I think the key is in the distributed nature, h3 is effectively a grid so can easily be distributed over nodes. A recursive system is much harder to handle that way. R-trees are great if you are OK with indexing all data on one node, which I think for a global system is a no-go.

This is all speculation, but intuitively your criticism makes sense.

Also, mapping 147k cities to countries should not take 16 workers and 1TB of memory, I think the example in the article is not a realistic workload.

How do you join two datasets using r-trees? In a business setting, having a static and constant projection is critical. As long as you agree on zoom level, joining two datasets with S2 and H3 is really easy.

To add to sibling comment, if you have streaming data you have to update the whole index every time with r/kd trees whereas with H3 you just compute the bin, O(1) instead of O(log n).

Not rocket science but different tradeoffs, that’s what engineering is all about.