  It is becoming increasingly popular to produce high-resolution maps of
vaccination coverage by fitting Bayesian geostatistical models to data from
household surveys. Often, the surveys adopt a stratified cluster sampling
design. We discuss a number of crucial choices with respect to two key aspects
of the map production process: the acknowledgment of the survey design in
modeling, and the appropriate presentation of estimates and their
uncertainties. Specifically, we consider the importance of accounting for
survey stratification and cluster-level non-spatial excess variation in survey
outcomes when fitting geostatistical models. We also discuss the trade-off
between the geographical scale and precision of model-based estimates, and
demonstrate visualization methods for mapping and ranking that emphasize the
probabilistic interpretation of results. A novel approach to coverage map
presentation is proposed to allow comparison and control of the overall map
uncertainty level. We use measles vaccination coverage in Nigeria as a
motivating example and illustrate the different issues using data from the 2018
Nigeria Demographic and Health Survey.
