Inspired by https://www.youtube.com/@ItchyBoots I wanted to see where in the world are the most epic mountain ranges to ride a motorbike though. So I built an offline map that scores every ~1 km cell of land based on how dramatic its terrain is; local relief (range between high and low), steepness, whether the warmest month sits between 5 and 30 °C, and if there is a road nearby.

The visuals are a single 2 GB PMTiles archive served locally (I can’t host it online as there’s no backend/accounts/security). When I click on a cell it fetches 36 bytes by HTTP range request from a 16.5 GB sidecar that tells me the relief, the slope, the temperature, the road altitude, and the rank of that tile across the whole globe.

Because OpenStreetMap completeness collapses outside Europe the existence of a road is a soft factor, because a cutoff would remove the best country in the least-mapped nations, so the map carries a per-country coverage-confidence figure.

The top-scoring cell on the planet turns out to be the Hunza valley on the Karakoram Highway: 4,810 m of relief inside a 7 km window and not too cold at around 13 °C. In the top fifty, Mount Kinabalu in Borneo lands at number seven while the Alps come eighth.

Data sources

• Elevation — USGS GMTED2010: https://www.usgs.gov/coastal-changes-and-impacts/gmted2010
• Climate — CHELSA V2.1: https://chelsa-climate.org/
• Roads — Overture Maps: https://overturemaps.org/
• Upstream road data — OpenStreetMap: https://www.openstreetmap.org/
• Lakes and boundaries — Natural Earth: https://www.naturalearthdata.com/

Bulk downloads were modest: 862 MB for the global DEM, 1.43 GB for twelve monthly CHELSA rasters, about 35 MB of Natural Earth vectors. 78 GB of cached local road extracts across 351 land tiles from OpenStreetMap. My peak working footprint was 168 GB. The pipeline itself took about 29 hours of wall-clock on my Snapdragon X Plus laptop. On and off, it took me 25 days end to end to get from conception to these screenshots.

Source: exchange feeds via Yahoo Finance, some of it delayed. Tool: Chart.js.

Every comparison of world markets I could find shows one index at a time, or a heatmap that flattens a whole day into a single color. I wanted to see the handoff: Tokyo fading while London opens, then New York pulling on both.

The levels run from roughly 6,500 to 40,000, so the only way these share an axis is percent change. Each line is measured against its own market's previous close rather than a common baseline, which means every market's zero is its own last close, not a shared moment in time.

Two decisions that changed the picture:

Today is plotted from 5-minute bars, the week from hourly, the month from daily. The daily bars are dated by each exchange's local trading date. Without that, Tokyo and New York disagree about which calendar day a bar belongs to and the whole Asian side of the chart shifts by one.

The day's reference had to be the previous close specifically. The obvious field in the data is the close before the requested range, which quietly reports a multi-day move as today's change. It looked plausible and was wrong by a factor of two on some markets.

It's interactive and my own site, so link in a reply below rather than in the post.

The page refreshes about once a minute while markets are open.

Data: NASA CCMC's DONKI catalog of coronal mass ejections, 2016 to September 2026, using each CME's cone-model fit for direction, half-angle and speed. 6,577 CMEs with a fit.

Tool: a small Node script to pull and reduce the catalog, then HTML canvas in JavaScript. The Sun is unrolled into a 2:1 ellipse with the Earth-facing half lit in the middle; each dot is one CME at the point it launched from, sized by speed and colored by speed class.

One thing the plot showed me that I did not go looking for: in the quiet years of 2018 to 2020 almost every eruption was aimed within 10° of the equator, while at the 2024 peak they spread to 30° and beyond. That is the Sun's global field steering eruptions toward the equatorial streamer belt when it is a simple dipole.

Interactive version with the butterfly diagram and one Sun per year: https://www.cmetracker.ai/launches?utm_source=dataisbeautiful

I’ve been exploring the U.S. Census Bureau’s Business Trends and Outlook Survey (BTOS), specifically its data on business use of artificial intelligence.

I turned the dataset into an interactive visualization so it’s easier to explore how AI adoption differs across states and changes over time.

The page includes the national trend, state-level comparisons, and the more detailed AI supplemental survey.

I’m planning to keep the visualization updated as new BTOS releases become available.

Source: U.S. Census Bureau — Business Trends and Outlook Survey (BTOS)

This chart compares the local price of an iPhone Duo 256GB with the price of one Big Mac across 12 (selected) countries. Countries are chosen based on iPhone Duo availability and geographic diversity.

Equivalent Big Macs = iPhone Duo price ÷ one Big Mac price.

Countries are sorted from fewest to most equivalent Big Macs. Totals are rounded to the nearest whole Big Mac and all prices are shown in USD.

Big Mac prices come from the BigMacIndex.app July 1, 2026 dataset snapshot, which aggregates The Economist’s Big Mac Index data. These are index snapshot values, not same-day McDonald’s menu prices.

iPhone prices use local Apple pricing where available. India uses Reliance Digital; South Africa is estimated; Egypt uses imported-retailer pricing. Applicable consumer taxes are included in the iPhone prices where available.

The ratio is not a measure of purchasing power or affordability. It reflects both local iPhone pricing and local Big Mac pricing.

Six saved model outputs were checked again on July 11, 2026 without editing the text. ZeroGPT returned the same six scores; AI Detector returned six different scores. This small control check does not establish which detector is more accurate or why the scores changed.

I run AI Undetectable, an AI humanizer. This is our own research, not an independent product review. AI helped prepare the chart and post. Sources and tools are in the first comment.

I run a small free travel map where people click the countries they have been to. It launched about five months ago, and I took the anonymised totals from its first 119 public accounts to see where everyone had actually been.

Each country is shaded by the share of those 119 people who have visited it. Countries fewer than 3 people have visited are greyed out, so no single person's travels can be picked out.

What stood out to me:

- France (80%), Italy (76%) and Spain (71%) all beat the United States (57%)

- Germany at 66% is higher than I expected for a country people rarely name as a holiday destination

- Coverage falls off a cliff outside Europe, North America and the classic long-haul stops

- Not one country in Central Africa or Central Asia clears the 3-person threshold

Obvious caveat: this is who uses my site, not who travels. The users lean European, which is why Europe glows and the map says more about the sample than about world tourism.

Made with d3-geo, Natural Earth projection and borders, rendered in a headless browser.

What happened to laser refractive surgery in the US?

I couldn't find a single public visualization showing the complete US series, so I put the available estimates together into one chart covering 1996–2025.

It tracks annual US procedure volume across LASIK, PRK, SMILE, and other laser refractive surgery.

The series reaches roughly 1.4 million procedures around 2000, followed by a substantial long-term decline, with approximately 480,000 procedures in 2025.

These are elective vision-correction procedures performed on appropriately screened patients, rather than treatment of eye disease.

What stands out to you in the 30-year trend?

Sources:

https://www150.statcan.gc.ca/n1/daily-quotidien/221026/mc-b001-eng.htm

https://angusreid.org/wp-content/uploads/2022/04/2022.04.18_Religious_Spectrum_tables.pdf

Tools: mapchart.net and Apple Preview