  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.
