Selection for visualization: Voronoi tessellation of large scale and sparsely distributed data

Visualizing data specifically selected among a large scale and sparsely distributed database can often be instantaneous and has to be low complexity in computation, especially when simulation process is involved. This article presents a novel method of selection for visualization. It uses Voronoi tessellation to decompose the database into Voronoi data cells, then it adapts the Point Location and the Nearest Neighbor Searching algorithms to reduce data search time over the large data set so that visualization and simulation can be instantaneously realized. The method is successfully implemented in large scale and complex virtual environment systems and is tested with applications in satellite tracking and controls.