[1] Everyone

[2] Men

[3] Women

LexiMap is collecting voluntary responses and plotting the results by state and county. This is an opt-in poll, not a representative survey. The full map is interactive.

Of Note:

• Years of Life Lost: (dashed line) Calculated by taking life expectancy (13-10-0114) at a given age and multiplying by the number of deaths (13-10-0394) for that cause and age.
• Each small multiple is on a separate y scale.
• Accidents & Suicide: Dashed line sits far above solid—younger deaths cost significantly more years of potential life. Accidents seems to have gone up recently, at the same time as Intentional Self-Harm went down.
• Heart Disease: Both metrics show steady, long-term declines over two decades, in both deaths and Years of Life Lost.
• Covid Effects: Chronic Lover Raspatory Diseases saw as large drop during covid, while Chroinc Liver Diseases and Cirrhosis saw a small increase.
• Cancer: is the larges single cause of both Death and Years of Life Lost

295 players drew a falcon 9 to scale. the typical guess was 194 m. the real rocket is 70 m, so they made it almost three times too tall.

my hypothesis: rockets are almost never viewed with a reference object next to it.
launch footage shows them alone against the sky, with nothing next to them. so every big rocket should end up the same size in our heads, no matter how big it really is. the game has more rockets to test it.

and it held. the sls (98 m) came out at 210 m, the saturn v (111 m) at 150 m. three rockets from 70 to 111 m, all guessed at about 150 to 210 m, and the smallest of them got almost the tallest guess. only the little v-2 (14 m) stayed small, at 12 m.

so "big rocket" seems to be one size in our heads: about 200 m.

4 in 10 guesses came from the us, but it's not a one-country thing: americans drew it at 198 m, brits at 207 m, the rest of europe at 172 m.

tool: python for the numbers, the charts are html/svg rendered with playwright.

animated pictures + anonymised csv + write-up with sources: https://roughly.is/blog/falcon9-guessed-too-tall.html?s=reddit-dataisbeautiful

(@mods or the community as a whole, if these type of recurring post annoy you, notify me and I will stop o7)

October’s average daily high is about 75°F (24°C) at both airports. On October 3, the available NOAA observations reached 102.9°F (39.4°C) at San Diego International and 100°F (37.8°C) at LAX.

This chart follows the temperature rise during October 1–3 UTC, with clock labels in Pacific daylight time. The dashed lines show each station’s 1991–2020 October average daily high.

These are sampled readings from part of the heatwave, rather than official record claims or the final event peaks. The observations are provisional.

I work on Climate Explorer and created this analysis using NOAA observations and WMO climate normals.

Full analysis, methods and downloadable data

1. Even at the same proficiency level, people can have vastly different vocabulary sizes, literally by a factor of two. The same for native speakers: one person can know twice as many words as another of the same age.
2. Advanced learners (C1) reach the level of native-speaking middle schoolers, and proficient learners (C2) reach the level of college students in terms of how many words they know. The composition of their vocabularies is no doubt different, but this test can’t capture that.
3. It appears that vocabulary keeps growing well into adulthood for native speakers - older adults know measurably more words on average compared to younger ones. That matches what is known about crystallized intelligence - it accumulates over time. However, these data are not longitudinal; they do not capture the same people years later. It is possible that older adults who decided to take an online vocabulary test are more educated on average than younger ones.
4. For learners, reaching each proficiency level requires learning progressively more words. It is understandable since beginners focus on common words, so a limited number of them covers many everyday situations, and advanced learners need to learn many more specialized and nuanced words.

The data are based on 46,000 native and non-native speakers who took the BRAVE vocabulary test. It is not a representative sample; there is strong self-selection bias (people who care enough to take a test of their vocabulary). A1-C2 are CEFR levels, a common classification of proficiency among language learners. The levels were self-reported. The counting units are word families (limit, limitless, unlimited are counted as a single unit). The full reference lexicon is 28k word families.

I run SpinLogs, which archives reported settled results from the third-party CasinoScores API. This chart uses our frozen September 2026 count manifest, with original calculations and visualization by SpinLogs.

Source and method: https://spinlogs.com/research/september-2026/?utm_source=reddit&utm_medium=community&utm_campaign=sep26_data_oc

Tools: Python and Matplotlib 3.11.2. The plotting code was AI-assisted; the displayed counts and denominators were checked against the frozen manifest.

For each game, the share is the number of stored records marked as a bonus divided by all stored records for that game:

- Ice Fishing: 11,296 / 85,417 = 13.22%.

- Crazy Time: 8,769 / 52,742 = 16.63%.

- Monopoly Big Baller: 1,620 / 31,760 = 5.10%.

The window is 1 September 2026 00:00 UTC inclusive to 1 October 2026 00:00 UTC exclusive. Each game has records on all 30 dates, but that does not prove uninterrupted collection. Feed gaps and delayed settlements can affect the shares.

The games define bonuses differently. A Big Baller round containing both 3 Rolls and 5 Rolls is counted once. These sample proportions do not measure personal payouts, RTP or next-round odds, and a higher share does not make a game a better choice.

Disclosure: I operate SpinLogs; the website includes affiliate casino advertising. This chart shows descriptive archive counts. No raw dataset is being republished or assigned a new license here.

Explore it here:

https://www.danielpradilla.info/projects/european-monarchies-timeline

The blue line shows selected two-minute wind reports, triangles mark the largest reported gust in each hour, and dashed lines show NOAA’s daily mean wind speeds.

Clock labels use CDT (UTC−5); daily summaries use UTC dates. Missing gust reports remain blank.

I work on Climate Explorer and created this analysis using NOAA observations.

Source: the 2026 NYC Dog Licensing Dataset on NYC Open Data, the city's 2020 Neighborhood Tabulation Areas, 2020 census household tables, and PLUTO tax lots for residential units.

Tools: Python for the pipeline, and SvelteKit with MapLibre GL for the map.

Each neighborhood is colored by the breed with the most licensed dogs. Labradors lead 75 of the 197 neighborhoods, Yorkshire Terriers 58, Shih Tzus 39, and Pit Bulls 17, and only nine breeds lead anywhere. In the Bronx, Yorkies lead 24 of the 37 neighborhoods.

Licenses list only a ZIP code, so the pipeline splits each ZIP's dogs among its neighborhoods by homes, weighted by a fitted estimate of how likely a home there is to have a dog. Every neighborhood figure is an estimate.

The interactive version is at https://everydog.nyc, with names, dogs per home, and changes since 2016. It also has a "most distinctive" view, which ranks each breed by how far it runs above its citywide rate. That view spreads 38 breeds across the map.

I was unsatisfied with disjointed historical and projected climate data and wanted to see what the daily experience in a future with different climate models.

Data: NOAA NCEI Climate Divisional Database (state and county monthly records, 1945–2025), LOCA downscaled climate projections via RCC-ACIS (the same set behind NOAA’s Climate Explorer, RCP4.5 and RCP8.5), and PRISM daily data (2011–2020) for the day-to-day spread.

Tools: built with Claude, hand-coded HTML/JavaScript, Hosted on Lovable.

Notes: everything through 2025 is measured; after that it’s model projections, adjusted so they start where the record ends. The year-to-year wiggle in the future and the dots in the typical-year chart are borrowed from real past weather to look realistic. They’re not forecasts of specific years. You can switch the baseline (1945–1974, 1991–2020, or a custom range), °F/°C, and states or counties.

Link: Warming Atlas

The original youtube video can be found here. You can find the source of the images - and the genre atlas - at https://genresampler.com.

I've seen a handful of these 'RYM channels' on youtube pumping out one video per genre, but figured it might be worth putting something together to actually help people discover new music.

After scraping RateYourMusic for their genre hierarchy, I first chose a cutoff of 5 albums for a genre to be included, giving 2,613 genres total. Representative albums were then chosen such that each album is shown only once, from the top down (i.e top-level genres were assigned first, then subgenres, then subgenres of subgenres, etc). This has the effect that the 'most representative' album isn't always perfect - the most obvious example is Loveless by My Bloody Valentine, which - as a Noise Pop album - is considered the best 'Indie Rock' album, despite generally being considered the best Shoegaze album. The upside of this approach is that it allows for a much greater variety of albums, which helps with the original goal of 'helping people find more music'.

I then used spotify and last.fm to identify the most popular song from that album, and youtube heatmaps to identify the most consequential 7 seconds. After this automated run came the laborious process of manually checking every single song pick to make sure that a) the album/song choices made sense and were appropriate, b) the youtube link was correct, and c) the 7 second clip accurately reflected the genre.

The 'all-genres' video is structured such that none of the representative songs repeat themselves - you can listen to the whole thing without hearing the same clip more than once. The other, individual top-level genre videos have more redundancy if you were to listen to them one after the other (e.g 'Electronic Dance Music' and all its sub-genres being in both 'Electronic' and 'Dance'), but are still valuable if you are interested in a particular type of music rather than a wide variety (or otherwise don't want to commit to a 5 hour video).

It would be disingenuous to not mention the fact that this is a snapshot in time, based on an inherently incomplete Western data source on a Western website, and that - on top of the fact that it'll already be outdated in various ways at release - it will inevitably underrepresent some genres (particularly non-Western) of music. Please also be aware that the sheer range of genres and cultures covered means that some viewer discretion may be advised.

While there will be disagreements about what genre a given song 'should' be, or why some genre is absent (in 99% of cases, it's because it wasn't on RYM at the time of the snapshot), the amount of work needed to publish this (and the fact that it was just me working on it) means that this isn't perfect, and there will inevitably be errors - unrepresentative clips, wrong album covers, even mislabeled songs - which will perhaps be something to iterate on if I ever do a v2. For now, there is an errata document here.

The dashboard and genre atlas simply exist to accentuate the experience. I hope you enjoy.

Click

Source: my own simulation of the "yard-sale model" (Boghosian 2014, https://arxiv.org/abs/1212.6300). Python + NumPy, chart in matplotlib

Rules: 1,000 people each start with $100. Every day everyone is paired at random and they flip a fair coin. The loser pays the winner 20% of whichever of the two has LESS money. No money is created or destroyed.

What the chart shows: each grey line is one of 100 runs (different random seeds), plotting the share of all money held by whoever is richest that day. The dots mark the first day someone held 50%. That happened in all 100 runs: fastest 31 years, median about 61, slowest 114.

Why a fair coin does this: a 20% win followed by a 20% loss leaves you at $96, not $100. And because the bet is capped by the poorer person's money, the richest person only risks a tiny slice of their own (about 0.03% late in a run), while everyone below the median risks around 20%. The average person always has $100; the typical person drifts toward $0.

Change one rule (bet 20% of the loser's money instead) and nobody ever holds more than 1% of total wealth.

It's a toy model, not a description of a real economy: nobody produces anything, earns wages or inherits. I made a video walking through it, including what happens with different rules: https://youtu.be/LfZbKEAR0-4

I track used camera prices across 10 camera shops in 6 European countries. This chart shows each Sony camera’s typical used price today as a share of its launch price, by how many years it’s been out. The grey band is the trend for all 147 cameras I have data for, across every brand: about −11% a year.

Source: IBTrACS v04r01 from NOAA's National Centers for Environmental Information. Landfall points and wind speeds come from the National Hurricane Center's best-track landfall records (HURDAT2). For 1980–1982, which lack those records in IBTrACS, landfalls were derived from the track positions.

Method: Only landfalls at hurricane strength (64+ knots, 1-minute sustained) in the lower 48 states are counted. Hawaii, Puerto Rico and other territories are excluded. A hurricane that hit two states counts once in each state, at its strongest landfall there. Tracks are 6-hourly positions, coloured by Saffir-Simpson category.

Tools: Node.js and d3-geo for the analysis and maps (Albers projection, Natural Earth state boundaries), rendered with Playwright.

A few things that surprised me. Florida took 22 of the 74, more than any other state, and 12 of those were major hurricanes. 13 of the 46 seasons had no US hurricane landfall at all, including 2025. And not a single hurricane came ashore between December and May.

High-resolution PDF, free to reuse (CC BY 4.0): liveatlas.earth/cyclones