[cpa]

The big reason is that H3 is data independant. You put your data in predefined bins and then join on them, whereas kd/r trees depend on the data and building the trees may become prohibitive or very hard (especially in distributed systems).

[mgaunard]

Indices are meant to depend on the data yes, not exactly rocket science.

Updating an R-tree is log(n) just like any other index.

[vouwfietsman]

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.

[cpa]

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.

[rockinghigh]

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.

[mgaunard]

Spatial indices simply partition your data in N-dimensional space the same way a binary tree does it in 1-dimensional space.

The whole advantage over a static partition is that it will allow you to properly deal with data that is irregularly distributed.

Those data structures can definitely be merged if that's what you're asking.

[vouwfietsman]

This data is indeed not irregularly distributed, in fact the fun thing about geospatial data is that you always know the maximum extent of it.

About your binary tree comment: yes this is absolutely valid, but consider then that binary trees also are a bad fit for distributed computing, where data is often partitioned at the top level (making it no longer a binary tree but a set of binary trees) and cross-node joins are expensive.