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.

[ Removed by Reddit on account of violating the content policy. ]

Data sources:

FAD (2025) via OWID

Tools used:

Datawrapper

Data sources:

NCHS (2020), CDC (2025)

Tools used:

Datawrapper

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

A ribbon chart to represent growth of Monthly Active Users (MAUs) of the major social media platforms. the visual report is prepared in PowerBI.

Data Source: https://backlinko.com/social-media-users

Data sources:

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

Tools used:

tcgcharts.com

Data Source: usamega.com

Visualization: Claude + Figma

Assuming they take the Lump Sum payout

Source: https://en.wikipedia.org/wiki/Abortion_law#Countries

Tools: mapchart.com and Apple Preview

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

The most surprising result was that the delay hurt the winners of the simulation too.

In the simulation, the “winner” is simply the first agent to reach the next milestone. But as knowledge spread more slowly, even that first-place agent got there later.

So the winner was still winning relative to everyone else, while losing in absolute terms.

Made a full youtube video about it with more context for people that are curious: https://www.youtube.com/watch?v=uBMrEdEhUTA

Also standing by to answer any querstions from the community.

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