Spatially balanced sampling designs for environmental surveys

Abstract

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.

Sequential process to choose efficient sampling design based on partial prior information data and simulations

“…Hence, a population could be i.i.d., spatial auto-correlated (SAC), spatially stratified heterogeneity (SSH), both SAC and SSH and these characteristics will modify our method results. For example, it is now proved that spatially balanced sampling designs (SBS) are more efficient than simple random sampling when the studied population in SSH (Stevens and Olsen, 2004;Barabesi and Franceschi, 2011;Grafström and Tillé, 2013;Robertson et al, 2013;Kermorvant et al, 2019b). Furthermore, if co-variates are available, some SBS can also balance samples in this co-variates dimensions to gain in efficiency (Brown et al, 2015).…”

“…Probabilistic sampling designs, displaying a random property, must be used for design-based sampling. Kermorvant et al (2019b) published a review of probabilistic spatially bal-anced sampling designs and a tutorial to use them on R software. Simple random sampling (SRS) design is one of the most commonly used survey design in ecology, due to its ease of use and its flexibility.…”

“…Power analysis are, for now, able to be calculated only for simple random sampling design and so are not very relevant when using a more advanced sampling design (i.e spatially balanced sampling design). Spatially balanced sampling designs are proved to be more efficient than simple random sampling (Robertson et al, 2013;Brown et al, 2015;Grafström and Matei, 2018;Kermorvant et al, 2019b) and so need fewer samples to detect same level of change between two survey seasons. We believe that overestimation of sample size is probable in such cases.…”

MBHdesign: An R‐package for efficient spatial survey designs

“…Several packages exist for generating spatially-balanced designs (Kermorvant et al 2019). These include:…”

“…Several packages exist for generating spatially balanced designs (Kermorvant et al, 2019). These include: spsurvey (Kincaid et al., 2019) that implements the generalized random‐tessellation stratified (GRTS) algorithm (Stevens & Olsen, 2004); SDraw (McDonald & McDonald, 2020) that implements a range of spatially balanced methods including BAS, and; BalancedSampling (Grafström & Lisic, 2019) that implements the local pivotal method (LPM Grafström, 2012) as well as spatially correlated Poisson sampling (SCPS Grafström et al., 2012).…”