Faster Compressed Quadtrees
Guillermo de Bernardo, Travis Gagie, Susana Ladra, Gonzalo Navarro, and
Real-world point sets tend to be clustered, so using a machine word for each
point is wasteful. In this paper we first show how a compact representation of
quadtrees using O(1) bits per node can break this bound on clustered point sets, while offering efficient range searches. We then describe a new compact quadtree representation based on heavy-path decompositions, which supports queries faster than previous compact structures. We present experimental evidence showing that our structure is competitive in practice.