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.

}, author = {Jim Gray and Mar{\'i}a A. Nieto-Santisteban and Alexander S. Szalay}, institution = {Microsoft Research}, month = {April}, number = {MSR-TR-2006-52}, pages = {0}, title = {The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets}, url = {http://research.microsoft.com/apps/pubs/default.aspx?id=64524}, year = {2006}, }