Spatially Balanced Sampling with Local Ranking (2022)
Type of ContentJournal Article
A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with good precision. Spatially balanced designs draw samples with good spatial spread and provide precise results for commonly used estimators when surveying natural resources. In this article, we propose a new sampling strategy that incorporates ranking information from nearby locations into a spatially balanced sample. If the population exhibits spatial trends, our simple local ranking strategy can improve the precision of commonly used estimators. Numerical results on several test populations with different spatial structures show that local ranking can improve the performance of a spatially balanced design. To show that local ranking is simple and effective in practice, we provide an example application for the health and productivity assessment of a Shiraz vineyard in South Australia. Supplementary materials accompanying this paper appear online.
CitationRobertson B, Ozturk O, Kravchuk O, Brown J (2022). Spatially Balanced Sampling with Local Ranking. Journal of Agricultural, Biological, and Environmental Statistics.
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordsenvironmental sampling; quasi-random sampling; ranked set sampling; spatial balance
ANZSRC Fields of Research49 - Mathematical sciences::4905 - Statistics::490507 - Spatial statistics
RightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Funding: Open Access funding enabled and organized by CAUL and its Member Institutions
Showing items related by title, author, creator and subject.
Trent M; Jennifer B; Chris P; Robertson, Blair (2021)
Robertson B; McDonald T; Brown J; Price C (2019)
Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation Curran M; Hodza P; Cox S; Lanning S; Robertson B; Robinson T; Stahl P (2020)Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ...