China is often the poster child for rapid reductions in poverty, and for good reason. In the early 1980s, over 90% of its population lived in extreme poverty, but by the early 2020s, that number had dropped to nearly zero.

Some might assume that China is the only reason why global extreme poverty has declined. But that’s wrong: many countries have seen dramatic reductions in poverty, too. Indonesia is one clear example; it’s shown alongside China on the chart.

In 1984, 86% of Indonesians lived on less than $3 per day. By 2025, this had fallen to less than 4%. While it didn’t quite match China’s decline, it has still been impressive. The number of people living in extreme poverty has fallen from 140 million to about 10 million.

Note that the international poverty line is extremely low, defined as people living on less than $3 per day. But Indonesia has also made progress when measuring higher poverty lines.

Data source: World Bank Poverty and Inequality Platform (2026)

Tools used: Python, OWID Grapher, Figma

The US elections seem to have grabbed the focus of many, but the US is still at war with Iran and President Trump is giving small statements multiple times per week on the status of the war. Most of the time, though, it's no more than saying the war will end "soon."

I've been tracking all of his statements (both spoken and written) on the status of the war and any negotiations, and it became obvious that he was saying similar things as time passes. Even months apart.

For example, in March Trump said, "It's going to be finished pretty quickly." And then yesterday, October 14th he said "It's going to be over very quickly." That's nearly word-for-word of what he said during the start of the war.

So the bar chart is a plain look at how many times he's said "soon" or "shortly" or other words when describing the end of the war or the imminence of some deal or agreement.

Data Source: http://artofnodeal.com/ (direct link to the JS object storing all the quotes here)

Tools: Vanilla JS

It's almost always 7.

**This list does not include every partnership or ownership stake.

Youtube version | Full dataset

Every US location of the 20 fast-food chains with the most stores, put on a map.

Source: Collected from each chain's store locator with Kadoa in October 2026.

Tools: Python and matplotlib, ffmpeg for the GIF

Source: GTA 6 Standard Edition prices from the PlayStation Store as reported in each region ($79.99 / €79.99 / £69.99 / A$129.95 / CA$109.99 / ₹5,999). Minimum wages from government and payroll sources, as of October 2026: US Department of Labor, Canada.ca, UK National Living Wage, Fair Work Australia, and the EU national rates.

Tool: built with HTML/CSS and rendered with headless Chrome.

Method: hours = local Standard Edition price ÷ statutory hourly minimum wage for adults, before tax. Prices are before US/Canadian sales tax; European, UK and Australian prices include VAT/GST, so those markets are, if anything, slightly flattered.

A few things that surprised me:

- Australia has one of the highest sticker prices (A$129.95) and still comes 3rd, because its minimum wage (A$26.44) is the highest in the set.

- The US federal minimum hasn't changed since 2009, so the jump from GTA 5's $59.99 to GTA 6's $79.99 hits it in full: 8.3 hours then, 11.0 hours now.

- India is off the chart: 67.6 hours at Delhi's minimum wage and about 270 hours (roughly 34 working days) at the national floor wage.

Caveat: one source lists Australia at A$109.99 instead of A$129.95. At that price Australia would be 1st at 4.2 hours.

Full table, sources and a calculator to put in your own wage: https://leonidascout.com/gta-6-affordability-index/

Live tracker of 13 subreddits: https://ok-stand-2128.github.io/Live-Reddit-Tracking/index.html

Sources: Reddit json data pulled daily.

Methodology: See the live tracker's Methodology page. Source data can also be found there.

Visualizations: Plotly.

This image was made for this post and is not available on the tracker, but several other charts and visuals are.

This is a 2025 follow-up to u/eltokh7’s maps for 2022–23 and 2024 (original thread), in the same style.

Florida gained 22,517 people on net from other states in the year to July 2025, eighth among the states. North Carolina gained 84,064, Texas 67,299 and South Carolina 66,622. Per resident, Florida ranked 26th of the 50 states.

In 2022, Florida led every state, with 310,892. It was second in 2023, fourth in 2024 and eighth in 2025 (Census Bureau, Vintage 2025 series).

On the map, Florida still pulls heavily from New York (about 30,000 net), New Jersey (about 15,000) and California (about 11,000), but it sends an estimated 39,000 net on to Georgia and the Carolinas, plus smaller estimated flows to Tennessee and Texas.

California lost 229,077 people on net to other states, and New York lost 137,586.

State totals are official 2025 estimates; the corridors between states are estimated, since the Census Bureau’s 2025 state-to-state table isn’t out yet. Method, error ranges and full tables are in the starter comment.

Belgium has very complex political system, which makes predicting elections incredibly difficult. That's why I have started a project called 1830 with the aim of giving people a more complete and detailed look of the Belgian political landscape.

The map lets you explore the projected vote shares geographically, select individual parties, compare projections with the official 2024 election results, and identify municipalities where the projected largest party changes.

We feed our model by combining polling data with other fundamentals; such as PM popularity, unemployment rate, inflation, purchasing power index, etc...

The projected vote share on the municipal level is the most detailed forecast ever released and something we've been working on for a couple of months.

One important caveat: these are not predictions based on polling conducted in each municipality. The model estimates voting intentions at the regional level (Flanders, Wallonia and Brussels). Those estimates are then projected onto current municipal boundaries using the 2024 federal election results as the local geographic baseline.

In other words, the map combines a regional forecast with the existing geographic distribution of votes. Local voting patterns are used to distribute the projected regional changes, rather than being independently forecast for every municipality.

Hope you enjoy it!

Interactive map: https://1830belgium.com/Prognose.html

Data sources:

• Official election results: Belgian federal election of 9 June 2024, municipal-level results published by the Belgian Federal Public Service Home Affairs (FOD Binnenlandse Zaken / IBZ).
• Polling data: Ipsos polls published by Le Soir, RTL TVI, Het Laatste Nieuws and VTM; and polling by ULB and the University of Antwerp, published through VRT, De Standaard and RTBF.
• Historical polling data: Historical Belgian federal polling series, including data compiled in the project's historisch_2024.json file.
• Geographic data: Current Belgian municipal boundaries for 2025/2026, with a mapping that consolidates the 2024 results of municipalities that have since merged.

Tools used:

Python, NumPy and SciPy for the regional forecasting model. HTML, CSS, JavaScript and SVG for the interactive map. JSON and geographic data files for storing and processing the election results and projections.

Methodology:

The forecasting model combines historical election results, polling data, pollster-specific adjustments, time-weighted trends and additional political and economic factors. The relative weight assigned to polling and these fundamentals changes as the next election approaches.

To create the map, official 2024 municipal results are first consolidated into the current municipal boundaries. The model's regional vote-share projections are then distributed geographically using each municipality's 2024 voting patterns as the baseline. The projected shares are normalized, with votes for parties not explicitly modelled retained in an “Other” category.

The map therefore represents a spatial projection of regional estimates, not independent polling or a separately calibrated forecast for every municipality.

Interactive visualization: https://1830belgium.com/Prognose.html

Project: 1830 Belgium — https://1830belgium.com

Where do college hockey players come from, and how far from home do they play?

Explore the interactive Player Atlas

I built the Player Atlas to explore the hometowns of men’s and women’s college hockey players. You can compare states, provinces, and countries by player count or relative to population, explore hometown clusters, and see a team’s recruiting footprint through connections between roster hometowns and campus. Filters let you explore by season, conference, program, and position.

The Atlas is part of a larger project called College Hockey Data. Besides the Atlas last the site has eleven more studies, each with their own interactive page exploring every aspect of NCAA hockey

A few data notes: Hometowns are the locations reported on team rosters, not verified birthplaces. Coverage varies by season, and women’s roster coverage currently begins in 2026–27. When viewing multiple seasons, a player is counted once for each season they appear.

Data: OpenAlex snapshot of 2026-06-26 (CC0).

Tools: Python, DuckDB, MapLibre and deck.gl. Interactive version, no sign-in: https://scienceexplorer.org

Method and limits: https://scienceexplorer.org/methodology

Happy to answer any questions

Data: road distances and drive times from OpenStreetMap, routed between each park's main visitor center. The stops are every national park in the lower 48 on the National Park Service list, which is 51 today. For Channel Islands, Dry Tortugas, and Isle Royale, the drive ends where you catch the boat.

Tools: Python for the routing and the solver, and JavaScript and SVG for the map.

Each leg is shaded by its driving time. The western parks sit close together, so most legs out there are short. The 5 longest legs are connectors between regions, and together they take about 65 of the 270 hours:

• Cuyahoga Valley back to Acadia, 14.4 hours
• Acadia to Shenandoah, 13.8 hours
• Biscayne to Great Smoky Mountains, 13.7 hours
• Hot Springs to Big Bend, 12.5 hours
• Congaree to Everglades, 10.4 hours

The visit order is proven fastest for these 51 stops and the modeled drive times. It's a loop, so you can start anywhere and the total stays the same.

Visit order:

1. Acadia National Park, ME
2. Shenandoah National Park, VA
3. New River Gorge National Park and Preserve, WV
4. Congaree National Park, SC
5. Everglades National Park, FL
6. Dry Tortugas National Park, FL
7. Biscayne National Park, FL
8. Great Smoky Mountains National Park, TN
9. Mammoth Cave National Park, KY
10. Gateway Arch National Park, MO
11. Hot Springs National Park, AR
12. Big Bend National Park, TX
13. Carlsbad Caverns National Park, NM
14. Guadalupe Mountains National Park, TX
15. White Sands National Park, NM
16. Saguaro National Park, AZ
17. Grand Canyon National Park, AZ
18. Petrified Forest National Park, AZ
19. Mesa Verde National Park, CO
20. Great Sand Dunes National Park and Preserve, CO
21. Black Canyon of the Gunnison National Park, CO
22. Canyonlands National Park, UT
23. Arches National Park, UT
24. Capitol Reef National Park, UT
25. Bryce Canyon National Park, UT
26. Great Basin National Park, NV
27. Zion National Park, UT
28. Death Valley National Park, CA
29. Joshua Tree National Park, CA
30. Channel Islands National Park, CA
31. Sequoia National Park, CA
32. Kings Canyon National Park, CA
33. Yosemite National Park, CA
34. Pinnacles National Park, CA
35. Lassen Volcanic National Park, CA
36. Redwood National and State Parks, CA
37. Crater Lake National Park, OR
38. Mount Rainier National Park, WA
39. Olympic National Park, WA
40. North Cascades National Park, WA
41. Glacier National Park, MT
42. Yellowstone National Park, WY
43. Grand Teton National Park, WY
44. Rocky Mountain National Park, CO
45. Wind Cave National Park, SD
46. Badlands National Park, SD
47. Theodore Roosevelt National Park, ND
48. Voyageurs National Park, MN
49. Isle Royale National Park, MI
50. Indiana Dunes National Park, IN
51. Cuyahoga Valley National Park, OH

Full route with season and access notes for each park: https://optimalroadtrips.com/trips/national-parks-2026/

Pick your own set of parks and get their fastest order: https://optimalroadtrips.com/build/

A 2-D model learned the rules of German der/die/das from spelling alone. Each ending is a representation, i.e. a "constellation" of nouns, and the exceptions shown inside it**.**

Data: German Wiktionary via the german-nouns package (CC BY-SA 4.0). The map shows 85,146 nouns with a single gender. The rule statistics use 81,720 ordinary nouns, after removing names, places, abbreviations and nouns with more than one article (der/die See).

Tools: Python, pip install event2vector, pandas, scikit-learn and matplotlib.

Approach:

• Every letter is an event, and a word is the exact sum of its letter vectors in 2-D.
  • The coordinates are the model itself, so we use no dim. reduction (PCA, UMAP...)
• A plain sum ignores letter order, so the last four letters are tagged by their position from the end.
  • e.g. The n in -chen is a different event from an n in the middle of a word.
• The model worked with 19,200 nouns (balanced across der/die/das), predicting each next letter and, at the end, the article. It was never given a rule.
• The three zones come from a straight-line classifier on those two numbers. It gets 64 % of unseen nouns right; chance is 33 %.

Rule mining: I mined 619 endings that are at least 90 % one gender (30+ nouns each, and each more informative than any shorter ending). All together they cover 71% of nouns. The video shows eight of them. The closing scorecard covers all 619: the model places 90 % of die-rule nouns in the die zone, 69 % for das and 53 % for der. Masculine endings are the hardest to fit into 2-D.

Exceptions: der Schwung and der Sprung end in -ung but aren't the -ung suffix (they come from schwingen and springen). The model puts them in the die zone, just as a learner would. The same goes for der Kuchen (-chen), der Irrtum (-tum), der Zombie (-ie) and das Dinner (-ner).

Issues: The model sees spelling only, never meaning, so it can't know that Zombie is a person. Furthermore, wiktionary has errors. For example, 21 entries are tagged feminine but take a genitive -s, which feminine nouns can't.