Halton iterative partitioning: spatially balanced sampling via partitioning

Robertson, B. L.; McDonald, Trent L.; Price, C. J.; Brown, Jennifer

doi:10.1007/s10651-018-0406-6

Cited by 29 publications

(26 citation statements)

References 18 publications

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“…BAS uses the Halton sequence to spread its sample across multiple dimensions. BAS is conceptually simple, computationally efficient and is particularly useful for drawing spatially balanced oversamples (Robertson et al 2018). We found 34 publications citing BAS, where most of the papers were methodological rather than applied.…”

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confidence: 99%

“…To improve the performance of BAS on point resources, Robertson et al (2018) presented Halton iterative partitioning (HIP). This spatially balanced design uses properties of the Halton sequence to partition a point resource into nested boxes to draw its sample, rather than using the sequence itself.…”

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confidence: 99%

“…Why should environmental scientists use spatially balanced designs?(Stevens and Olsen 2004;Christianson and Kaufman 2016;McGarvey et al 2016) Robertson et al 2018;Theobald et al 2007) observed. Of the unobservable sites, eight were on private land and 39 had dry channels or were not accessible.…”

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confidence: 99%

“…To achieve the desired sample size, the researchers selected replacement sites from an oversample drawn using simple random sampling. The potential advantage of using spatially balanced oversamples is that the observed sample maintains some degree of spatial balance over the observable resource(Stevens and Olsen 2014;Robertson et al 2018). Although oversampling is of practical importance, it does not eliminate the non-response from unobservable units or the bias of an inference(Robertson et al 2018).…”

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confidence: 99%

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Spatially balanced sampling designs for environmental surveys

Kermorvant

D’Amico

Bru

et al. 2019

Environ Monit Assess

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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.

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Spatially balanced sampling designs for environmental surveys

Kermorvant

D’Amico

Bru

et al. 2019

Environ Monit Assess

Self Cite

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“…Then a two-dimensional random start Halton sequence with uniformly spread points in the rectangle is generated. The first n points that are observed in the study area will constitute the sample of size n. An alternative design to BAS which is called Halton iterative partitioning (HIP) was introduced by Robertson, McDonald, Price, and Brown (2018) to overcome some drawbacks of BAS for finite populations.…”

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confidence: 99%

A sample coordination method to monitor totals of environmental variables

Zhao

Grafström

2020

Environmetrics

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A new sampling strategy for design‐based environmental monitoring is proposed. It has the potential to produce superior estimators for totals of environmental variables and their changes over time. In the strategy, we combine two concepts known as spatially balanced sampling and coordination of samples. Spatially balanced sampling can provide superior estimators of totals, while coordination of samples over time is often used to improve estimators of change. Compared with reference strategies, we show that the new strategy can improve the precision of the estimators of totals and their change simultaneously. A forest inventory application is used to illustrate the new strategy and the results can be summarized as (i) using auxiliary information to spread the sample can improve the estimators of totals; (ii) a positive coordination of the samples reduced the variance of the estimator of change by more than 37% compared with independent samples; and (iii) a high overlap between successive samples does not guarantee a good estimator of change. The presented strategy can be used to develop more efficient environmental monitoring programs.

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Sequential spatially balanced sampling

2022

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Sequential sampling occurs when an entire population is unknown in advance and data are received one by one or in groups of units. This article proposes a new algorithm to sequentially select a balanced sample. The algorithm respects equal and unequal inclusion probabilities. The method can also be used to select a spatially balanced sample if the population of interest contains spatial coordinates. A simulation study is proposed, and the results show that the proposed method outperforms other methods.

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Halton iterative partitioning: spatially balanced sampling via partitioning

Cited by 29 publications

References 18 publications

Spatially balanced sampling designs for environmental surveys

Spatially balanced sampling designs for environmental surveys

A sample coordination method to monitor totals of environmental variables

Sequential spatially balanced sampling

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