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?
Hexagonal tessellations are optimized for display and visualization because they approximate equal surface areas on a sphere. They have poor properties for scalable and efficient analytical data processing because they are not congruent.
Indexing for scalable analytical processing on a sphere requires congruent equal volume decomposition, even if you only care about the surface. You can trivially project it to the surface later if needed. Binary space decomposition, of which space-filling curves are a subset, are strongly preferable for this type of indexing.
In practice, a lot of data processing systems will render data as an H3 tiles only for visualization as a final step. That conversion is fast and trivial and it makes pretty pictures. It is not as commonly used to index the underlying data model because scalability and performance is prohibitive unless the data is small.
The purpose is to be able to predictably map any coordinate to its associated hexagon.
In database applications this makes it easier to query all data associated with a hexagonal area.
It’s the same reason soccer balls aren’t covered in squares/rectangles but rather hexagons.