Some methods to improve the utility of conditioned Latin hypercube sampling | Zendy

Brendan Malone | Zendy; Budiman Minansy | Zendy; Colby Brungard | Zendy

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Some methods to improve the utility of conditioned Latin hypercube sampling

Author(s) -

Brendan Malone,

Budiman Minansy,

Colby Brungard

Publication year - 2019

Publication title -

peerj

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.927

H-Index - 70

ISSN - 2167-8359

DOI - 10.7717/peerj.6451

Subject(s) - latin hypercube sampling , sampling (signal processing) , computer science , sample (material) , field (mathematics) , operations research , statistics , mathematics , monte carlo method , filter (signal processing) , chemistry , chromatography , pure mathematics , computer vision

The conditioned Latin hypercube sampling (cLHS) algorithm is popularly used for planning field sampling surveys in order to understand the spatial behavior of natural phenomena such as soils. This technical note collates, summarizes, and extends existing solutions to problems that field scientists face when using cLHS. These problems include optimizing the sample size, re-locating sites when an original site is deemed inaccessible, and how to account for existing sample data, so that under-sampled areas can be prioritized for sampling. These solutions, which we also share as individual R scripts, will facilitate much wider application of what has been a very useful sampling algorithm for scientific investigation of soil spatial variation.

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