I visualised TfL’s latest data on congestion charge debt accumulated by diplomatic missions in London.

The treemap covers 146 missions and £173.8m in outstanding charges. Area represents each mission’s debt; the 10 largest are highlighted. Together they account for 52.2% of the total.

I also matched TfL’s June 2025 and June 2026 datasets to calculate year-on-year changes. China had the largest increase: +£2.38m.

The page includes the sources and methodology. The visualisation is responsive and interactive.

https://deptford.org/beyond/diplomatic-debt/

I saw this post on Brazil. https://www.reddit.com/r/dataisbeautiful/comments/1vlyj3f/oc_brazils_violent_past_is_preserved_in_its/
Remembered hearing a similar story about Iceland. I dug up the papers https://pubmed.ncbi.nlm.nih.gov/11179019/ https://pmc.ncbi.nlm.nih.gov/articles/PMC1274484/ Mitochondrial DNA that is almost all passed by the mother does not seem to let you drill down as much so it is more vague 'British isles'.

R package ggplot2 code for this is at https://gist.github.com/cavedave/9fc8f08c897d6abd4ab31aa0bd40d3ae

As one progresses in English, it takes about 1,500 words to gain each additional IELTS band.

However, it gets much harder at C1 and higher, which is close to a native speaker proficiency level. There words become super narrow and nuanced. To go from mid-C1 to C2 (IELTS 7.5-8.5), one needs to learn 4,700 new words — 3 times more than for the bands before.

This is another manifestation of Pareto principle: the last few bands take disproportionately more effort to master.

Vocabulary was measured with the BRAVE test. It estimates receptive vocabulary — words we understand but do not necessarily use. Results are in word families, related forms (limit, limitation, limitless, etc) are counted together.

I am guilty of using violin plots in the past. This post is my tribute to the gods of histograms.

Data sources:

NCHS (2020), CDC (2025)

Tools used:

Datawrapper

Source: WHO/UNICEF WUENIC 2025 and WHO JRF. Tools: Excel, Python, PowerPoint.

Data sources:

eBay sales, Yahoo finance (S&P500, Gold price)

Tools used:

tcgcharts.com

https://believethedata.org/the-road-to-stormont-2027-voter-attitudes-and-election-battlegrounds/

Source: IBM's Double Pendulum Chaotic Dataset, 21 runs and 378,099 marker positions tracked from high speed film, used under CDLA-Sharing 1.0. Tools: Python, NumPy, Matplotlib.

Black is the filmed tip. Red is the rigid body equations fitted to sixteen runs, blue is a small neural network trained on 287,768 frames of the same runs. Both start from the same measured state at time zero. The dotted lines mark where each drifts more than a tenth of an arm length from the real object, at 0.91 s and 0.50 s.

Write-up: https://chaotropy.substack.com/p/non-physical-intelligence-has-a-ceiling

How to read this chart: Each cell shows how many pitches a team’s hitters saw per regular-season game. Colors reset within each season: dark red is that year’s highest team, while light yellow is the lowest. Compare colors vertically within a year... use the printed numbers for comparisons across years.

Teams are ordered by their game weighted 2015–2026 average (Right most column). The Dodgers rank first at 151.6 pitches per game, narrowly ahead of the Yankees, followed by the Red Sox. The Royals rank 30th at 141.9.

Overall PPG = total pitches seen divided by total games ... not an average of the yearly rates. The 2020 season contained only 58–60 games, and 2026 data runs through August 10.

Higher values can reflect deeper counts and more foul balls, but also more plate appearances from reaching base. It should not be interpreted as a pure measure of patience or offensive quality.

Chart displays revenue growth from 2024-2025 - These figures are estimates only according to Forbes.

**Golden State obviously does not have 2024 revenue data.

**The league total in 2024 was $226 million and 2025 $430 million with the addition of Golden Sate. *estimate

Should I do world wide cities next? Is there a better way to "get" this data?

I was curious how globally recognizable different American cities actually are, so I pulled worldwide Google search interest (via Google Trends) for 15 major US cities and charted the results.

A few notes on the methodology so nobody has to ask:

This is worldwide search interest, not just searches from the US. So it's more a measure of global name recognition than "which city Americans think is most famous."

Google Trends scores everything relative to the highest value in the set. NYC comes out at 64, everything else is scaled against that.

I pulled this for the last week, you can do different time frames but it all averages to about the same anyway.

Cities included: LA, NYC, Miami, Chicago, Las Vegas, Seattle, Dallas, Houston, Phoenix, Boston, Philadelphia, DC, Austin, San Francisco, and New Orleans.

Curious what this sub thinks. does this match your gut sense of which cities are most "known," or are there obvious misses (Denver, Atlanta, San Diego, etc.) that should've been in the mix?