I recall when I first saw Ubers’s H3[1], it really reminded me of healpix (from grad school). I know the algorithm is different but similar problem space: “given a point on a sphere, assign it to a stable cell ID”
Uber unsurprisingly doing a lot of innovation in this space due to the spicy routing problems they have. If you want to test someone's geospatial knowledge in an interview, give them 45 minutes to "Design Uber" and see what creative solutions they come up with to solve the routing problems.
I recall when I first saw Ubers’s H3[1], it really reminded me of healpix (from grad school). I know the algorithm is different but similar problem space: “given a point on a sphere, assign it to a stable cell ID”
1. https://www.uber.com/us/en/blog/h3/
Uber unsurprisingly doing a lot of innovation in this space due to the spicy routing problems they have. If you want to test someone's geospatial knowledge in an interview, give them 45 minutes to "Design Uber" and see what creative solutions they come up with to solve the routing problems.
Also very cool: Google's S2 geometry - https://s2geometry.io/
Used by https://catch.astro.umd.edu/
I used S2 to make https://wherewords.id/
It was very pleasant to work with - I spent by far the majority of the project time on the wordlist.
I've seen some similar geohash-words stuff, this is super cool, thanks for sharing!
I used healpy[1] once during my undergraduate years. It was a summer project to develop an algorithm to find void galaxies.
[1]: https://healpy.readthedocs.io/en/latest/
Love the HEALPix scheme, super useful for indexing too (some similar characteristics to geohashes).
See also HiPS maps, which map the sky onto ever finer HEALPix grids to allow zooming: https://aladin.cds.unistra.fr/hips/