Some methods to improve the utility of conditioned Latin hypercube sampling
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 f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2019-02-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/6451.pdf |
| _version_ | 1827612184906039296 |
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| author | Brendan P. Malone Budiman Minansy Colby Brungard |
| author_facet | Brendan P. Malone Budiman Minansy Colby Brungard |
| author_sort | Brendan P. Malone |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T08:17:47Z |
| format | Article |
| id | doaj.art-8f997bae5ec54e3682fc28afd7abac4e |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:17:47Z |
| publishDate | 2019-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-8f997bae5ec54e3682fc28afd7abac4e2023-12-02T21:59:30ZengPeerJ Inc.PeerJ2167-83592019-02-017e645110.7717/peerj.6451Some methods to improve the utility of conditioned Latin hypercube samplingBrendan P. Malone0Budiman Minansy1Colby Brungard2CSIRO, Agriculture and Food, Canberra, ACT, AustraliaThe Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW, AustraliaPlant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USAThe 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.https://peerj.com/articles/6451.pdfSoil samplingConditioned Latin HypercubeDigital soil mappingOptimizationSamplingSample optimization |
| spellingShingle | Brendan P. Malone Budiman Minansy Colby Brungard Some methods to improve the utility of conditioned Latin hypercube sampling PeerJ Soil sampling Conditioned Latin Hypercube Digital soil mapping Optimization Sampling Sample optimization |
| title | Some methods to improve the utility of conditioned Latin hypercube sampling |
| title_full | Some methods to improve the utility of conditioned Latin hypercube sampling |
| title_fullStr | Some methods to improve the utility of conditioned Latin hypercube sampling |
| title_full_unstemmed | Some methods to improve the utility of conditioned Latin hypercube sampling |
| title_short | Some methods to improve the utility of conditioned Latin hypercube sampling |
| title_sort | some methods to improve the utility of conditioned latin hypercube sampling |
| topic | Soil sampling Conditioned Latin Hypercube Digital soil mapping Optimization Sampling Sample optimization |
| url | https://peerj.com/articles/6451.pdf |
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