Casting from float to short gives rise to the retraction \(\rho: P \to B\) and its section \(\sigma: B \to P\) which lifts \((0,0,0)\) to \((0,0,0)\), is exploted in SM64 speed running. In particular \(P\) is a covering space of \(B\) with fiber \(F:= \ker(\rho)\), which are all points, where Mario transitions from one “parallel universe” to the next.
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The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.