Spatial Spread Sampling Using Weakly Associated Vectors

TL;DR

This paper introduces a new spatial sampling method that ensures well-spread samples with exact inclusion probabilities, outperforming existing methods in accuracy and variance estimation for spatial data analysis.

Contribution

A novel sampling technique using a stratification matrix to achieve well-spread samples with exact inclusion probabilities, improving over existing methods.

Findings

Outperforms GRTS and LPM in simulations

Provides more accurate variance estimates

Ensures well-spread samples with specified inclusion probabilities

Abstract

Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion probabilities and provides samples that are very well-spread. A set of simulations shows that our method outperforms other existing methods such as the Generalized Random Tessellation Stratified (GRTS) or the Local Pivotal Method (LPM). Analysis of the variance on a real dataset shows that our method is more accurate than these two. Furthermore, a variance estimator is proposed.