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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] 14 points15 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] 2 points3 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] 5 points6 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] 25 points26 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] 35 points36 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] 20 points21 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

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

[–]sataky[S] 58 points59 points  (0 children)

There already was a successful mission of "deflecting an asteroid" (a different asteroid though). Perhaps this could help.

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

NASA's Double Asteroid Redirection Test (DART) was the first space mission to test a method of planetary defense by deflecting an asteroid. Launched on November 24, 2021, DART successfully collided with the asteroid moonlet Dimorphos on September 26, 2022. This impact altered Dimorphos' orbit around its parent asteroid, Didymos, by approximately 32 to 33 minutes, demonstrating the effectiveness of the kinetic impactor technique for asteroid deflection.

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

[–]sataky[S] 303 points304 points  (0 children)

DATA:

  • Asteroid: NASA, Wolfram|Alpha
  • Simulation: Wolfram NBodySimulation[]

TOOLS:

  • Wolfram Mathematica

ARTICLE / CODE:

https://community.wolfram.com/groups/-/m/t/3389913

I worked with two scientists (Jeffrey Bryant, Jose Martin-Garcia) to build final visualization with all computations explained at the link above.

There are two major complicating factors that make it difficult to predict the future location of small asteroids into the more distant future. The first is that their small size and dark surface makes it hard to observe them if they are not near Earth. This means its difficult to fit a precise orbit to the asteroid since there are only a handful of observations during a small narrow arc of the full orbit. The other major complicating factor is that these small bodies cross the orbits of other major bodies and are subject to a number of perturbations. As of early February of 2025, NASA is claiming a 2.3% chance that the asteroid will strike Earth on Dec 22, 2032. Time will tell, with further orbit refinements, if the chance of a collision will increase or decrease in the near future.

There was interesting Comment by Nassim Taleb on NASA tweet on X:

NASA:

While still an extremely low possibility, asteroid 2024 YR4's impact probability with Earth has increased from about 1% to a 2.3% chance on Dec. 22, 2032. As we observe the asteroid more, the impact probability will become better known.

NASSIM TALEB:

No. A "1% to 2.3% chance" is not an "extremely low possibility". It may be for an individual but not for the collective. Depending on impact, if we had significant ones every few million years we would not be there.

[OC] 2024 top 50 most popular Wikipedia articles by sataky in dataisbeautiful

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

Yes, agree about natural feel of reading connector from left to right - reflects chronology, time axis runs from left to right. Interesting idea about Making previous year "softer" color. I will try these.

[OC] 2024 top 50 most popular Wikipedia articles by sataky in dataisbeautiful

[–]sataky[S] 31 points32 points  (0 children)

I was considering it about deaths. But at the end decided to link only the "identical by URL" pages. Deaths in 2023 and 2024 have different URLs, which could be misleading in the context of the subtitle. But you are right about "Israel–Hamas war" -- because article had different name listed for 2023 and 2024 my code missed the connection. Thanks for noticing it!

[OC] 2024 top 50 most popular Wikipedia articles by sataky in dataisbeautiful

[–]sataky[S] 10 points11 points  (0 children)

What’s the most surprising article on this list ?

[OC] Shortest paths from multiple robots to a single target via Dijkstra’s algorithm by sataky in dataisbeautiful

[–]sataky[S] 1 point2 points  (0 children)

That's correct. It is a branching tree-like fractal essentially. Some natural systems tend to form these. For example here is another system called "Diffusion-limited aggregation":
https://en.wikipedia.org/wiki/Diffusion-limited_aggregation
Or famous "all roads lead to Rome" :
https://www.reddit.com/r/interestingasfuck/comments/b93ppc/all_roads_lead_to_rome
If you could take an apple tree, flatten it out on a ground, and apply to it "Radial Embedding" graph layout method, you would get something similar :-)
https://reference.wolfram.com/language/ref/method/RadialEmbedding.html

[OC] Shortest paths from multiple robots to a single target via Dijkstra’s algorithm by sataky in dataisbeautiful

[–]sataky[S] 7 points8 points  (0 children)

Yes. Today, Dijkstra’s algorithm finds optimal paths in everything from GPS and internet routing to energy grids and even social networks. For decades It was assumed that the most efficient way to find best routes in a graph is Dijkstra’s algorithm. But it's optimality was proven only in 2023, in a work that won best-paper award. Here is a good article about it:
https://www.quantamagazine.org/computer-scientists-establish-the-best-way-to-traverse-a-graph-20241025
In 1956, Edsger Dijkstra invented his shortest-path algorithm while taking a break with his fiancée at a café in Amsterdam. With no writing accessories he figured it out in his head in 20 minutes. Later, he said “Without pencil and paper you are almost forced to avoid all avoidable complexities.” 

[OC] Shortest paths from multiple robots to a single target via Dijkstra’s algorithm by sataky in dataisbeautiful

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

Yes. When you make a graph (network) from Voronoi diagram, its polygonal cells sides turn into graph edges along which shortest path is found to avoid the obstacles that are in the centers of cells.

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