Abstract
AbstractDesigns that produces spatially balanced, or well‐spread, samples are desirable as they increase the probability of obtaining a sample highly representative of the population. Spatially correlated Poisson sampling (SCPS) is a method for selecting well‐spread samples. In the SCPS method, the sampling outcomes (inclusion or exclusion of units) are decided sequentially. After each decision, the inclusion probabilities of surrounding units are updated. A specific order for deciding the sampling outcomes is not enforced for SCPS, that is, the order can be chosen randomly or be fixed. A new modified method called locally correlated Poisson sampling (LCPS) is suggested. In this new method, the order of the decisions makes sure the inclusion probabilities are updated (more) locally. As a result, a stronger negative correlation between inclusion indicators of nearby units is achieved. Simulations on various data sets show that the resulting samples from LCPS, in general, are more spatially balanced and produce lower variance than samples from SCPS and the local pivotal method.
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