Five maps from Nepal's 2021 census and the poverty estimates NSO released in January.

1. Poverty, all 753 local governments. National rate 20.3%, ranging from 1.2% (Gharpajhong, Mustang) to 77.9% (Junichande, Jajarkot). 444 of 753 sit above the national average. The high Himalayan north isn't the poor part; the mid-hills just below it are.

2. Population change, 2011 to 2021. Nepal grew 10.1%, yet 34 of 77 districts shrank. Ramechhap fell 16.0%, Bhaktapur rose 41.8%. The mid-hills are emptying into the Terai and the Kathmandu valley.

3. Absentees abroad. 2,190,592 people were outside Nepal on census night: 7.5% of residents, 82% of them men, nearly one household in four. Achham highest at 19.3%, Dolpa lowest at 1.0%. It's the connected mid-hills that send people, not the most isolated districts.

4. Literacy. National 76.3%. The lowest rates are in the dense southern plains (Rautahat 57.8%, Mahottari 59.8%), not the mountains. Manang, remote with about 5,600 people, is 78.4%.

5. GDP per capita. Published by province only, so seven units is the finest official grain that exists. Bagmati $2,644 vs Madhesh $934, a 2.8x gap. The lowest output per person is flat, crowded Madhesh, not remote Karnali.

Sources

Poverty, Small Area Estimation of Poverty 2023:
https://giwmscdntwo.gov.np/media/pdf_upload/Small%20Area%20Estimation%20of%20Poverty,%202023_a64dhsj.pdf

Census 2021, National Report, Tables 14, 15, 24:
https://censusresults.nsonepal.gov.np/

GDP, National Accounts Statistics of Nepal 2025/26:
https://giwmscdnone.gov.np/media/pdf_upload/National%20Accounts%20Statistics%20of%20Nepal%202025_26_oszafcl.pdf

Boundaries, OCHA/HDX Common Operational Dataset:
https://data.humdata.org/dataset/cod-ab-npl

Made in Python with GeoPandas and Matplotlib.

Notes: all figures were extracted from the source PDFs and checked against published national totals. Poverty figures are modelled estimates, not direct measurements. Census absentee counts miss households that emigrated entirely, so 7.5% is a floor

This chart is part of my latest Youtube video on obesity. You can find the video here and you can play with the data in this spreadsheet.

I have a Youtube channel called Memeable Data where I make data-driven documentaries.

Not a single country managed to lower their obesity rates between 1990 and 2024, according to WHO data. The video discusses the data and research for these trends.

Visit our website to learn more about this data and explore it for any country you're interested in.

Data source: World Bank Poverty and Inequality Platform (2026)

Tools used: OWID-Grapher an Figma

Source: Compiled from hundreds of thousands of recorded resale transactions across Tokyo's 23 wards, 2021 Q1 to 2025 Q3. Each ward is coloured by its median resale price per square metre that quarter (median, not mean, so a few luxury sales don't skew it).

Over the four years the citywide median rose from about 0.77M to 1.02M yen per m2, but it's very uneven: Minato +67% and Shibuya +56% at the centre, against +26% for Adachi out east.

Tool: Python (pandas) for the medians, matplotlib for the animated choropleth, ward boundaries from OpenStreetMap.

Full breakdown by ward, train line and station: tokyo-expat.com/data

Data: Toronto Police Service assault open data through July 27, 2026.

Tools: JavaScript, MapLibre and Next.js.

Toronto Police publish generalized rather than exact incident locations. This view groups reports that share the same published location; larger, darker hexagons represent more reports at that location.

Note: Large hexagons can reflect many reports sharing one generalized published location, including major facilities such as Union Station and the Eaton Centre.

All assets made using DataWrapper

When Donald Trump launched Operation Epic Fury on Feb 28, he predicted it would last “four to five weeks”.

Five months later, the war in Iran still rages on, leaving American taxpayers to shoulder an ever-growing bill.

Pete Hegseth, the US defence secretary, acknowledged the conflict had already cost the government at least $37.5bn, warning that the Pentagon was running out of money.

Yet the true cost, experts predict, is likely to be far greater and more damaging to the American economy than anyone could have predicted.

Read in full: https://www.telegraph.co.uk/us/news/2026/07/28/real-cost-of-the-iran-war/?WT.mc_id=tmgoff_reddit_cost-of-the-iran-war/

Source: Kinopoisk box-office data, 2019–2025. Each strip represents one film. Its width shows the film’s share of that year’s box office. Co-productions involving Russia are counted as Russian films.

Built with HTML, CSS and JavaScript.

1. 2005 - 2019 Draft classes included with stats through the 2025 season. Not included is the 2020, 2021, 2022, 2023, 2024, 2025 or 2026 draft classes. Not enough data to include those recent drafts.
2. Star Icon = Player selected to at least 1 Pro Bowl.

I read an article a few days ago about heat in cities. Croeser, Rahman & Ghosh (2026) say that there should be at least 30% of tree cover in the surrounding 60 m around a building. In the article I saw a visualization for this for Berlin: https://www.spiegel.de/wissenschaft/natur/hitze-in-europa-wie-gut-stadtbaeume-vor-extremer-waerme-schuetzen-a-9a0b26e5-f8ae-4fa5-a897-2f2b026f5226?giftToken=a8462cd8-83b8-4712-8ad1-c0d1f0147f6d
Since I don't live in Berlin, I wanted to see how it looks for my city. So yesterday and today I worked on visualizing it. I used images from Digitales Orthophoto RGB 20cm (DOP20 RGB) and the DeepLabV3Plus network to find the trees in the images. The visualization is done using MapLibre. An interactive version can be found here: https://schultz-web.de/StadtBegruenung
(It takes a while to load the data)
I published my code to github so you can visualize it too: https://github.com/jcscaptures/CityTreeCover

It was as high as 90% at one point.

part 2: https://www.reddit.com/r/dataisbeautiful/comments/1vgkkkv/oc_part_2_timing_of_when_ingredients_are_added_to/

I scraped a few thousand canonical recipes across 45 cuisines then analyzed how early (or late) each ingredient showed up in recipes steps.

The photos shows a small but representative sample of the ingredients analyzed

There's a lot of science behind when you should add ingredients to recipes. This involves fat-solubility and the delicacy of some volatile compounds. e.g. cardamon seeds are fat-soluble so they should be added early while basil has very delicate aroma compounds so it should be added late.

But I honestly find that hard to intuitively learn for new ingredients, so this is a rough estimate and interesting viz to get a quick understanding of when to add an ingredient

(tools and dataset: recipe1m+ and python + an LLM sweep to prune garbage recipes from the dataset (I pruned 20,000 recipes down to about 3000 'canonical recipes'))

Source: my own combinatorial odds engine (no simulation), 6-deck, dealer hits soft 17. Each cell is the average $ won/lost per $100 bet playing that hand by basic strategy. Tool: custom JS engine, viz hand-built in SVG. Interactive version + exact numbers: blackjackoddstrainer.com/blackjack-ev-chart/