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

TL;DR

The paper presents the Zones Algorithm, a relational database-based method for efficiently performing spatial proximity and cross-matching queries in high-dimensional spaces, with improvements over previous versions.

Contribution

It introduces algorithmic enhancements and corrections to the Zones Algorithm, enabling portable and efficient spatial queries within relational database systems.

Findings

Provides a corrected and improved version of the Zones Algorithm

Demonstrates implementation using USGS spatial datasets

Enables efficient points-near-a-point and cross-matching queries

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.