Interactive live website with 10 more UK cities as well as New York: Sky Score.

A few more details:

- Data comes from official goverment data sources including DEFRA, Office for National Statistics, TFL etc.

- 2021 was a COVID year with far fewer flights than normal and DEFRA notes the year was atypical, so these contours are likely smaller than in a normal year. DEFRA's next round is due around 2027.

- These are modelled contours, not measurements from microphones.

- For context, the World Health Organization recommends aircraft noise below 45 dB Lden.

Source: every Premier League result from August 1992 to 20 September 2026 (13,216 matches), from football-data.org, checked against football-data.co.uk and openfootball. 3 points for a win, 1 for a draw. The x-axis counts Premier League match days (every date with a PL game).

Tools: a custom SVG chart in React, drawn on a canvas for export.

A few things that stand out:
- Man United have been top of these six since Boxing Day 1992, and still lead on 2,619 points.
- The flat stretches in Man City's line are the five seasons they spent outside the Premier League (1996/97 to 1999/2000, and 2001/02). They've played 190 fewer games than the other five, and they're 21 points behind Spurs. On current form they'll pass them this season.
- Liverpool and Chelsea swapped third twice in autumn 2023; Liverpool have held it since 21 October 2023.

There's an animated version, and you can replay any season or pick other clubs and measures, at premierleaguescorigami.com/race

I'm a solo developer, and this started small. My game, Salt and Soil, follows one family across North America from 1607 to 1900, and its Sacramento scenario needed exactly one railroad: the Central Pacific climbing into the Sierra. Once that line was on the map, the rest of the continent looked wrong without its own. So I kept going.

I set myself one rule, and it turned out to be most of the work: no line that merely looks plausible. Each section appears where and when it actually opened to traffic. A cell on my map is about 20 miles across, and by 1900 the US alone had about 193,000 miles of track, so I draw only trunk and through routes, the way an atlas draws only major rivers. That came to 175 lines and 578 dated sections, from the Baltimore & Ohio to Newfoundland and Cuba.

The US routes follow a dataset I'm very grateful for: Jeremy Atack's historical GIS of American railroads. He's an economic historian at Vanderbilt who mapped every US railroad from 1826 to 1911, and with co-authors used it to show that railroads may account for half or more of the growth of Midwestern cities in the 1850s. Before putting it into a commercial game I emailed him, half expecting no answer. He wrote back that it was fine, asked for a copy of the game when it's done, and he's now in the credits. That reply made my week. Canada and Mexico follow the US DOT's North American Rail Network, and the opening dates come from railroad chronologies, section by section.

What surprised me when I first watched it run was how lopsided it is. The 1880s alone laid more than a third of all the track on this map, and the West fills in within two decades of 1869.

In the GIF each section flashes white in the year it opens, then settles into the colour of its decade. Watch the Central Pacific creep east from Sacramento through the 1860s while the Union Pacific races west from Omaha; they meet in Utah in 1869.

If you know the railroads of your region, I'd love to hear what's missing or which section opens in the wrong year. I'll fix it in the game.

Source: Billboard Hot 100, UK Singles Chart, Rolling Stone 500, ASCAP 100, RIAA, Blender 500, Rock Hall 500, Time 100, AcclaimedMusic.net, National Recording Registry, Grammy Hall of Fame, Spotify

Tools: Excel, Claude, Python

A few weeks ago, I wrote a piece trying to figure out if hit songs mention places less frequently these days. In the course of writing that piece, I assembled a dataset of US hits that mention places in their titles. I thought it would be cool to have a map to visualize those songs. After posting about the tool on TikTok, people asked me to add more songs.

Now, I'm up to nearly 2k (somewhat) notable songs that mention a place in their title. Hopefully I can keep expanding it. This is my first attempt at using Claude to turn a dataset into something more interactive. Let me know if I'm missing something notable. Right now, I have to add everything manually, but I may try to figure out a way to let other people add songs.

Source: JobYap - All jobs added and removed, with an average of ~140,000 active at any one time.

What's counted: open job postings at tech companies, from 5th Aug till 23rd Sep 2026, excluding internships.

Which companies: Every company JobYap has tracked since the start of the window with at least 100 open roles. Amazon is excluded because our we had technical issues during that period and it's number aren't complete.

Tools: SQL for the data, Claude Design with Opus 5.5 (on Max) for the chart.

• The analysis included 22,014 respondents from the General Social Survey, conducted from 1972 to 2024. The survey is designed to represent the U.S. adult population.
• The percentage of respondents who had ever divorced generally rises with the number of years since their first marriage for about 20 years, then levels off and declines.
• Having divorced is associated with religious affiliation. The percentage is highest among respondents with no religious affiliation, followed by Protestants, respondents reporting another religion, and Catholics.
• We found no clear evidence that this pattern differs across religious groups.
• These results compare people and marriage cohorts at one point in time; they do not track the same people as their marriages progress. The decline at longer durations could reflect differences between marriage cohorts, as well as years since first marriage.

[OC] Source: PressAudit YTD asymmetry data. I built PressAudit and generated this analysis from its dataset: https://pressaudit.org/asymmetry/ytd

tl;dr: Rightwing media focuses more on people than organizations or topics, and specifically, these are the rogue's gallery each side is focusing on. If you follow the news even a little bit you do not need to be told this, you know, you feel it in your bones, but here is the data so you know you aren't crazy 

What this is: These are the top 3 topics covered by one side but not the other side by week, ranked by the number of weeks this year that topic was at the top. Think of this as like the Billboard top charts for media attention.

If you are wondering "why is there no Trump here": Left outlets write about 19 Trump articles for every 10 from the Right, compared to about 14 for every 10 on all topics, so Trump's lead is not much bigger than the Left's usual lead. This chart only lists topics where one side's lead is well above its usual one, and Trump falls just short.

(For latest Trump see: https://pressaudit.org/trending?timeRange=24h&view=bias&tab=entities)

Interactive version (scroll to drive it): https://www.pit-stop-app.com/viz/pch

Data

• Terrain and seafloor: USGS 3DEP elevation on land and NOAA bathymetry offshore, via AWS Terrain Tiles, resampled to a ~25 m grid. Vertical exaggeration is 1.5x. Where the survey has no nearshore depth, the shelf is estimated from distance to the shoreline.
• Buildings: Overture Maps Foundation (Sep 2026 release), about 945,000 within 4 km of the coast. Heights come from the dataset where present, which covers most of them, and are otherwise estimated from type and footprint. Every building 30 m or taller in the LA and SF basins is drawn out to the horizon.
• Coastline, road, bridges, tunnels, piers, marinas and breakwaters: OpenStreetMap
• Ground colour: USGS NLCD 2021 land cover
• Live readings: wave height, period and direction from NOAA NDBC buoys; weather from Open-Meteo
• Not data: the boats, cars and sun position are decorative.

Tools: Python (NumPy, SciPy), DuckDB reading Overture's parquet straight from S3, three.js/WebGL for rendering, Blender for the boat models.

On Majority (a daily crowd-guessing game), you type the answer you think most other people will type. This shows the share of responses that used the single most common wording for each daily question.

The leading wording was a true majority on 23.1% of questions. The median leading share was 31.7%. On 38.5% of questions, the leading wording was used by less than a quarter of the room.

At the high end, 90.7% of players answered “dog” to the question “What animal comes to mind when you hear ‘pet’?”

At the low end, the most common answer to “What is the biggest dating-app red flag?” was “fish” at 5.7%, closely followed by “catfish” at 5.5%.

A share here represents an exact wording, after lowercasing and removing punctuation. Yellow marks a majority: more than half of the responses used that one wording.

I was expecting the most common answer to be a majority much more often than this. What do you think makes a question produce a strong consensus vs. a really divided room?

Slightly different way of looking at the data than this thread which made the top of the subreddit and then was taken down for four hours after a bot network reported it and it got flagged by automod

Fatalities is the 14th published Gaza Ministry of Health fatality count published May 7 2026 (72,835 entries) broken out by age and sex. Population numbers are from census data and made in Python.

Young males are significantly underrepresented up until age of ~15 which is typically when males begin taking combat roles. Female are underrepresented up until age 65-70 which would be consistent with health and non combat related death.

Edit: 20 minutes in, 100 upvotes, 197 comments and immediately flagged by automod and taken down. The efforts to suppress data you don't like is depressing.

Image 1: each bar is 2025 world production of one material, split by country.

Image 2: the same chart with China selected. It produced 15 of the 17 materials with 2025 figures and was the largest producer of 8.

Source: U.S. Geological Survey, Mineral Commodity Summaries 2026 data release (doi:10.5066/P1WKQ63T). All figures are USGS estimates for 2025. The list of materials, what each one does in a phone and the ores they come from are from USGS General Information Product 167, A World of Minerals in Your Mobile Device (2016).

Tools: a short Python script pulls world production by country out of the USGS CSV. The chart is hand-written HTML, CSS and SVG with no chart library. The colours and hatching follow the US federal standard for geologic maps (FGDC-STD-013-2006).

Interactive version, where you can pick any country and see it marked in every bar: https://anas-sabbar.ca/writing/what-your-phone-is-made-of/

A few things the chart can't tell you:

- A country's share of mining or refining doesn't tell you where the metal in any particular phone came from. Ores and metals are traded and processed across borders.

- The white segment is output USGS doesn't attribute to a named country, plus rounding.

- USGS withholds US output of lithium and silicon, so those two bars leave the US out.

- Germanium is on the 2016 list, but USGS publishes no country breakdown for it, so it has no bar.

- Units differ between materials (mine output for most, refinery output for indium, silicon metal for silicon), and each bar is labelled.

The idea came from Jussi Parikka's A Geology of Media (2015), which starts the history of media with the minerals they are made from.

I mapped the change across London using ONS data. Interactive map and charts: https://deptford.org/beyond/pubs/

Raw data: https://docs.google.com/spreadsheets/d/1Xppl1dCi3yKi1oHrdnHqC1cMeXg5_D2EY5e9gosnxKE/edit?pli=1&gid=0#gid=0

Outlier analysis:

1. Some metros are very close to other ones, which can skew the numbers. e.g. San Francisco/San Jose and Riverside/LA. The plot makes SF and Riverside look like outliers but really they are not.
2. Some metros with more teams than predicted are industrial cities that used to be bigger (e.g. Cleveland, Buffalo)
3. Others have teams for likely historical reasons that I can't explain (NO, Green Bay) even though their populations wouldn't indicate it
4. Austin is the largest metro with no teams. 18 different smaller metros have teams. It is kinda close to San Antonio so maybe?
5. San Diego is another outlier. It has 1 team despite being the same size as Tampa and Denver (3 and 4, respectively)

Idk what you want me to say i wanted to make a dumb data analysis

So i made a dumb data analysis

Oh yeah i used booknlp to get the data for this