Efficient design of geographically-defined clusters with spatial autocorrelation

TL;DR

This paper presents a geostatistical approach to optimize geographically-defined cluster designs by accounting for spatial autocorrelation, improving efficiency and cost-effectiveness in survey and experimental studies.

Contribution

It introduces a method to approximate within-cluster covariance using geostatistical models, enabling more efficient cluster design under budget constraints.

Findings

Cluster design efficiency depends on area, sampling proportion, and method.

Approximate covariance helps estimate effective sample size.

Optimization of cluster parameters improves study cost-effectiveness.

Abstract

Clusters form the basis of a number of research study designs including survey and experimental studies. Cluster-based designs can be less costly but also less efficient than individual-based designs due to correlation between individuals within the same cluster. Their design typically relies on \textit{ad hoc} choices of correlation parameters, and is insensitive to variations in cluster design. This article examines how to efficiently design clusters where they are geographically defined by demarcating areas incorporating individuals and households or other units. Using geostatistical models for spatial autocorrelation we generate approximations to within cluster average covariance in order to estimate the effective sample size given particular cluster design parameters. We show how the number of enumerated locations, cluster area, proportion sampled, and sampling method affect the efficiency of the design and consider the optimization problem of choosing the most efficient design subject to budgetary constraints. We also consider how the parameters from these approximations can be interpreted simply in terms of `real-world' quantities and used in design analysis.