Spatially Balanced Sampling with Local Ranking

Abstract

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.

The Master of Data Science of the Department of Statistics and Actuarial Science at The University of Hong Kong

“…Recently, balanced sampling has been actively used in the area of environmental sampling. Stevens Jr and Olsen (2004) first introduced the term spatially balanced sampling and since then many efforts have been made in this area (Grafström et al, 2012;Grafström and Tillé, 2013;Brown et al, 2015;Grafström and Matei, 2018;Kermorvant et al, 2019;Robertson et al, 2022;Xie et al, 2022). Despite its development in the aforementioned domains, to the best of our knowledge, there has not been an application of balanced sampling in the context of fitting a Cox model for survival times under a two-phase design.…”