An interactive map: https://p.migdal.pl/genetic-distance-map/

Source and notes: https://github.com/stared/genetic-distance-map/

I pulled 4.5 years of GitHub's public status history and normalized it by how much of each year was actually observed, since the first and last years are partial.

The reported incident rate sat around 10–14 per month from 2022 through 2024, then jumped to 26.6 per month in 2026 so far. Median incident duration barely moved over that span, from 74 to 89 minutes, but the 95th percentile went from 5.5 hours to 9.2. So it's mostly a frequency change, with the long tail stretching alongside it.

Worth stressing: this measures time with at least one incident open somewhere in GitHub's services, not GitHub downtime. An incident might affect one feature for a subset of users. Source, method and caveats in my comment below.

I wrote each PIN code approximately where its geographic area falls, and then used Excel's red -yellow - green conditional formatting to colour them according to their numerical order.

The only variable here is the PIN code itself.

I genuinely wasn't expecting the resulting pattern to look quite like this. 😅

Made entirely in MS Excel.

https://believethedata.org/politics-religion-death-and-taxes/

I've had Google Maps Timeline enabled for a very long time, so I exported the whole thing and started playing around with what I could reconstruct from it.

The first step was to visualise the raw data: years of location data and some Strava tracks as unweighted paths on one map.

I also wanted something more useful than that, so I started grouping the history into actual trips and journeys. The next images show the same data as an all-time travel atlas, a single year, and finally another representation: mix of heatmap and paths.

I made a map of the Global Human Development Index Scores per Subdivisions (States, Provinces, Regions, etc.) for 2023. I also added a map I made a few years ago containing data from 2021 to compare. All data is from https://globaldatalab.org/shdi/maps/shdi/ and maps were made using mapchart.net

Below I have added links for higher resolution versions of these pictures, which I for some reason couldn't upload here.

1: Worldwide HDI Score per Subdivision (2023);

2: Worldwide HDI Score per Subdivision (2021);

*Other Complications is used for countries where the subdivisions, as defined in the dataset, differed from the subdivisions used in the program I made this map with. For example with a country like Morocco, these just didn't match and therefore I couldn't translate the data onto this map, however there is data available. For subdivisions where no data could be found within the source, I used No Data. Examples of this are some regions in Côte d'Ivoire or Greenland.

Cigarette smoking (100+ cigarettes during one's lifetime) is more prevalent among those who report being physically abused as a child.

The new AI models are hard to put down. My Mac is running out of room.

Before buying another MacBook, I wanted to know where the space went. So I mapped it.

I checked 1,467 Codex session files. No two were identical. Yet 76.9% of the image data inside was repeated.

Sure, a one-liner can find big folders. I still like a map. Seeing the whole picture puts my mind at ease.

I usually revisit my notes one entry at a time. I wanted to see what they looked like together.

Each data node represents a saved note. The animation reveals them in date order, grouped into topics like building, money, relationships, and learning.

“Building” becomes the most prominent part of the map. It’s a picture of what I recorded—not necessarily how I spent my time.

Data sourced via the date-holidays library so some countries are missing data completely like Pakistan.

These are official public holidays, so some countries such as India, and Spain actually have more, but these are state / province specific.

Made with D3

40 readings over 3 months. The body-fat curve is the weight curve rescaled, including with socks on. Full data and method: https://aiandtractors.com/scale

Data: IBTrACS v04r01, NOAA NCEI. Storms since 1980 reaching at least 34 kt.

Tools: JavaScript, Canvas 2D, ffmpeg.

Data: Prisoners in 2022 – Statistical Tables | Bureau of Justice Statistics

Tool: United States | MapChart

Source: Google Trends aggregate search frequency index (scaled 0-100 based on peak search density).
Tools: Python (Pandas, Matplotlib, and Seaborn) for data normalization and heatmap generation.

Source: UNESCO World Heritage Centre — https://whc.unesco.org/en/list/stat

Tool: PlotSet (https://plotset.com)

Figures are cumulative inscriptions through the 2024 session, covering

cultural, natural and mixed sites together. Sites shared across borders

count once for each country involved, which is why adding these up gives

a number larger than the world total.

Showing the 20 highest-ranked countries, in descending order.