The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets

Abstract

Zones index an N-dimensional Euclidian or metric space to efficiently support points-near-a-point queries either within a dataset or between two datasets. The approach uses relational algebra and the B-Tree mechanism found in almost all relational database systems. Hence, the Zones Algorithm gives a portable-relational implementation of points-near-point, spatial cross-match, and self-match queries. This article corrects some mistakes in an earlier article we wrote on the Zones Algorithm and describes some algorithmic improvements. The Appendix includes an implementation of point-near-point, self-match, and cross-match using the USGS city and stream gauge database.

References in corpus (3)

• Indexing the Sphere with the Hierarchical Triangular Mesh
• When Database Systems Meet the Grid
• There Goes the Neighborhood: Relational Algebra for Spatial Data Search

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• Probabilistic Cross-identification of Multiple Catalogs in Crowded Fields