空间数据集上点的区域算法
The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets
摘要：
区域索引N维欧几里德或度量空间，以有效地支持数据集内或两个数据集之间的点附近的点查询。该方法使用了关系代数和几乎所有关系数据库系统中的B树机制。因此，Zones算法提供了一个可移植的关系实现的点附近的点，空间交叉匹配，和自匹配查询。这篇文章纠正了我们之前写的关于区域算法的文章中的一些错误，并描述了一些算法改进。附录包括使用美国地质勘探局城市和河流测量数据库的点-近点、自匹配和交叉匹配的实现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
