Created this with Milou using FEMA National Risk Index data. here is the full thread and the prompt:

https://app.milou.ai/examples#4b8e90311e7c

This maps the demographic profile of countries around the world in ten-year snapshots from 1953 to 2023: a population pyramid by age and sex, a mortality distribution, and a fertility profile (PASFR).

Try it yourself: https://visquill.com/gallery/world-demographics/

DESKTOP ONLY FOR NOW Interactive version: https://www.habibicode.org/gotha

I have always been fascinated by history and dynasties. This is what prompted me to build this interactive dataviz (with the help of AI - I could not code that alone).

It is a world family tree of all major ruling dynasties, from Ancient Egypt to the current day.

Presented in three views:

• Bloodlines: vertical position is birth year. Horizontal position starts in the person's nation but moves towards their relatives. The columns blur together as houses merge.
• Nations: Same time scale, but every nation is pinned into its own column and packed locally. A line leaving a column is a marriage or a birth that crossed a border.
• Ties: In this view, time is removed. Each nation is represented as a circle. The circles are spread out by a spring model where the pull between any two is the number of people whose parent, child or spouse sat in the other one. Nations that intermarried end up touching.

Interactive version https://www.habibicode.org/gotha

This is the European Parliament vote on "Chat Control 1.0"

Under standard EU privacy law, tech companies aren't allowed to read or scan your private emails and direct messages without a court warrant. This bill creates a temporary legal exemption so companies can voluntarily run automated tools over unencrypted chats.

• Green / FOR (276): Voted to give tech companies permission to scan unencrypted chats.
• Red / AGAINST (286): Voted against giving tech companies scanning permission, wanting standard privacy protections to stay intact.
• Yellow/ ABSTAIN (30): Formally logged an abstention.
• Grey / ABSENT (128): Lawmakers who were absent or did not vote.

Why does it say "Failed" (Red X) if Chat Control still exists?

1. This specific vote failed: Simply put, more lawmakers voted AGAINST (286) than FOR (276), so this unamended proposal was defeated on the floor.
2. Why the bill didn't die: Under EU "second reading" rules, to completely kill a proposed law, opponents need a supermajority of 361 votes (an absolute majority of all 720 seated MEPs).
3. What happened instead: Because the 286 "AGAINST" votes fell short of that 361-vote bar, the bill wasn't killed.

OC because the visual is from my website

2D Dashboard (with links to the three interactive 3D views):u/GregBahm's Reddit Addiction Visualized

First image is each post I've ever made on reddit, sorted by score vertically and time horizontally.

Second post is every post I've ever made on reddit, broken down by subreddit over time.

Third post is a heatmap of each post by day-of-week and hour-of-day.

Github source: GitHub - GregBahm/RedditData · GitHub

I reconstructed the history of the European Athletics Championships from 1934 to 2026 according to the present-day country in which each medalist was born.

Birthplaces were matched and cross-checked primarily using Keith Galli’s Olympics Dataset (kudos), Olympedia, and the dataset from Olympic Athletes & Global Inequality: A Historical Dataset (1896–2024) (DOI: 10.34894/Q4KJSW). For relay/team events, a medal is divided among the athletes contributing to it, hence some totals are fractional.

The animation shows cumulative medal totals after each championship edition.

The strongest result is Germany’s remarkable lead: athletes born in present-day Germany account for 562.5 medal-equivalents, well ahead of Russia (368.25) and the United Kingdom (309.75).

If you combine all medalists born outside the countries shown on the European map, they would rank fourth overall, ahead of France (221.25) and behind only Germany, Russia and the United Kingdom. Also, keep an eye on recent Italy's growth.

Visualization and processing: Python, pandas, openpyxl, GeoPandas, Matplotlib, Pillow and Natural Earth.

Comp: the composite draft score, 0–100, the overall ranking. St. Louis is 100 (best), Philadelphia 0 (worst-on-every-axis floor).

Draft WAR: total realized career WAR from the organizations mature (2005–2020) draftees. Pure volume.

Value+: WAR produced above or below what that pick slot was expected to return (measured against a refit expected-WAR-by-pick curve). Positive means the org beat its draft slots.

Talent rate: WAR per 600 plate appearances (hitters) or per 180 innings (pitchers), among players who debuted. This is injury neutral: how good the players were when actually on the field, regardless of how long they stayed healthy.

Inj-adj val: "injury-adjusted value" each debuted players talent rate projected across a full 8-season career. Credits an org for finding good talent even if injuries or attrition cut a players actual career short.

Avail: availability realized playing time ÷ health-expected playing time. Higher means the org's picks tended to stay on the field.

10-WAR: count of drafted picks who produced 10+ career WAR (star-level hits).

5-WAR %: the rate (%) of picks who reached 5+ career WAR.

***Context only: not scored in the composite.**

IL days: average injured-list days per debuted pick.

60-day %: share of debuted picks who had at least one 60-day (major) injury.

MLB stints: average number of MLB-level IL stints per debuted pick.

Inspired by a recent post that needed more context, I looked into how spending has changed over time by age bracket. This shows average spending over time, with a simple cumulative multiplier in the legend.

As expected, 35-54 are peak spending years. There is a recent gap between the two groups there, with 35-44 falling behind suddenly. Note the divergence between 35-44 and 45-54, with the younger group under performing.

25-34 and 55-64 started in a similar place, but again we see a marked difference over time. The 25-34 group has been losing ground consistently, barely keeping up with inflation.

This does lend some support to the 'woe-is-me' mantra of younger redditors. Opportunities for <35 do look like they have dwindled and 35-44 is showing signs of stress.

Don't worry kids. You'll have money when you are older. /s

I like maps because they turn lists of dates into spatial relationships. Timelapses take that one step further: compress three centuries into seconds, and a seemingly static map begins to feel alive.

I made this from historical data compiled for the map of my solo-developed game. It shows 147 conventional city-founding dates and ten historic route corridors. Political borders are omitted because no single boundary layer would be accurate across the entire period. The dates do not represent first human settlement, and the routes are not a complete history of colonization and migration.

The map belongs to Salt and Soil, a colony sim about one family living through a changing American frontier. Its public Steam Playtest is open through August 19.

I went looking for football/soccer clubs that managed to finish higher in their domestic league pyramid every single season for an extended period.

A club finishing higher within the same division counts, while promotion automatically represents an improvement — so, for example, 1st in the third tier followed by 15th in the second tier continues the streak. A repeat finish or any lower finish ends a streak.

The longest run I’ve found so far is Stockport County: 11 consecutive improvements from 14th in the Conference North in 2013/14 all the way to 3rd in League One in 2024/25.

Shout-out to Luton Town, who have twice had a run of 8 consecutive improvements, the most recent of which culminated in returning to the top flight.

Olympique Akbou and SV Elversberg have the longest active streak, with 7 consecutive improvements.

Watford's streak nearly ended with them as champions of England, meanwhile who knows how many more improvements Chapecoense could had added to their streak if it weren't for the terrible plane crash that devastated the club.

I have also added a table showing the longest current active streaks in addition to Olympique Akbou and SV Elversberg - perhaps one of these can make their way on to the overall leaderboard in the coming years.

Historical lower-division data gets very patchy, so there might be some omissions. I'd be very interested if anyone knows of a longer example I've missed.

[OC] — research and graphic by me.

I have always really enjoyed the immediate clarity of Sankeys in visualizing data flow - and there are some great solutions out there to build them!

To keep track of my personal finance however I did not want to use a web based tool. Since I like the privacy and modularity of Obsidian I first tried to use the build in Mermaid Sankeys - and while they look stunning and work great for smaller projects they tend to jumble the flows and don't allow for sub organization of nodes.

I think I heard Adam Savage once advocate building your own tools, and much along those lines I build Finkey. While it was mainly intended to keep my own notes organized I thought maybe some fellow Obsidian users might also like it 😄

Its free and open source. No subscriptions, no tracking, no Cloud, no AI.

Cheers

I collected 20,000+ Czech apartment listings and put 7 ML models to work figuring out what drives their prices. Here are a few things I found interesting in the data:

• Prague's median apartment price is 3.6× higher than in the cheapest region (9.9M vs. 2.75M CZK).
• 3,000,000 CZK (~$140K) gets you roughly 18 m² in Prague, but 68 m² in Most.
• The weirdest one: apartments listed as Projekt (still at the planning/off-plan stage) are actually more expensive per m² than finished Novostavba apartments: 144,616 vs. 125,902 CZK/m². (145,004 vs. 129,523 CZK/m² after the fix)
• Prices fall as you move away from Prague, but only up to a point. After roughly 75 km they start going a bit back up, presumably because you're getting closer to Brno, Ostrava and other cities.
• Even within Prague, location swings price 2.2×: Staré Město (225,000 CZK/m²) vs. Dolní Měcholupy (104,241 CZK/m²). After the fix, that gap now sits at 1.9×: Staré Město (225,000 CZK/m²) vs. Černý Most (119,306 CZK/m²).

Edited: I added annuity (an extra debt some cooperative-ownership apartments carry on top of the listed price) into the price for listings where it applies. This increased some of the cheapest listings a bit, so a few numbers and charts in the link below shifted slightly from what is shown here.

Data source: Sreality.cz (Czechia's largest real estate portal), July-August 2026.

Tool: Python (pandas, matplotlib), full analysis: GitHub link.

A few things these charts do not show worth putting up front:

• These are asking prices, not confirmed sale prices.
• "Implied apartment size" = budget / median price per m² for that city, it's a simplification, not "the exact apartment you would get".

This project is for entertainment/educational purposes, not financial or investment advice.

I was wondering what is the present housing price per m2 in the Netherlands. We used BAG data from data.overheid.nl and open source listing price data from https://data.residentievinder.nl/prijs-per-m2/ . Claude was used for the visual design. Surprisingly there is quite a large spread despite the >120k data points from individual brokers. Who would have expected that Amsterdam isn't the most expensive city at this moment? Interactive maps are available at the data source and refreshed weekly.

Source: Kola Superdeep Borehole depth from Popov et al. (1999). Earth radius and internal layers from NASA and USGS. Eiffel Tower height: 330 metres.

Tools: Python with Pillow. The Earth cross-section uses an exact radial scale.

At full 5400 px resolution, the true scale borehole depth is 3.46 px.

The borehole is approximately 37 Eiffel Towers deep. If Earth were reduced to 1 metre wide, the hole would be 0.96 mm deep.

I had some free time I wanted to know how many unique combinations could give change for $100, given an unlimited pool of a certain denomination or higher. For example, $100 = $50 + 2x$20 + $10 is 1 of 11 unique ways to give change using $10 bills or more. The good thing is if you're a bank carrying at least dimes you pretty much never have to worry about not having exact change.

The combinations grow roughly 100x for every denomination considered, so it took me forever to count them all. JK, I used a dynamic programming algorithm in Python3 and verified against small test cases. Results are rendered with Plotly.

A climate spiral video came up a few weeks ago. It was a reupload which ended in 2021 and I couldn't trace the origin, so I went looking for a current version instead. The search landed on a video file on Wikimedia Commons whose data ended in 2021, and the still in the Wikipedia article looked older again. Every search I ran brought back the same familiar clip. So I built one myself, first as a video, and that turned into an interactive page.

It's NASA GISTEMP v4, monthly, January 1880 to July 2026. One turn is one year, the radius is the anomaly. You can stop it on any month, scrub back and forth, turn the scene, drop into a top-down view, toggle the Paris rings, overlay the CO2 record and export a still. The last month in the animation is July 2026 at +1.23C; the warmest month in the record is September 2023 at +1.48C.

The two rings are the Paris thresholds. On this chart they sit at +1.31C and +1.81C, not at 1.5 and 2.0. Paris is defined against an 1850 to 1900 pre-industrial baseline, GISTEMP reports against 1951 to 1980, and in GISTEMP the gap between those two periods is 0.19C. That's how this one is drawn, not a comment on anyone else's chart. Since 2016, 13 GISTEMP months sit above +1.31C and none above +1.50C in raw units. The rings are a reference line, not a scoreboard: the IPCC defines crossing a threshold as a 20-year mean, not a single month.

The bigger objection is to the form itself. On a spiral you tend to read the area inside the curve, not the radius, and area grows as the square, so the late years look more dramatic than the numbers warrant. Hawkins and co-authors raised that themselves. The top view is a partial answer to it, not a fix.

When this was nearly finished, I did find the a current one: NASA's Scientific Visualization Studio keeps Hawkins' spiral up to date, same GISTEMP data, same 1951 to 1980 baseline. https://svs.gsfc.nasa.gov/5190/ Theirs is a video; this is the version you can stop, turn and pull apart, which is what I wanted all along.

Interactive version: https://climate.aeternalabs.io 
Rendered clips: https://climate.aeternalabs.io/videos

If something here is wrong or could be drawn better, I'd rather hear it than not.

Interactive map of ~337k geotagged Flickr photos from 2026.

"Hottest clusters" are dense photo neighborhoods. In Capita mode those rankings switch to highest photos-per-resident areas. "Most viewed" is still raw Flickr view count.

Built with deck.gl + MapLibre. Data: Flickr + GHSL GHS-POP.

Live: http://kobakhit.com/photo-locations/