I found this presentation helpful for an intro to H3's design/motivation: "Engineering Sub-City Geos for a Hyper-Local Marketplace with Uber": https://youtu.be/wDuKeUkNLkQ?si=-9JmxZQJ2LZo6Kh4
This online tool gives you great idea about what this means on different levels:
Is the big thing here that those hexagons have the advantages of a circles (almost even max distance in all directions from center) with the advantages of squares (no overlap)?
This is like the advantage of using 6 mile hexes in a tabletop rpg map.[0]
Each hex can be divided into smaller hexes until you get to the level of feet/meters as opposed to miles/kilometers.
[0]: https://steamtunnel.blogspot.com/2009/12/in-praise-of-6-mile...
How is this better than space filling curves? Or does this solve a different problem? It’s a bit hard to see what and how it solves. Why hexagons?
There is a super high quality Rust lib that implements this: https://github.com/HydroniumLabs/h3o
Hexagons could be a new kind of political entity of the future, where, for example, rewards for climate progress (measured by satellites) could be put into practice.
I'm still surprised What3Words isn't more popular outside of the UK.
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I've read all the comments here and still don't understand the rationale for hexagons as opposed to squares.
In every sense, squares seem to be much easier to reason about and easier to hierarchically partition than hexagons are.
There are certain advantages to hexagons in certain contexts, like six degrees of movement instead of four in board games, but I don't see how any of those advantages translate here for geographical indexing.
I'd love to understand why hexagons as opposed to squares in this context are a superior solution rather than unnecessary complexity?