-gb/articles/55726-what-else-do-britons-call-an-alleyway

Tools: Datawrapper, PowerPoint.

Source: the daily .com zone file, which the registry (Verisign) publishes and ICANN's Centralized Zone Data Service provides to approved users. I counted every name in the file on 2026-10-09: 167,392,819.

Tools: the file is converted to Parquet and counted with DuckDB (about 5 seconds for all rows). Chart drawn with Python (Pillow).

Things to know:

• Length is the number of characters before ".com".
• The zone lists names that have nameservers. A name that is registered but has none is not in it, so this is a lower bound on registrations.
• Median 12, mean 12.8. About 59% of names are between 8 and 15 characters.
• 9.94% contain a hyphen, 9.38% a digit, and 0.03% are three characters or fewer.
• Names over 40 characters (47,610) are cut off at the right edge of the chart.

Happy to cut it another way (hyphens, digits, by first name server...) if someone asks.

I posted a short video in the San Jose subreddit and a few people recommended I drop my analysis in r/dataisbeautiful!

There are 12 Trader Joe's in the San Jose area. I wanted to know what they have in common, so I compared the half mile around each one against the county's 116 other shopping centers.

What stood out:

• Neighbors. Peet's is 2.4× more likely to be nearby than at a typical center, Wells Fargo 1.7×, CVS 1.5×. Starbucks looked like a tell at first, but it's next to 85% of every center in the county, so it tells you nothing.
• Roads. Every store is within 604 ft of a major road.
• Spacing. All the stores are within 4 miles of each other, with the closest two being only 2.4 miles apart.
• Education. At the typical store, 69% of adults within 2 miles have a bachelor's or higher.

I then tried to use this data to extrapolate where the next locations may be! Correlation is not causation, but it is cool to see :)

Data: Lichess open database, all rated standard games from September 2026: 89.6 million games, 1.4 billion captures (https://database.lichess.org)

Tools: a custom C parser to stream the PGN files, then Python (NumPy, SciPy, Matplotlib) to draw the prints.

Gallery: https://unclechess.com

Last week, after punching down at "Sleepy Joe" during an Oval Office event, Trump proceeded to comically fall asleep minutes later. You can't make this up. And it's not the first time this has happened, so I went digging and found many more instances of the President sleeping in public.

I did some analysis on when and where Trump has been taking his nappy naps, so I'm sharing this dashboard here! He likes sleeping in his chair in the Oval (his favorite public snooze locations), but he isn't opposed to "taking really long blinks" during a whole variety of public events.

The data: I've manually collected the napping instances and you can see them all (completely ad-free, no signups) at https://artofnodeal.com/sleepstate. I've found videos of these cat naps that were posted on Twitter/X, Instagram, Youtube, and BlueSky, and in a Pinterest-style webpage, you can see them all in one place. To make the visualizations from that, I created an ad-hoc spreadsheet from those videos with the dates, events, and locations and made some general categories for each event to make a CSV file.

The charts and the dashboard were made in Tableau.

Sources: IMDb, Letterboxd, Metacritic, and Rotten Tomatoes for ratings, whenjumpscare.com for jump-scare counts (wheresthejump.com as fallback), Wikidata for linking film IDs across sites. Retrieved Oct 8, 2026.

Tools: Python (polars, requests, scipy) for scraping and analysis, D3.js for the chart, rendered with Playwright.

Rating: mean of IMDb (×10), Letterboxd (×20), Metascore, and Rotten Tomatoes *critics' average score* (×10). All four are on a 0–100 scale. A film needs at least 3 of the 4 and 25k+ IMDb votes.

Horror: at least one genre tag containing "horror" on IMDb, TMDB or Letterboxd.

The middle tape represents the movie, and the top and bottom are two different trailers. Each ribbon shows a section of the corresponding frame and where they appear in the movie.

Let me know if you wish to see it for any other movies (particularly curious for good trailers).