Quadtree- and octree-based approach for point data selection in 2D or 3D
This article describes a new automatic quadtree-/octree-based and scale-dependent generalization algorithm for point selection. The benefit toward existing point selection methods is that it preserves global as well as local characteristics of the spatial point distribution and of the spatial point density. It can be applied not only to points in 2D space but also to points in 3D space. In this article, an evaluation of the new point selection method is also provided.