Out of curiosity I combined 25 open datasets (national statistics, geolocated real-estate sales, rents, natural-hazard registries, 2050 climate projections, GP access, train stations...) to score every French commune on seven axes: housing, living environment, transport, culture & leisure, demographics, economy, and exposure to natural hazards.

Honest caveat: the overall score is a mean of percentile ranks, so it compresses (median 52, best 83). And no dataset knows what it's actually like to live somewhere. Interactive map and full method in the comments.

Lexis diagrams were invented about 150 years ago and have been used mostly by demographers. Even if you're not a demographer, they're worth learning about because they can reveal subtle patterns if you have the right kind of data.

This example, created using R and ggplot and Current Population Survey data, shows the percent of men and women aged 25-80 who obtained a bachelor's degree or higher between 1995-2025. The diagonal white lines show the birth year for each age group. The diagonal bands therefore represent age cohorts. Dark colors indicate low rates of degree attainment, while bright colors indicate higher levels. For example, in the second graph, you can see that early generations of women had lower attainment than men, but have reversed that trend and now have higher rates.

There's a lot to learn about Lexis diagrams. Check out my recent Substack article.

The United States is home to thousands of data centers. They are part of the infrastructure that powers many digital services, including email, gaming, streaming, social media, and, increasingly, artificial intelligence (AI).

As this chart shows, between 2018 and 2022, spending on building data centers in the US hovered between $500 million and $1 billion per month (when adjusted for inflation).

But since late 2022, large investments in AI have led to a rapid growth in data center construction — both by expanding existing facilities and building new, larger ones.

The latest figures released by the US Census Bureau estimate that $4.4 billion was spent in June 2026 alone — a 5-fold increase compared to November 2022, when ChatGPT was released by OpenAI.

These figures cover privately-funded construction and represent only the money spent on building the actual facilities, including materials and construction labor. They don’t include the cost of IT hardware like servers and storage, which can represent a very large share of total investment.

Data sources: United States Census Bureau (2026); U.S. Bureau of Labor Statistics (2026)

Tools used: Python, OWID Grapher, Figma

Data: the 2027-28 Student Aid Index worksheet (Formula A) published by Federal Student Aid, computed line by line for a two-parent family of four with one child in college and no assets counted. Cost medians are the US Department of Education's College Scorecard 2024 release, across 1,797 public and 1,354 private nonprofit institutions. Family income shares are Census CPS ASEC 2025, table FINC-07, whose top bracket is $250,000 and over, which is why the $300,000 row says "inside the top 14%" rather than a share of its own.

Tools: Python, pandas, matplotlib.

One thing the headline compresses, so I want to state it rather than have it found. The bar is the Student Aid Index, not a bill. It decides how much of a school's price is yours, so it only bites where a school costs more than the bar does.

Detailed methodology at worthmydegree.com/welcome?src=re

Source: Public professional-profile employment and education histories aggregated by a commercial data provider, snapshot August 2026. 21,166 current employees across the five labs, 8,231 institutions. Colleges grouped to parent bodies, so Wharton counts under Penn and Haas under Berkeley.

Full methodology and the raw counts (CC BY 4.0) at trayo.ai/research/ai-lab-colleges

Tool: Python for the aggregation, hand-written SVG for the chart.

Known limitation: counts of current staff run high. People remove an old employer from a profile much more slowly than they add a new one. Against public headcounts the gap looks widest at OpenAI, roughly 15 to 20%.

Disclosure: I work at Trayo, which published this. The entity resolution is the same thing our software does, pointed at five companies instead of a customer's target accounts.

I couldn't help but noticing Claude font and styling on a lot of the top posts over the last 6 months.

Does this sub have an official policy on the use of AI?

Personally, I don't mind prompting to create infographics, but at the very least dump your prompt in the post, the model(s) used, and maybe [AI OC] rather than [OC]

I'm using a Raspberry Pi to feed ADSB data to tracking sites. The device's network graph shows how busy the airspace around me is.

You can tell that the airport nearby opens at 5am and traffic fades out before it closes at 11pm.

I wanted to see what GitHub’s incident history would look like if it were visualized like a GitHub contribution calendar.

Each square represents one day:

• gray = no incident started that day
• green = one or more incidents started
• darker green = more incidents

The visualization covers March 2022 to September 2026. In the dataset I analyzed there are 829 incidents across 575 days with incidents. One thing that stood out to me is how much denser the calendar becomes in parts of 2025 and 2026.

Data source

The data comes from the open-source github-statuses project https://github.com/mrshu/github-statuses

Important caveat: these are GitHub Status incidents, not necessarily periods when all of GitHub was completely down. An incident may affect only one service or a subset of users.

Tools

I did the analysis in Python with pandas and built the calendar with the ActivityCalendar widget in Mercury. I also made an interactive version where you can filter by date, impact and component, and switch between incident count and total incident duration. Notebook source code in on GitHub. Hope it works ;)

Hurricane Helene made landfall in Florida as a Category 4 storm on September 26, 2024, bringing historic rainfall and catastrophic flooding across the Southeast. This animation tracks hourly precipitation and streamflow at USGS streamgages along Helene's path (Sep 24–29, 2024). Spikes appear at gages flowing at or above the 75th percentile, with height scaled to streamflow percentile and color marking flood severity. USGS stream gage data helps forecasters and emergency managers make decisions that protect lives and property.

View video from USGS Vizlab here: https://www.usgs.gov/media/videos/hurricane-helene-animation-usgs-vizlab

https://www.danielpradilla.info/blog/which-british-music-city-punches-furthest-above-its-weight/

Interactive explorer: https://www.danielpradilla.info/uk-music-cities/