The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets
<|reference_start|>The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets: 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.<|reference_end|>
@article{gray2007the,
    title={The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching
  Spatial Datasets},
    author={Jim Gray, Maria A. Nieto-Santisteban, Alexander S. Szalay},
    journal={arXiv preprint arXiv:cs/0701171},
    year={2007},
    number={MSR TR 2006 52},
    archivePrefix={arXiv},
    eprint={cs/0701171},
    primaryClass={cs.DB cs.DS}
}