I'd be curious to see a more thorough comparison to S2, which IMHO seems simpler (it's just quadtrees) and likely faster (it supports O(1) lookup vs. needing a hierarchical search).
Representation systems for geospatial data models is an amazingly deep theoretical rabbit hole. Common systems are almost always optimized for presentation as most were originally designed for cartographic use cases. If you were looking at representation systems optimized for fast, scalable geospatial analytics, for example, you'd use some type of 3-space embedding representation. There is a lot of diversity.
Unlike single-surface representations, these have the advantage of being essentially free of computational edge cases if you design them correctly. They are also amenable to implementations that are extremely computationally efficient to use, which is a bit of an afterthought for most presentation-optimized designs but important for high-scale geospatial analytics.
A common reflexive criticism of these representations is that they use equal volume sharding, which means that sharding them is not a good approximation of equal area on the embedded surface. An equal area decomposition only makes sense in the context of presentation (e.g. tiling) because the underlying data distribution is naturally extremely and unpredictably skewed, leading to non-uniform cell loading no matter how you decompose it. The assumption that equal area decomposition helps to ensure uniform cell loading is trivially false in practice, making it a non-optimization. Therefore, any competent implementation always requires a separate mechanism for ensuring uniform loading independent of the decomposition model.
The term of art for all of this is discrete global grid systems (commonly "DGGS"). The vast majority of the literature is focused on presentation optimized systems, and the design of single-surface representations, but other types of representations are discussed. It has a very rich taxonomy. I have an article I've been sporadically writing which I should probably finish that steps through the design of a state-of-the-art 3-space embedding representation system for scale-out analytics, based on a (currently stalled) effort to produce a formal standard for industry. A good 3-space embedding has a relatively simple description and implementation but there is much technical subtlety as to why it is designed a specific way.
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.
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.
I use this library every day and absolutely love it.
Bear in mind that H3 only has 16 levels of resolution, so traversing from top level down to the square-metre level is still a constant time operation i.e. O(16)