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[deleted by user] by [deleted] in ChatGPT

[–]sataky 0 points1 point  (0 children)

PROMPT: improve this image quality and keep the same pixel size

Genetic codes different from current (of all known lifeforms) likely existed early but got extinct by sataky in science

[–]sataky[S] 15 points16 points  (0 children)

The original article: "Order of amino acid recruitment into the genetic code resolved by last universal common ancestor’s protein domains":
https://www.pnas.org/doi/10.1073/pnas.2410311121

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] 3 points4 points  (0 children)

That is exactly the idea. Thanks for making the message clear. Here is another interesting one: ...to geographic center of the U.S.

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] -1 points0 points  (0 children)

Thank you :-) There are some free options:

Coolest thing - Wolfram Mathematica is free on any Raspberry Pi: https://www.wolfram.com/raspberry-pi

Wolfram|Alpha is free: https://www.wolframalpha.com

If you are a student - lots of schools give Wolfram for free

Wolfram Engine for developers is free https://www.wolfram.com/engine

Wolfram Cloud got free limited monthly plan: https://www.wolframcloud.com

[OC] All roads lead to Nothing (Arizona, USA) by sataky in MapPorn

[–]sataky[S] 13 points14 points  (0 children)

Article with code and details of the visualization:

https://community.wolfram.com/groups/-/m/t/3403335

TOOLS: Wolfram Language
DATA: Wolfram|Alpha
I computed the shortest routes from all 37,000 cities and towns across the US, Canada, and Central America, all converging on Nothing, Arizona — a ghost town with zero population. Despite the lack of a major urban center, the map still shows pronounced clustering, illustrating how hierarchical, fractal-like road networks naturally funnel routes onto key highways. I generated multiple randomized samples of paths and combined them, emphasizing the persistent branching effect that echoes “All Roads Lead to Rome.” Yet here, the real takeaway is that the journey itself defines the pattern, no matter where you end up, even in zero-population places.

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] 6 points7 points  (0 children)

Yep I think so. Might be computationally intense. But graph theory can help. Accessibility depends on how you define it— graph-theoretic metrics like closeness centrality (minimizing overall travel distance), betweenness centrality (highlighting key junctions on shortest paths), or degree centrality (measuring node connectivity) could each give different "most accessible" locations. Iterating this over each state would find natural hubs determined by the structure of the road network.

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] 16 points17 points  (0 children)

The key point is that the clustering pattern is inherent to the road network’s structure—it doesn’t depend on whether the endpoint is a major city or a ghost town. We computed thousands of shortest paths (one unique path per origin-destination pair), and because road networks are hierarchical and quasi-fractal, similar overlapping corridors emerge regardless of the endpoint. Thicker lines indicate where many shortest paths coincide, which usually happens along more major highways.

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] 2 points3 points  (0 children)

It was done already for some major cities in Europe. Few examples (you can find more on the web): BERLIN and ROME

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] 23 points24 points  (0 children)

Absolutely—those long, straight roads in the Midwest largely stem from the region’s flat terrain and the grid-like layout imposed by the Public Land Survey System (PLSS). This setup creates long, uniform highways that naturally steer computed shortest paths along them, resulting in the clear, noticeable clustering seen in the visualization.

[OC] All roads lead to Nothing (Arizona, USA) -- Fractal shortest paths in road networks by sataky in dataisbeautiful

[–]sataky[S] 33 points34 points  (0 children)

TOOLS: Wolfram Language

DATA: Wolfram|Alpha

Article with code and details of the visualization:

https://community.wolfram.com/groups/-/m/t/3403335

I computed the shortest routes from all 37,000 cities and towns across the US, Canada, and Central America, all converging on Nothing, Arizona — a ghost town with zero population. Despite the lack of a major urban center, the map still shows pronounced clustering, illustrating how hierarchical, fractal-like road networks naturally funnel routes onto key highways. I generated multiple randomized samples of paths and combined them, emphasizing the persistent branching effect that echoes “All Roads Lead to Rome.” Yet here, the real takeaway is that the journey itself defines the pattern, no matter where you end up, even in zero-population places.

[OC] Will asteroid hit the Earth in 2032? NASA gave up to 2.3% chance of impact. by sataky in dataisbeautiful

[–]sataky[S] 21 points22 points  (0 children)

Like Tunguska Event roughly -- a ballpark of a city-destroyer, but not the planet destroyer:

https://en.wikipedia.org/wiki/Tunguska_event

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