Display Accessibility Tools

Accessibility Tools


Highlight Links

Change Contrast

Increase Text Size

Increase Letter Spacing

Dyslexia Friendly Font

Increase Cursor Size

Alex McKim

Various statistical models for spatial data rely on some form of a nearest neighbor calculation among observed spatial locations. A brute force solution to a nearest neighbor calculation is easy to implement, but is computationally impractical for large data sets. Various data tree structures, e.g., k-d trees, have been proposed to improve the computation efficiency of nearest neighbor searches. Our focus is on efficient implementation of a statistical model called the Nearest Neighbor Gaussian Process (NNGP) that involves nearest neighbor searches for massive spatial data sets. We developed a specialized k-d tree structure and search algorithm designed to work with the NNGPs model assumptions. We compare the search time of our proposed k-d tree to that of a brute force nearest neighbor search under different parallel computing settings and data sizes.