Spatial indexing of large volume swath data sets

This paper introduces a new hierarchical data structure that supports efficient insertion, deletion and two-dimensional range query operations on swath data. Three different spatial indexing methods were designed, implemented and compared on a large swath data set. Two of the methods are based on the Morton code sequence, and the third uses an R-tree for the index. A relational database management system (RDBMS) was used to build one of the Morton code sequence indexing methods. Experiments with the large scale data set show that R-tree indexing is superior to Morton sequence indexing by providing two to nine times faster two dimensional range search speed, occupying slightly less disk space, and supporting the range deletion operation. Time and space requirements for spatial indexing with the RDBMS were shown experimentally to be significantly worse than for the direct C code implementation of the Morton code sequence indexing.