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A comment on H3: Uber’s Hexagonal Hierarchical Spatial Index (2018). Parent comment

Re:H3: Uber’s Hexagonal Hierarchical Spatial Index (2018) by pininja on Thursday July 18, 2019 @12:21AM (#20466667)
H3 and S2 are kinda similar, except by using hexagons, H3 grid centroids are equidistant - rectangles have different distances from center to center.

H3 also has efficient means for finding a cell’s neighbors, and comes with some nice algorithms - like the “compact” fill. See https://uber.github.io/h3/#/documentation/overview/use-cases

The hierarchical search data is built into a grid’s ID in both H3 and S2, which helps when comparing ID’s to see how close they are to each other.

For visualization, I prefer the way H3 looks over S2. While that’s just an opinion, H3 grid is exposed directly to drivers and in lots of tools.

I work for Uber and have used H3, but don’t work on the library.

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Re:H3: Uber’s Hexagonal Hierarchical Spatial Index (2018) by Mr_P on Thursday July 18, 2019 @12:25AM (#20466693)
> For visualization, I prefer the way H3 looks over S2. While that’s just an opinion, H3 grid is exposed directly to drivers and in lots of tools.

Ah, yes this makes a lot of sense. The video in the link actually does have a nice comparison at 17:30, where this is called out. It seems to me to be the most compelling argument for hexagons.

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