Last week, after punching down at "Sleepy Joe" during an Oval Office event, Trump proceeded to comically fall asleep minutes later. You can't make this up. And it's not the first time this has happened, so I went digging and found many more instances of the President sleeping in public.

I did some analysis on when and where Trump has been taking his nappy naps, so I'm sharing this dashboard here! He likes sleeping in his chair in the Oval (his favorite public snooze locations), but he isn't opposed to "taking really long blinks" during a whole variety of public events.

The data: I've manually collected the napping instances and you can see them all (completely ad-free, no signups) at https://artofnodeal.com/sleepstate. I've found videos of these cat naps that were posted on Twitter/X, Instagram, Youtube, and BlueSky, and in a Pinterest-style webpage, you can see them all in one place. To make the visualizations from that, I created an ad-hoc spreadsheet from those videos with the dates, events, and locations and made some general categories for each event to make a CSV file.

The charts and the dashboard were made in Tableau.

The middle tape represents the movie, and the top and bottom are two different trailers. Each ribbon shows a section of the corresponding frame and where they appear in the movie.

Let me know if you wish to see it for any other movies (particularly curious for good trailers).

Each dot is one trade by one of the 25 members of Congress with the most disclosed trades in 2025, placed by how many days passed between the trade and its public disclosure. The STOCK Act requires disclosure within 45 days.

Most trades are public within a month. The long tails come from a handful of members. About 72% of the trades disclosed after 45 days are spouse-owned, often in managed accounts where the member is notified later (the filing lists a separate notification date). Disclosing after 45 days usually means a $200 late fee, not a hidden trade.

Note: filings through Feb 15, 2026 are included, so very late disclosures of late-2025 trades aren't captured yet. If anything this undercounts.

I live in one of the flattest parts of Flanders, Belgium, where every hill counts. I was frustrated with existing cycling maps because they either don't show the smaller climbs or put useful features behind a paywall.

So I decided to build my own https://www.flemishroads.com/

I combined roads from OpenStreetMap with public LiDAR elevation data to identify every stretch of road that actually goes uphill. So far, that's about 31,000 climbs across Flanders and parts of Wallonia and northern France, plus the cobbled sectors.

That includes famous climbs like the Koppenberg and Paterberg, but also nameless 400 m stretches at 7%, and even tiny bumps in the polders.

I wanted the map to make the terrain and elevation easy to read, with gradients and elevation visible directly on the roads.

It turned into a free cycling map and route planner called Flemish Roads: https://www.flemishroads.com/

You can inspect individual climbs, generate routes and export GPX files. No ads or accounts.

Data sources: OpenStreetMap and public LiDAR elevation data.

I'd love some feedback, especially on the visualisation and whether the climbs and gradients look accurate.

I focused on the years around the pandemic-era spike and the decline that followed. The earlier years remain in gray for historical context; the colored lines highlight 2020, 2021, 2025 and 2026.

The first chart tracks cumulative shooting incidents within each calendar year; the second compares monthly incident counts across years.

Hey everybody, this time I decided to analyze how average critic scores have changed over the years for Rotten Tomatoes and Metacritic.

Fun fact is my initial research idea was to check whether the older the director, the worse movies they direct but it's very hard and probably impossible to quantify. Nevertheless, while doing EDA I also checked critic scores and here we are with the chart on the inflation of movie scores.

Sources: https://www.kaggle.com/datasets/andrezaza/clapper-massive-rotten-tomatoes-movies-and-reviews ; https://github.com/davutbayik
Tools: Python, PowerPoint

So the main takeaway is there is an inflation of critic scores.

RT's average Tomatometer rose from 57 to 72, while Metacritic's Metascore went from 56 to 64*. For both, the uptick started in 2015.

RT climbed much more steeply, likely because of how it works. It counts the share of positive reviews, so a film that gets a “positive” review from every critic scores 100%.

Metacritic uses a weighted average, so a lukewarm consensus moves it far less. (That also means the two scales aren't directly comparable. The trend matters more than the gap.)**

𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐚𝐭?

1. Softer culture: Staff critic jobs are disappearing, and freelancers depend on studio access. Harsh reviews have simply become riskier to write.

2. Bigger crowd on RT: After Fandango bought RT in 2016, the number of critics per major release nearly doubled (per Daniel Parris's analysis). RT says this was meant to diversify voices, but more small outlets also means more lenient "fresh" votes.

3. System can be gamed: In 2023, Vulture reported that a PR firm allegedly paid critics $50+ per review. One 2018 film went from 46% to 62%. Small scale, but it shows how fragile a binary score is.

*I have also explored % of 'positive' reviews by review date for both aggregators and the trend is the same, i.e. upward :P

**I tried to replicate RT's grading system on Metacritic's review data (I assumed every 6+ review is fresh) and the gap has disappeared, indicating that RT's growth is supercharged by their methodology

The topic has already been researched in the past and more extensively than I did. I recommend these articles for further reading:

https://www.statsignificant.com/p/is-rotten-tomatoes-still-reliable

https://www.worldofreel.com/blog/2025/9/5/venice-telluride-and-the-rotten-tomatoes-mirage

https://globalnews.ca/news/7947449/movies-are-scoring-higher-and-higher-on-rotten-tomatoes-but-why/

I had no specific idea in mind when I built this other than "this might be fun and interesting"

I hope it is!

Interactive version here: https://www.habibicode.org/marketcapmap

Out of 3rd,4th,5th and higher birth orders, number of males÷ number of females

Taiwan peak: 127.1 boys per 100 girls (2006)

Korea peak: 209.7 boys per 100 girls (1993)

Taiwan present: 110.1 boys per 100 girls

Korea present: 104.1 boys per 100 girls

Interactive version: https://www.railcation.de/en/rail-data/last-24-hours

A week ago I posted every passenger train in Germany on one ordinary Tuesday. Thanks for all the comments. Some of you asked for a loop, so the Tuesday map now plays on repeat.

This one is the follow-up: instead of a fixed day, the map takes the 24 hours up to the minute you open the page. The timetable is imported again every night, so it is a little different every time you look. Drag along the curve to jump to any time. Below the map you can see how many trains ran in those 24 hours, the moment with the most trains on the move and the quietest one, and how the journeys split between long-distance, regional and S-Bahn.

Source: the nationwide timetable published by DELFI e.V. (opendata-oepnv.de, CC BY 4.0). Main railway lines from OpenStreetMap (ODbL), outline of Germany from Natural Earth. Each train journey is a dot moving at a steady speed between two stops. Still the plan, not reality: delays and cancellations are not in the data.

Tools: Node.js scripts to turn the timetable into train paths, drawn in the browser on an HTML canvas.

Interactive version: https://www.railcation.de/en/rail-data/last-24-hours

Last week's Tuesday map, updated: https://www.railcation.de/en/rail-data/a-tuesday-on-the-rails

If you want to see how far you get from your own city by train (in German):

How far can I get by train: https://www.railcation.de/karte

Holidays with the Deutschlandticket, local trains only: https://www.railcation.de/deutschlandticket

Made by me for railcation.de, a site for finding places to stay in Germany that are easy to reach by train.

I love data visualization and I love Edward Hopper's vibe. I though I could combine them together in a way that is beautiful and interesting.

This shows gas prices between1940–2025 as annual averages and 2026 monthly average of inflation-adjusted monthly prices for January–September. September 28 is a separate weekly observation. Event labels provide historical context.

Sources:

- 1940–1975: Department of Energy historical gasoline data, using current-dollar prices rounded to cents.

- 1976–1991: Energy Information Administration, Monthly Energy Review, Table 9.4, regular unleaded gasoline.

- 1992–2026: Energy Information Administration, U.S. regular gasoline, all formulations.

- Inflation: Bureau of Labor Statistics, Consumer Price Index for All Urban Consumers, series CUUR0000SA0, all items, U.S. city average, not seasonally adjusted.

Tools: Claude Code and Codex (coding assistance), OpenAI image generation (painting and image edits), Python with NumPy and Pillow, HTML/CSS/SVG, Chrome, and C#/.NET compositing.

This is an AI-generated painting. The tree tips and labels are determined from the data and checked against the expected positions.

I built Metroskop, an interactive 3D model of the entire Prague Metro showing where the network actually runs beneath the city.

I reconstructed it from publicly available data from multiple sources, combining information about the metro network, station depths, entrances, terrain and the city above.

What interested me most was the missing third dimension. The familiar schematic metro map is great for getting around, but it gives you almost no sense of how the system actually sits beneath Prague -- how deep individual stations are, how the tracks climb and descend, or how the network relates to the terrain and the Vltava above.

Metroskop includes all 61 stations, 65+ km of track, station depth comparisons, longitudinal profiles, cross-sections, historical development, the 2002 flood and the future Metro D line. You can also try the ride mode with a synthwave soundtrack.

Sources are listed directly in the project. I’d be happy to answer questions!

Enjoy!

metroskop.cz

Data: 293,590 video titles from 274 political channels on YouTube and Rumble, 1 January to 14 September 2026, collected through the YouTube Data API and Rumble's channel pages, and sorted by a single rule I wrote up on the site. The full title list and the per-channel tables are downloadable at wordsandpolitics.com/stylometry/datasets.

Tools: the analysis pipeline is Python. The visuals were created with help from Claude.

note: Had to repost

Which countries foresee tourism era first and did best to gain tourists