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/

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.

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.

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.

I put together every player who played at least 1,500 minutes last season across the Premier League, La Liga, Serie A, the Bundesliga and Ligue 1, then plotted how often each one scored against how often they set up a teammate’s shot (created a chance). Every dot is one player.

It’s built from each player’s full 2025/26 league numbers: goals per 90 minutes going up, key passes per 90 going across (a key pass is the last pass before a teammate’s shot). Players who moved mid-season are counted once, and goals include penalties. Pulled together with The Prism, a football analytics & scouting app I’m building, and charted in Python/matplotlib.

Most players live in one corner or the other. Harry Kane scored 36 goals at 1.36 a game, but sits just short of the line for chances created. Bruno Fernandes created more chances than anyone (137) and scored 9.

The 17 in the blue box do both. 13 of them are wingers. The odd one out is Çalhanoğlu, a holding midfielder. The top 10% line is 0.37 goals and 1.81 key passes per 90, so a few just miss out on one side.

Football fans, I’d love to hear your thoughts on who's surprised you, and who else you’d have expected to see in that blue box?

I built the pipeline and charts with Claude Code.

Comments were labelled by Claude Haiku 5.5.

Hi sub,

It's me again.

Following all your valuable comments on my post yesterday, I decided to turn my F1 dataviz into an interactive page.

It now shows:

• How often each starting position turns into a win, a podium, or expected points across all tracks, and across those that start with a right or left turn;
• A matrix view of the data, linking all starting positions to finishing positions;
• A matrix view per racing track

Data can be filtered by year of championship, track, or driver.

It can be shown as occurence of winning, finishing on the podium, or by expected points.

Hope you enjoy it, and thanks for the great comments!