  Surveys provide important evidence for policymaking, decision-making, and
understanding of society. However, conducting the large surveys required to
provide subpopulation level estimates is expensive and time-consuming.
Multilevel Regression and Poststratification (MRP) is a promising method to
provide reliable estimates for subpopulations from surveys without the amount
of data needed for reliable direct estimates. Graphical displays have been
widely used to communicate and diagnose MRP estimates. However, there have been
few studies on how visualisation should be performed in this field.
Accordingly, this study examines the current practice of MRP visualisation
using a systematic literature review. This study also applies MRP to estimate
the Trump vote share in the U.S. 2016 presidential election using the
Cooperative Congressional Election Study (CCES) data to illustrate the
implication of current visualisation practices and explore alternatives for
improvement. We find that uncertainty is not often displayed in the current
practice, despite its importance for survey inference. The choropleth map is
the most frequently used to display MRP estimates even though it only shows
point estimates and could hinder the information conveyed. Using various
graphical representations, we show that visualisation with uncertainty can
illustrate the effect of different model specifications on the estimation
result. In addition, this study also proposes a visualisation strategy to also
take the bias-variance trade-off into account when evaluating MRP models.
