![]() With lh the total sample size was random, but the size fluctuations were acceptable. In bc the four covariates and the spatial coordinates were used as spreading variables. In designs b and bc four covariates were used as balancing variables. In a simulation study balanced (b) random sampling, balanced coverage (bc) random sampling, and latin hypercube (lh) random sampling were compared in terms of the sampling distributions of number of unsampled marginal strata (U) measuring coverage of feature space, Mean Squared Shortest Distance (MSSD) measuring spatial coverage, and error in the estimated mean e. Population parameters can then be estimated by design-based, model-assisted or model-based inference. ![]() ![]() When implemented as a balanced sampling design, the inclusion probabilities of the population units are known. Latin hypercube sampling appears to be a special case of balanced sampling. This paper introduces balanced sampling and demonstrates its potential utility and versatility. Recent developments make this sampling design attractive for statistical soil surveys. In balanced sampling a linear relation between the soil property of interest and one or more covariates with known means is exploited in selecting the sampling locations. ![]() 111 - 121.ĭesign-based estimation - Digital soil mapping - Latin hypercube sampling - Probability sampling - Random forests Balanced sampling : A versatile sampling approach for statistical soil surveys ![]()
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