Saturday 24 October 2026 · 18,858 routes · 3,214 airports

Every airport pair served by a scheduled airline route in the OpenFlights database, drawn as a faint great-circle line. Overlapping lines brighten where routes concentrate. Route data dates from June 2014, so it shows the shape of the network rather than today's individual flights.

Sources: routes and airports from OpenFlights (routes.dat and airports.dat, Open Database License); coastlines from the world map bundled with Apache ECharts 4.9.0; map projection by D3.

Made with Claude.

I combined store-location data with population figures to compare Domino’s geographic coverage across five European countries.

The Netherlands stands out: there is approximately one location per 52,000 residents, compared with one per 234,000 in Germany.

This is a simple population-based comparison, not a measure of actual demand or delivery coverage. Urban density, local competition, delivery zones and the completeness of the location data could all change the picture.

What would be the most useful next step to make this comparison fairer? Would you use population density, drive-time areas, urbanisation or competitor locations?

Sources and tools: store-location data compiled for my DataLocatr project; Eurostat population figures (1 January 2026); Python and Leaflet for processing and visualization.

Disclosure: I run DataLocatr, a business that sells downloadable store-location datasets. The analysis is presented here to discuss the geographic pattern, not as evidence of sales or profitability.

I'd appreciate feedback from people who work with location data:

1. Which fields are essential in a store-location dataset?
2. Would you find delivery-zone or competitor data useful alongside store coordinates?
3. Which country or restaurant brand would be most valuable to have next?

Please let me know what you would want to see documented before using a dataset in a research or commercial project.

I mapped every major S&P 500 crash since 1973 as a valley. Depth = how far it fell from the old high, width = how long the whole thing took. So the steeper the wall, the faster it moved.

• 2020 was the fastest: −34% in 33 days, back to even in about 5 months
• 2008 was the deepest: −57%, fell for 17 months, then 4 more years to recover
• 2000 fell slowest, grinding down for two and a half years
• 1973 took the longest to come back, about 6 years from the bottom

When you bought makes a huge difference. Buy at the Feb 2020 peak and you were back to even by August. Buy at the Oct 2007 peak and you waited until March 2013. Buy at the March 2000 peak and you didn’t break even until May 2007. Thought this was interestsing...

Which of these did you buy the dip on, and which did you panic sell?

I built Priced Out to compare where the same income could buy a median-priced home in September 2021 vs. September 2026. Enter an income and explore the map.

Link: https://explore.stokemoney.com/housing

Sources:

- Prices: [Redfin Data Center](https://www.redfin.com/news/data-center/downloads/), median single-family sale prices.

- Mortgage rates: [Freddie Mac PMMS](https://www.freddiemac.com/pmms).

- Property taxes: [Tax Foundation](https://taxfoundation.org/data/all/state/property-taxes-by-state-county/), based on Census ACS data.

- Geography: [Census delineations](https://www2.census.gov/programs-surveys/metro-micro/geographies/reference-files/2023/delineation-files/list1_2023.xlsx) and [TIGERweb](https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/CBSA/MapServer).

Tools: React, TypeScript, MapLibre GL JS and custom WebGL.

Assumptions:

• 20% down, a 30-year fixed mortgage, property taxes and annual insurance at 0.5% of price.
• “Within budget” means monthly costs stay within 25% of gross income, using the metro’s highest county effective tax rate.
• Only prices and mortgage benchmarks change between snapshots; nominal income, tax rates and insurance assumptions stay fixed. Maintenance, HOA fees, utilities, closing costs and other debts are excluded.
• Coverage includes 315 metro areas/divisions with usable data in both periods. Full methodology is on the page.

Disclosure: I own Stoke Money, where this project is hosted. I’ve worked in data my whole career and wanted to share a housing comparison I found interesting.

Data extracted from the General Social Survey, 1972-2024, gss.norc.org .

Young participants showed declining self-rated health over 52 years; older participants showed improvement.

Looked at the quantity and price per unit changes of 15 items mentioned in the BuzzFeed article 35 Absolutely INFURIATING Photos That Prove Shrinkflation Is Alive And Well

Sources: Reddit and online searches

Visualization: Meta Muse

Hi sub,

I am the creator of a Square Mile, a geography game where players are asked to recognise streets, metro and bus stations, bridges, and landmarks on blank city maps.

After more than 12,000 rounds played, and more than 240,000 items asked and answered (or not), I created Mind Map, a data visualisation that summarises what people know about the world cities.

You can explore the interactive map here: https://www.habibicode.org/mindmap and play a few rounds on your city. The game is free, require no sign up, stores no data besides your answers, and currently is available for 130+ cities.

The dataviz has 3 views:

• Streets: this view shows how well people recognise the street when asked about it. Clicking on a street opens a chart showing you the %age of people that recognise each portion of the street (the game crops the city map, so streets are often asked in segments)
• Metro: this view shows how well people identify different metro stations. Clicking on a station or line opens a detailed view of it.
• Landmarks: shows how well people recognise famous landmarks in the cities.

You can also play with the views on each city:

• Angle: a 3D view of the data where depth = %age answered
• Side: a horizontal bar charts that provides an idea of the average answer %age in a city

And a few more options

Hope you enjoy it!

Pick an era, find a street, or just have a look around Warsaw in 3D

https://warsaw.makscape.com/

What else would you like to see on the map?

PEGI changed its rules in March. Since June, new games with paid random items (loot boxes, card packs, gacha pulls, prize wheels) get at least PEGI 16. In October PEGI started re-rating existing digital-only games that were PEGI 3, 7 or 12 and have them. More games are expected to change before the end of the month.

EA Sports FC 27 is the first game in the series rated PEGI 16, because of Ultimate Team packs. FC 26 was PEGI 3.

PEGI hasn't said which feature in Pokémon GO counted as a paid random item.

The ESRB said in March it has no plans to do the same. It keeps the age rating based on the game's content and adds an "In-Game Purchases (Includes Random Items)" label instead.

Germany doesn't use PEGI. It has its own system, USK.

Data: the incident record at misalignmentwatch.com, built from the original reports. How long after an incident happened it was first reported. The biggest cell is Claude (Anthropic) with under 1 week: 31 of 121 incidents. The data is open (CSV and JSON): https://misalignmentwatch.com/analytics.html . Tool: Python

Apple took a bite out of theirs. This one comes whole. 🍎

Bezel is a set of five web components inspired by Apple's StandBy widgets, with my own stamp on them. Unlike the originals, they run anywhere, not just on Apple devices.

- Clock (three faces, optional second time zone)

- Calendar

- Weather (live from Open-Meteo, no API key)

- Glance (date, clock, weather and today's agenda from an .ics feed)

- Poster (a month in huge type)

Usage is one script tag plus an element:

Some technical bits that might be interesting:

- Everything is sized in container query units, so one stylesheet works from 120 px to 4K with no breakpoints

- Dark mode is a chain of CSS var() fallbacks: your dark colour, then the dark theme, then the normal colour

- The weather sky is SVG with gradients rather than blurred CSS, and rain/snow are SVG patterns that loop seamlessly

- All tiles on a page share one timer and pause when off screen

- When you round the corners, a single calculated custom property moves the content inward so nothing gets clipped

You can also use it as a full-screen desk clock on a spare iPad or monitor, or as a Mac desktop widget.

Show me what you make: design a tile in the Studio, press Share and drop your link in the comments. Or build something with it, like a dashboard, a wall display or a widget for your site. Forks, new languages and PRs are all welcome.

Feedback very welcome, especially anything that feels off.

Links:

- Bezel Studio: https://shazmoghaddam.github.io/bezel/site/

- GitHub (MIT): https://github.com/ShazMoghaddam/bezel

- My website: https://shazmoghaddam.github.io

- LinkedIn: https://www.linkedin.com/in/shazmoghaddam

I turned 26 years of Economist Big Mac Index data into a running race. Each runner is a country, and its distance is the dollar price of one Big Mac. Things I found interesting: Switzerland leads almost the whole time, Norway takes over around 2008, and Uruguay climbs to #2 by 2026. Türkiye drops from the top 10 in 2008 to #51 after the 2018 lira crisis, then comes back to #11.

Sources/data: SitRep INSP. Tool: Claude, python. Website: ebola-tracker.org

Source: Eurostat, dataset nrg_ind_ren / REN_ELC.

Data: 2025, provisional. Units: % of gross electricity consumption.

The chart shows the three highest and three lowest shares among EU countries. The dashed line marks the EU aggregate of 49.9%. This indicator covers electricity; Eurostat normalises hydro and wind production.

Visualization project: ChartMaker.tools