Sequential Spatially Balanced Sampling

TL;DR

This paper introduces a new sequential sampling algorithm that ensures spatial and statistical balance, accommodating various inclusion probabilities, and demonstrates its superior performance through simulations.

Contribution

A novel sequential sampling method that guarantees spatial and probability-based balance, applicable to populations with spatial data, and validated by simulation results.

Findings

Outperforms existing sampling methods in simulations

Works with equal and unequal inclusion probabilities

Effective for spatially distributed populations

Abstract

Sequential sampling occurs when the entire population is not known in advance and data are obtained one at a time or in groups of units. This manuscript proposes a new algorithm to sequentially select a balanced sample. The algorithm respects equal and unequal inclusion probabilities. The method can also be used to select a spatially balanced sample if the population of interest contains spatial coordinates. A simulation study is proposed and the results show that the proposed method outperforms other methods.