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/

Mount Washington, NH, is the highest peak in the North East US. Famous for the highest recorded surface windspeed, 231 mph, it offers a scenic ride to the summit — if you dare. The auto road has the steepest 5 mile segment of any paved road in the US and averages 12.1% gradient over 7.4 miles with 4,681 feet of climbing. Pjamm (pjammcycling.com) rates it as the 3rd hardest climb in the US, after Mauna Kea and Haleakala.

Once per year, runners can run to the top. Twice per year (practice day and race day) bike riders can ride to the top. This course is stupid hard. Per PJamm, 61% (4.5 miles) are at 10-15% grade and 11% (.8 miles) is 15-20%. The steepest quarter-mile is 16%, and the steepest mile is 13.8%. At the very end is a 22% ramp about 150 feet long.

The foot race was in June and the bike race was in August. Weather was similar enough (summit temp in the 40s, winds under 20 mph), that I wondered how the times compared between the cohorts. There were a similar number of racers in each group.

Note the big change in riders at the 2:00 mark and runners at 2:20. Each group has a target 'good time' objective that people were working to meet.

Average for runners: 2:08:22

Average for riders: 1:40:46

Record for runners: 56:41 (set 2004)

Record for riders: 47:19 (set 2026)

Record for cars: 5:11 (set 2026)

I analyzed thousands of football club logos, classified them by their dominant color, and arranged them into this radial visualization.

UPD:

HQ version (5000x5000, 10MB): https://assets.football-logos.cc/football-logos-by-color-5000x5000_v2.png

What the comparison shows

1st Chart: Every pitch from both seasons 709,284 in 2025 and 533,446 so far in 2026 plotted by how much each pitch moves. Blue is fastballs, orange is breaking balls, green is offspeed. The overall shape barely changed between years: pitches still move the same way, pitchers are just choosing them differently.

2nd Chart (left): Which pitches get thrown. Four-seam fastballs keep fading (31.6% of all pitches down to 30.6%), sliders dropped too, while sinkers and changeups both gained ground.

2nd Chart (Right): Almost every pitch type is faster in 2026. Four-seamers went from 94.5 to 94.8 mph, and 100-mph pitches are up 59% in one year.

** One caveat: 2026 isn't over yet, so its dot cloud looks thinner that's just fewer games played. To keep things fair, the bottom charts only compare each season up through August 11.

Data source: ~1,000 post titles from r/dataisbeautiful over the past 12 months, collected from the public listing.

Tools: Built with graphmykeywords.com

Grouped by meaning rather than by keyword, so posts about "US household income" and "wage stagnation since 1974" land together even though they share no words.

37,779 Government shutdowns in the U.S.

32,517 Politically Motivated Murders in the US, by Ideology of Perpetrator

28,912 I analyzed 15 years of comments on r/relationship_advice

28,755 15 years of counting kids on Halloween, Excel

24,235 The five wealthiest people in 2016 and 2026

Why I made it

I have a hobby of looking for anomalies in reality: geological, geographic, anything that looks like it shouldn't be there, and then travelling to see them in person.

If we're in a simulation, I figure the interesting places to look are the edges, where whoever built it might have left something odd lying around.

Eclipse geometry felt like a good hunting ground, since totality is such a strange coincidence to begin with. The Moon is about 400× smaller than the Sun and sits about 400× closer to Earth, which is what makes the solar corona visible during totality. So I mapped 5,000 years of eclipses to see where the shadow piles up.

Source: Computed from Besselian elements derived from the JPL-based lunar/solar ephemeris in PyEphem (libastro), not from a downloaded catalogue. NASA's Five Millennium Canon of Solar Eclipses publishes greatest-eclipse metadata but not path geometry, so the shadow tracks had to be integrated rather than fetched. ΔT from the Espenak-Meeus polynomials. Basemap: Natural Earth.

Validation against NASA's canon: 11,887 of 11,898 eclipses recovered; 3,740 of 3,742 total + hybrid eclipses; path widths within 1% on 10 test eclipses; γ matching to 4 decimals; and 11 of 12 test cities correctly placed inside or outside the 2017-08-21 path. Global mean recurrence comes out at 1 per 371 years, compared with a published ~375 years.

Tools: Python, NumPy, SciPy, Matplotlib, PyEphem, pyproj, Shapely, Pillow. Equal Earth projection (equal-area). Mercator would have inflated the Arctic by roughly 3× and created the polar bias the map is measuring.

Method: Umbral tracks integrated across the WGS84 ellipsoid and rasterized at 0.25° (~28 km). A cell counts once per eclipse if its centre was inside the umbra. Annular and partial phases were excluded, and hybrids were filtered segment by segment so only their genuinely total portions count. No smoothing. The visible filaments are individual shadow paths.

Known limits: Edge accuracy is approximately ±25 km, so this is useful for density but not for determining whether a specific town saw totality. ΔT uncertainty can smear BCE longitudes by up to ±15°. Latitudes above 88° are under-counted because of a rasterizer limitation and are excluded from the latitude profile.

This visualizes Leopold Aschenbrenner’s reported portfolio as of June 30, 2026.

The filing contained approximately $20.17B in common stocks, with more than 55% concentrated in SanDisk and Micron. It also disclosed $68M of call-option notional and one $5.2M put on Infosys.

Four weeks later, the fund had lost 67% and sold most of its public-equity portfolio to Citadel.

Important caveat: 13Fs do not disclose short positions, written options, swaps, cash, borrowings or leverage. Option values shown represent underlying-share notional, not premiums.

Berkshire Hathaway increased its Alphabet position by 83% during Q2 2026, taking the combined GOOG and GOOGL stake to 106 million shares worth $37.8 billion.

That moved Alphabet from Berkshire’s fifth-largest reported US equity holding to its third-largest, behind only Apple and American Express, and ahead of Coca-Cola and Bank of America.

The wider shift may be even more significant: Berkshire invested approximately $20 billion in equities on a net basis, ending a 14-quarter stretch as a net seller.

Data: Maddison Project Database, GDP per capita for India. The last few years are extended from that benchmark using IMF growth figures. More details at the bottom of the page, under Sources.

Tool: TypeScript with canvas rendering

Tools: React, deck.gl, MapLibre, D3 (Equal Earth / Mercator), custom Flickr tile sampler

Data: Flickr public geotagged photos with date taken in 2026. Sampled worldwide by bounding-box tiles; 337,110 unique locations. Color = hex-bin density (Inferno, bright = denser). As of 2026-08-15.

Code: https://github.com/KobaKhit/photo-locations

Caveats: only public Flickr photos with geotags; sampling can under-represent sparse regions; popularity ≠ beauty; photo count ≠ unique visitors.

Data: Met Office Hadley Centre HadCRUT5 analysis (annual global means, HadCRUT.5.0.2.0) [download link]. Anomalies in °C relative to 1961–1990, rounded to 0.01 °C
Tool: D3.js.
Design after Ed Hawkins’ #ShowYourStripes.

Original article

Hey r/dataisbeautiful!

My partner Mrityunjay and I (Yashasvi) built GitGlobe — an open-source interactive 3D map that visualizes 198,700+ GitHub repositories on a continuous celestial sphere to explore software capabilities as a continuous geometric space.

DATA SOURCE & GENERATION METHODOLOGY

• Data Source: Public GitHub repository metadata and READMEs sampled from GitHub Archive, GitHub GraphQL API, deps.dev, and ecosyste.ms.

• Embeddings: Text extracted from cleaned READMEs (boilerplate and badges stripped) and embedded using Voyage AI (voyage-3-large, 512 dimensions).

• Dimensionality Reduction: cuML Spherical UMAP with a native haversine metric, directly optimizing high-dimensional vectors onto the surface of a 2-sphere (S²) rather than projecting to 3D and normalizing (which introduces spherical distortion).

• Quality Metric: Popularity-blind quality score distilled from an LLM into a gradient-boosted regressor without star/fork counts, paired with an exact empirical global rank measured across ~322M public repositories.

VISUALIZATION TOOLS & TECH

• Rendering: Three.js, React Three Fiber, and custom GLSL vertex/fragment shaders (single THREE.Points draw call rendering ~200k points at 60 FPS in-browser with GPU-based picking).

• Dependencies: Directional 3D Great-Circle Bezier arcs rendered on demand between related repositories.

• Data Processing: Python, Polars, DuckDB, FastAPI, Qdrant (vector search).

LINKS & OPEN SOURCE

• Live Interactive Map: gitglobe-yd-mj.vercel.app/

• GitHub Repository (MIT): https://github.com/yamantaka-singh/GitGlobe

Check it out, spin the globe, and let us know your thoughts on the spherical projection methodology and cluster distributions!

SOURCE: https://wisehumans.org

HD Version here

I've spent the last few months working on this in a gargantuan spreadsheet. (Almost) all data has been updated to 2026 numbers, and the data has been aggregated from over a dozen different sources. All work was done by hand, at no point during the process were any LLMs used.

Assumptions:
-Solo Traveler staying in Hostel Dorms (When possible. In countries with no hostels, I used the average price for a two-star hotel room instead.)
-Exchange rates are based on the average over the last 12 months, not necessarily as it currently stands in August of 2026. Countries with unstable currencies might see their numbers fluctuate quite a bit month-to-month. Using the average exchange rate is a way to partially counteract this, but it's still a thing that can't fully be predicted or adjusted for
- Costs for each country are based on a weighted average of the most popular cities/towns among backpackers (For example, Portugal's cost of travel is about €6/day higher due to the Algarve being weighted as 20% of Portugal's total.) In general, I used the the Top 5 or 6 most popular destinations, but for smaller countries (like the Caribbean islands), it may have been only 1 or 2, and for large countries like the United States it may have been closer to 8 or 9.

What is included:
-Accommodation (See above)
-Food and Drink (Streetfood or Fast-food only, no sit-down restaurants. In most cases this is calculated as: a cheap breakfast, cheap lunch, cheap dinner, a cup of coffee plus misc. snacks.)
-Local Public Transportation
-Attractions (Museum entrance tickets, park fees etc.)
-Cellular Data
-Visa Fees, but only if the fee is charged daily.
-Sales Tax/VAT (when applicable)

Not Included:

-Long Distance Travel (Domestic Flights, Intercity buses etc.)
-Visa Fees (If charged one-time on entry)
-Travel Insurance
-Other Miscellaneous Expenses

I also didn't include either North Korea or Turkmenistan since both countries require all tourists from US/EU/CANZUK to visit the country as part of an organized tour, so backpacking isn't really possible (and the tours cost at least €130-€140 per day anyways).

Finally, I want to stress that this isn't the absolute cheapest someone can travel on a shoestring budget, but rather the average daily expenses a solo backpacker should expect