TL;DR
This paper introduces a novel graph-based spatial sampling method using a lagged Metropolis-Hastings walk, offering increased flexibility and efficiency for sampling units in space compared to traditional methods.
Contribution
It presents a new graph spatial sampling technique that enhances sampling flexibility and efficiency through a specialized Markov chain approach.
Findings
More flexible sampling design compared to existing methods
Improved efficiency in spatial sampling applications
Effective for sampling units-in-space
Abstract
We develop lagged Metropolis-Hastings walk for sampling from simple undirected graphs according to given stationary sampling probabilities. We explain how to apply the technique together with designed graphs for sampling of units-in-space. We illustrate that the proposed graph spatial sampling approach can be more flexible for improving the design efficiency compared to the existing spatial sampling methods.
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