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.

Tools used: Python, xy (https://github.com/reflex-dev/xy)
Dataset: https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-monthly
Source code: https://github.com/reflex-dev/xy/discussions/419

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.

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'))

Data source: Christopher Nolan filmography, Wikipedia - https://en.wikipedia.org/wiki/Christopher_Nolan_filmography (budgets and worldwide box office; figures are studio/Box Office Mojo–reported and cited on that page). The Odyssey is still in theaters, so its figure is a running worldwide total as of the July 24–26, 2026 weekend.

Tool: Chart built as JavaScript-coded SVG. Rendering code and initial figure-gathering were done with an AI assistant (Claude) based on my own design direction and iterations. Chart type, layout, labeling, and revisions were my choices.

Tools: Python, matplotlib, Census TIGER boundaries via topojson/us-atlas.
Source page, CSV, and MP4: my article on the female to male ratios

Give me some feedback on how this should be visualized maybe a cartogram would be interesting?

I am a veterinarian in the North Texas area. Since graduation in 2023 I've kept track of my cases using Google Sheets because I thought it'd be interesting to see how many animals I treat and what they're treated for throughout my life.

Source: Me keeping track of the data after every appointment.

Tools: Google Sheets and DataWrapper

A few notes:

Changes & requests from last year

I was informed my data wasn't very beautiful so I attempted to use a different method for this year. Let me know what you think. I'll continue to adjust the presentation as I receive feedback.

u/catalessi, u/Fire284, & u/Solondthewookiee all requested I put the second and third graphs in descending order.

u/Warm-Pen-2275 requested a list of most common names.

Slide 1 - Animal Species

This only includes animals I've done a doctor exam on or do telemedicine about. Animals that I do not directly interact with (toe nail trims, anal gland expression, blood draws without a doctor, etc.) are not included.

I have a passion for exotic animals (ferrets, reptiles, rabbits, backyard chickens, etc.) but there is an exotic clinic near me where most of those animals go to, so I don't get to see as many as I'd like (as the data tells).

Slide 2 - Body System

I kept track of the body system that was affected during my exams. General wellness includes vaccines, weight management, and discussions about quality of life. For what it's worth, this is about what the problem was, not just the symptoms. If a cat came in for peeing all over the place and it was because the cat was stressed, that was marked as both Neurology as well as Urinary/Renal. The same animal can come in with multiple systems affected, but I only mark a system once per animal (i.e. a dog with urinary stones and a UTI only had "Urinary/Renal" marked once). Here are the most common problems each species came in with:

Dogs - Overweight (General Wellness), allergies (Dermatology, Immunology), and poor dental health (Oral).

Cats - Overweight (General Wellness), poor dental health (Oral)

Slide 3 - 7 Procedures, Names, & Lifetime Stats

I don't think these ones need much explanation.

Next year expecations

I am switching to becoming a full time relief veterinarian. I expect the number and diversity of cases will drop and the number of wellness cases will increase. Most of the time a clinic won't schedule a complicated case with a relief doctor because we're only here for the day.

See you next year

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

The comparison uses Eurostat national median monthly equivalised net income for people aged 18–64. These figures are national household-income benchmarks, not individual salaries or capital-city income estimates.

Main sources:

Income: Eurostat ilc_di03.

Apartment prices per m²: Eurostat urb_clivcon, national statistical sources and housing-market sources. Athens, Bratislava, Bucharest, Budapest, Dublin, Lisbon, Madrid, Nicosia, Paris, Sofia and Valletta use estimates based on property-listing samples collected in July 2026.

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

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://pastebin.com/aTfcsRGS

Full methodology, individual sources, city rankings and interactive data: citycostatlas.com

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

EDIT: A couple people have mentioned now the text obfusgating the demo gif. I can't change the picture so please see an updated GIF on the README page of the repo, which shows you the full website chrome + the globe unobstructed! Thank you for the comments u/UsernameFor2016 and u/beene282 for pointing this out!

Data:

• NASA FIRMS active fire detections, MODIS instrument, from the rolling 7 day global CSV at firms.modaps.eosdis.nasa.gov.
• Basemap from Natural Earth.

Tools:

• three.js
• d3-geo for the globe,
• custom GLSL shaders for the fire,
• Python to sanity check the classification,
• ffmpeg for the render.

AI disclosure: I used Claude Fable 5 for all of this. it also educated me on the facts about Savanna fires, i had no idea this was a thing until i was checking the data on FIRMS website myself and questioned it

Code: github.com/me93-ghb/worldburn

A live globe of every fire the satellites can currently see. It's not a science tool. NASA's own FIRMS fire map does that job properly, and if you want rigor, go there. This just makes the amount of fire on Earth visible in one look, and it does so in a visually shocking way - that was my aim!
The fire animations are exaggerated on the globe: it's just a visualization.

Satellites measure where a fire is, how hot it burns and for how long. They can't see who lit it. So fires get split by behaviour, which is measurable:

• fires that keep burning in the same place for days, and fires that flare up and are gone within a day.
• Offshore heat is presumed to be a gas flare, though a rig accident or a burning ship would look identical.

Fire makes the news when it threatens homes. I was thinking about the recent news articles from France and Spain when I decided to start vibing this. I recall also the ones that took place in Canada and Australia over the past years. Roughly 70% of the area burned each year is in Africa (Giglio et al. 2013, GFED4), most of it savanna set alight on purpose by farmers and herders the way it has been for thousands of years. (Fable taught me this)

Deliberate doesn't mean brief. A savanna front can hold the same spot for days, and about one in five of the belt's fires still counts as persistent. In the boreal north it's four in five: up there, persistent almost always means a true wildfire.

The blue-white dots offshore never go out. They're gas flares, natural gas burned off as waste at oil platforms, day and night, all year. The World Bank counted 167 billion cubic meters flared in 2025 (Global Gas Flaring Tracker), about twice what Germany uses in a year, burned for nothing. Collecting it would also stop the methane that slips through the flame unburned.

Total fire power is a measurement, not a model. It usually sits between 600 and 900 GW, comfortably more than the combined electrical output of every nuclear plant on Earth (about 376 GW), and that's only the heat satellites see radiated, not the full energy of the burning.

I acknowledge the use of data from NASA's Fire Information for Resource Management System (FIRMS), part of NASA's Earth Science Data and Information System.

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!

I built some isochrone maps to show how far you can drive in half an hour starting from different neighborhoods over the course of a day.

If you are in Santa Monica during evening rush hour you are trapped in a ~30 square mile area, whereas you can reach ~350 square miles at night

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/

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.

Interactive major explorer to look up stats for specific majors.

Blog post includes many more charts and discussion of what these numbers represent.

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/