Skip quadtrees: Dynamic data structures for multidimensional point sets
International Journal of Computational Geometry & Applications, 2008•World Scientific
We present a new multi-dimensional data structure, which we call the skip quadtree (for
point data in R 2) or the skip octree (for point data in R d, with constant d> 2). Our data
structure combines the best features of two well-known data structures, in that it has the well-
defined “box”-shaped regions of region quadtrees and the logarithmic-height search and
update hierarchical structure of skip lists. Indeed, the bottom level of our structure is exactly a
region quadtree (or octree for higher dimensional data). We describe efficient algorithms for …
point data in R 2) or the skip octree (for point data in R d, with constant d> 2). Our data
structure combines the best features of two well-known data structures, in that it has the well-
defined “box”-shaped regions of region quadtrees and the logarithmic-height search and
update hierarchical structure of skip lists. Indeed, the bottom level of our structure is exactly a
region quadtree (or octree for higher dimensional data). We describe efficient algorithms for …
We present a new multi-dimensional data structure, which we call the skip quadtree (for point data in R2) or the skip octree (for point data in Rd, with constant d > 2). Our data structure combines the best features of two well-known data structures, in that it has the well-defined “box”-shaped regions of region quadtrees and the logarithmic-height search and update hierarchical structure of skip lists. Indeed, the bottom level of our structure is exactly a region quadtree (or octree for higher dimensional data). We describe efficient algorithms for inserting and deleting points in a skip quadtree, as well as fast methods for performing point location, approximate range, and approximate nearest neighbor queries.
World Scientific