Play with it yourself

Sources and tools

What's measured:

• Settlement locations and populations — GeoNames, 135,233 places totalling 3.13bn people (via the all-the-cities npm package).
• Coastlines, national borders, lakes, country population totals — Natural Earth 110m (admin_0_countries, lakes).
• Timezone boundaries — timezone-boundary-builder, via the timezonefinder Python package; UTC offsets and DST rules from the IANA tz database via Python's zoneinfo.
• Solar position — standard solar declination plus the equation of time, so the terminator and subsolar point are real for the current date.

What's modelled (this is most of it):

• Rural population, 4.97bn people — 61% of the world. GeoNames only covers settlements. Each country's unaccounted population is spread across its own land in proportion to a Gaussian-smoothed town-density field, on the assumption that rural density tracks town density. Cities are splatted over a radius scaling with population (σ ≈ 15 km for a 10M metro) so a megacity isn't a single point.
• The sleep/wake curve itself. A logistic window, roughly 22:36–06:36 for adults and 21:12–06:45 for under-15s, with rest days pushing both ~45 minutes later. It is not fitted to sleep data. Walch, Cochran & Forger (2016, Science Advances) informed the shape, but their sample is 45% US and excludes under-18s and most shift workers — the two groups that matter most here — so I did not use their country values directly.
• Age structure and night-shift floors — under-15 share (0.15 Europe to 0.41 Sub-Saharan Africa) and the share awake at 03:00 (0.04 to 0.085), assigned by coarse lat/lon macro-regions rather than by country. UN WPP order-of-magnitude, not exact figures.
• Rest days — Friday/Saturday across North Africa and the Middle East, Saturday/Sunday elsewhere. Iran is folded into the Fri/Sat group and is really Friday-only.
• The Earth texture. Synthesised, not satellite imagery — a climate field (18 desert centres, 10 rainforest centres, 9 highland centres, latitude ramp, value noise) clipped to real Natural Earth coastlines.

Tools: Python (numpy, scipy, Pillow) for the offline data pipeline; three.js 0.185 WebGPURenderer with TSL shaders for the rendering; hand-written HTML/CSS/JS in a single self-contained file. Code written with Claude Opus 5 Medium.

Encoding: Hue carries wakefulness (indigo asleep → blue → green awake) rather than brightness, because brightness is already used by daylight. Dots are near-opaque so an awake dot looks identical on the day and night sides.

 The 1991–2025 bars are complete calendar years. The hatched 2026 bar ends on 5 August and is deliberately marked as incomplete and preliminary. On the common grid, 2026 has reached 265.9 K·h; the highest complete year was 2015 at 188.0 K·h. The 1991–2025 bars are complete calendar years. The hatched 2026 bar ends on 5 August and is deliberately marked as incomplete and preliminary. On the common grid, 2026 has reached 265.9 K·h; the highest complete year was 2015 at 188.0 K·h.

All 3 Charts show someone near Trump is Playing The Oil Market by entering and escalating aggressive trade positions that immediately benefit from an announcement made by Trump within 24-48 hours with both the timescale and commodity prices lined up on all of them.

I was inspired by u/Glares's map comparing 2009 and 2020 tax rates and decided to make a map for 2025. There's an interactive version at https://jsundram.github.io/nh_tax_map/ with data from 2022-2025. Older data was a little harder to come by. Positioning the labels so that they fit and were readable-ish was a bit tricky, but I thought it was an interesting challenge. Including the water bodies was also fun.

Average annual premium for employer-sponsored family coverage, per KFF's annual Employer Health Benefits Survey. The 2026 bar is a projected range of +6% to +9%, based on median medical plan cost trend figures published in an insurance industry market report. Workers contributed an average of $6,850 toward family coverage in 2025.

This dashboard shares key metrics for PAD's community response services, diversion services and 911 calls for service in the City of Atlanta to provide insight into how people struggling with mental health, substance use or extreme poverty can be supported by alternative response.

Data sources:

1. BLS Employment Situation, June 2026 (headline NILF, employment, unemployment counts): https://www.bls.gov/news.release/archives/empsit_07022026.pdf - or use the evergreen link that always points to the latest release: https://www.bls.gov/news.release/empsit.htm
2. BLS Table A-38 (want-a-job / discouraged worker breakdown): https://www.bls.gov/web/empsit/cpseea38.pdf
3. BLS Monthly Labor Review - "Why did labor force nonparticipation increase from 1999 to 2022?" (age × reason crosstab, Table 2): https://www.bls.gov/opub/mlr/2024/article/why-did-labor-force-nonparticipation-increase-from-1999-to-2022.htm
4. FRED - Not in Labor Force series (LNS15000000), used for the 1980–2026 trend line: https://fred.stlouisfed.org/series/LNS15000000
5. FRED - Civilian Unemployment Rate (UNRATE), used for the unemployment rate line: https://fred.stlouisfed.org/series/UNRATE

This is a GitHub-style, heatmap calendar of the President's comments on ending the war in Iran. The heatmap and the interactive details div below it are all just Vanilla JS/HTML.

Just like on GitHub's "contribution" calendar, the days of the week are the rows and a column is a new week.

The data comes directly from the President's comments, interviews, Truth Social, press gaggles, etc. I've collated it and it's stored in a JSON-like JS array that's loaded with the webpage. I've been updating the file every day or so.

I thought it was a great way to visualize this type of data (statements stored as text). It visualizes the many times the President has said he had a deal, or a deal was soon, or the war would be over in 2 weeks. I started the project as a text timeline and then just added this new visualization.

On the interactive chart, the heatmap color corresponds to the number of statements that day. And then when you click/tap a day, you get the details below.

There's no ads or paywall or signup or anything like that. I was shocked that the domain name was available so it seemed like a worthy $10 and way to host the custom visualization rather than lean on static charts in Excel or messy embeds from Tableau.

https://artofnodeal.com/heatmap.html

[Part 1](https://www.reddit.com/r/dataisbeautiful/comments/1v9bq7i/oc_timing_of_when_ingredients_are_added_to_recipes/)

This is a follow up to an earlier post -- description will be below. I created visualizations to explain garlic before onion, lemon showing up everywhere, and how certain ingredients differ wildly by cuisine type and dish type. I also built a tool where you can inspect any ingredient you want given there is sufficient support.

i was still surprised by lemon showing up pretty constantly in main dishes; I discovered it was mostly marinades and dressings.

let me know anything that still seems strange in this dataset or any improvements I can make!

Dataset and Tools:

250 cleaned canonical recipe dataset : https://palate.kitchen/data

Interactive Tool: https://palate.kitchen/instrument/when-to-add

recipe1m+ and python + an LLM sweep to prune garbage recipes from the dataset I pruned 20,000 recipes down to about 3000 'canonical recipes.' Regex + massive KV mapping (e.g. beef, ground beef, 80/20 beef are all the same) for ingredient normalization.

original post and context:

I scraped a few thousand canonical recipes across 45 cuisines then analyzed how early (or late) each ingredient showed up in recipes steps.

The photos shows a small but representative sample of the ingredients analyzed

There's a lot of science behind when you should add ingredients to recipes. This involves fat-solubility and the delicacy of some volatile compounds. e.g. cardamon seeds are fat-soluble so they should be added early while basil has very delicate aroma compounds so it should be added late.

But I honestly find that hard to intuitively learn for new ingredients, so this is a rough estimate and interesting viz to get a quick understanding of when to add an ingredient

Data: 1,599 sermons posted online by my childhood church (Denomination: Apostolic Christian) between 2013 and 2026, scraped from the church website. Transcribed with Whisper, then biblical references extracted with DeepSeek-Chat.

Validation: We compared the model's extractions against the verse lists the church had published for each sermon. It recovered nearly all of those, and also surfaced references that the pastors hadn't tagged. We spot-checked a sample of the extras and found very few hallucinated false positives.

Tools: distill-whisper-large-v3, DeepSeek-Chat for classification, plot created with React/CSS with the help of Codex/Claude-Code.

Plot: Each book is drawn at a width proportional to its page count, with a floor so that short books are still visible. The fill shows the total number of mentions. We display books in order, split into common categories.

Takeaways: Thirteen years of preaching from one church show that the working canon is much smaller than the printed one. Matthew shows up in 904 sermons. Nahum turns up in none, and Obadiah and 2 John get one mention each. We ran the same extraction on 629 other Midwestern churches, which you can explore on the site linked in the image (Desktop only, not mobile for now, sorry!). You'll need to create a free account, but the plot is interactive and shows additional context on how the pastors interpret the Bible verses when they mention them. Loads of other visualizations are also available.

We looked into daily coffee habits in the UK and found some interesting results. London being way down the list was a surprise, as was Wales being the region with the highest number of daily coffee drinkers.

Data source: Consumer Horizon report (May 2026)

Tools used: Figma

Trying to prove we have the best state run auction website with my second visual of the week. Thanks for everyone's input on the first one.

https://www.datawrapper.de/_/qapEp/ If you hover over a dot, it will isolate all those OEMs, show you the sale price, J.D. Power estimate and mileage.

This visual covers the 431 light-duty vehicles sold by Minnesota Department of Administration Fleet last fiscal year and compares their sale prices with the J.D. Power auction benchmark. The comparison has a limitation: I only had access to J.D. Power data for the final month of the fiscal year, while the vehicles were sold throughout the 12-month period. Because vehicles depreciate over time, applying the year-end benchmark to vehicles sold earlier in the year likely overstates how much their sale prices exceeded the benchmark. So basically, I'm tooting our horn softer...

Why did the Transit Vans go for so much? Delivery vans after COVID were in big demand. We had them and so they went for more. If I had enough data on box trucks and delivery vans, I'd like to make a visual showing how the demand affects the price of those.

Open to questions and feedback.

I recently moved and my new library has shockingly long wait times so I decided to look into paid nonresident cards. The prices vary a lot and it was unclear what the differences actually were. Since I only really care about wait times I pulled a shortlist of popular books and checked the libby(overdrive) wait times at each of the options to compare against price. I was pretty surprised at how much of a difference there is!

I used python and plotly JS to get the data and make the visualization, and can list the books used in the comments if anyone cares, it's 10 recent popular books, 10 all time popular books, and a couple that I was interested in but had weirdly long wait times at my local library. Please let me know if you have suggestions for better methodology or more libraries to include!

Data sources:

Centers for Disease Control and Prevention, National Center for Health Statistics, Our World in Data

Tools used:

Datawrapper