loved the article. I strongly believe that when doing geospatial indices, it is quite important to up front establish the nature and distribution of your data.

Searching for a generic/theoretically all-encompassing solution is quite akin to looking for the perfect pub-sub system for all workloads. :)

My comment was related to lat/lon indexed data clustered around cities. If you know beforehand that you're going to deal with such a dataset (static or dynamic), one can pragmatically decide on some sort of by-convention partitioning (e.g. Partitioning by continent/region which will bring it down to in-memory indices not needing continuous disk access).

I love the non-euclidean bit in the piece you wrote. Anyone who has tried to do a k-nearest neighbor query east of New Zealand will appreciate that bit. :D