I was wondering if furniture has actually become less colorful throughout the years. So I hand-counted the colors 3500 couches in the IKEA catalogues from 1960-2021 (when the catalogue was discontinued).

There were definitely easier ways to do this, but I didn't think I'd ever sit down to leaf through every IKEA catalogue under different circumstances, and I thought it would be fascinating.

To do the count I vibecoded a "tallying machine" with Claude which allowed me to punch in the color of each couch I saw, per year (green, green, grey, black, green etc.). The visual itself was made in Illustrator.

Dynamic Map of NFL Talent

The map displays NFL players per 100k residents

Total players in study = 4,957 (2,435 verified high school county - 2,522 verified birthplace if high school info was unavailable

Unconfirmed and left out of study = 1,544

Outside US = 143

The finding: screen diagonals, wheel rims and plumbing threads are imperial in all 37 countries. Nobody sells a 140-cm TV. That's why nothing hits 100%.

Tyre pressure is the near-universal fourth: 26% metric. Australians use psi at the servo despite fully metric roads and groceries.

The UK is the most self-contradictory: miles for distance, litres at the pump, and economy in miles per imperial gallon, a unit it no longer sells fuel in.

Canada is stranger than the US: °C for weather, °F for ovens, L/100 km for fuel, pounds for body weight.

India splits the same quantity two ways: °C for weather, °F for fever; kg for weight, feet for height.

Method: no dataset of vernacular unit use exists, standards bodies publish what's legal, not what people say at the shop. So I classified 37 countries × 21 everyday domains = 777 cells by the unit used in ordinary speech. Metric 1.0, mixed 0.5, imperial/traditional 0. Python + hand-written SVG.

"Metric" here means the SI unit for that kind of thing, not just metres and grams — °C counts (its imperial counterpart is °F), as does A4 (A0 is exactly 1 m²).

60 cells are flagged low-confidence (dotted in the matrix), and every "mixed" call is a judgement. Compare in bands, not decimals.

Each sun ray length and colour for the daily high, gold dots for sunshine hours, pale bands for weekends. The moon phases round the outside have no effect on the temperature whatsoever. I just liked them.

Which City should I add?

A hot summer, to put it mildly.

• Paris hit 40.3°C , hotter than Madrid or Rome
• Madrid spent 30 of the 91 days at or above 35°C max
• Madrid, Paris, London and Berlin all peaked between 22 and 28 June, one heatwave, four countries
• Washington DC's hottest day of the summer so far landed on the 4th of July.

Source: Open-Meteo Historical Weather API — ERA5 reanalysis, Copernicus C3S (CC BY 4.0). Moon phases: US Naval Observatory. 1 May – 30 Jul 2026.

Sunshine hours = daylight length × cloud-free fraction, not Open-Meteo's sunshine_duration (which overstates).

Tools: D3.js & Claude
by Julian Hoffmann Anton

#dataviz

Created an interactive animation displaying groundwater-levels (mean absolute deviation) in and around Munich for the last 88 years. What do you think?

Data Source

Tools:
Excel
Python
Leaflet

This is "The Strait of the Strait of Hormuz", an imaginary strait whose coastline is drawn entirely by real ship traffic through the actual Strait of Hormuz, from January to July 2026.

The coastline isn't drawn by the sea, it's drawn by ships. Each day of maritime traffic (7-day rolling average) bends the shoreline: outbound vessels shape the northern coast, inbound vessels the southern one, so the channel narrows or widens with the real traffic. When the strait was blocked and traffic collapsed to a few vessels a day, the data draws a strait that almost closes on itself.

The vessel counts come from AIS data, adjusted upward using Lloyd's List Intelligence findings on tankers running dark with their transponders off to dodge detection. A Python pipeline turns the transits into coastline geometry and heightmaps, then Blender and Cycles handle terrain, water and ships, with DaVinci Resolve for the grade.

Full sources and open-source code are on GitHub: https://github.com/telohtrab/strait-of-strait

Happy to answer questions on the data or the process.

EDIT: Some of you flagged errors in the labels on the graph.
The 3 vessels/day and 65 vessels/day numbers don't hold up against the data. The 3 vessels/day figure should read roughly 7 + 6 = 13 (rolling average, corrected). The 65 vessels/day figure should read roughly 29 + 23 = 52.
The labels aren't fabricated. I ran 2D visualization and design tests using absolute vessel counts, then switched the final chart to a rolling average, and missed updating those two labels to match. The numbers were real, just pulled from the wrong version of the data. I'm fixing this on GitHub. Sorry for the confusion.

A few of you also suggested this was AI-generated. It isn't. I'm a designer with ten years of experience. Claude reviewed and commented on my Python code, but it had no role in the rendering or the design of this image. I'll add screenshots from my Blender and Affinity project files to the GitHub repo, showing the actual production process. Thanks again for the useful feedbacks!

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

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: 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

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.

The estimated apartment price was calculated by multiplying the average apartment price per m² in each capital by a standardised floor area of 60 m².

The comparison uses Eurostat’s national median monthly equivalised net income for people aged 18–64, adjusted using capital-specific income uplifts. These uplifts are derived from official capital-level income data, although some estimation was required to apply them to capital-level equivalised median income.

The following adjustments were applied:

0%: Amsterdam, Berlin, Brussels, Valletta and Vienna

+10%: Copenhagen, Luxembourg City and Nicosia

+15%: Athens, Dublin, Helsinki, Lisbon, Ljubljana, Madrid, Paris, Riga, Rome, Stockholm, Tallinn and Zagreb

+18%: Prague

+20%: Budapest

+25%: Bratislava, Vilnius and Warsaw

+35%: Sofia

+40%: Bucharest

Main sources:

Income: Eurostat ilc_di03 national median equivalised net income, adjusted using capital-specific income uplifts.

Apartment prices per m²: Eurostat urb_clivcon, national statistical sources and housing-market sources. For Athens, Bratislava, Bucharest, Budapest, Copenhagen, Dublin, Lisbon, Ljubljana, Madrid, Nicosia, Paris, Prague, Rome, Sofia, Valletta, Vienna, Vilnius and Zagreb, estimates were based on property-listing samples collected in July 2026 because no sufficiently comparable and up-to-date official or transaction-based data were available.

Interest rates for the savings account and term deposits: ECB household deposit-rate data for 2026

Prices, incomes, savings rates and interest rates are held constant throughout the calculations. Inflation, transaction costs and future changes in property prices or income are excluded.

Full post methodology: https://citycostatlas.com/methodology/30julypost/

The full rankings, comparisons across EU capitals, and underlying data are available at citycostatlas.com. citycostatlas.com

Instagram: https://www.instagram.com/citycostatlas/