Skip to main content
SLU publication database (SLUpub)

Research article2024Peer reviewedOpen access

Locally correlated Poisson sampling

Prentius, Wilmer


Designs 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.


auxiliary variables; design-based sampling; environmental monitoring; local pivotal method; spatially correlated Poisson sampling; unequal probability sampling

Published in

2024, Volume: 35, number: 2, article number: e2832Publisher: WILEY

    UKÄ Subject classification

    Probability Theory and Statistics
    Environmental Sciences

    Publication identifier


    Permanent link to this page (URI)