  Maps have long been been used to visualise estimates of spatial variables, in
particular disease burden and risk. Predictions made using a geostatistical
model have uncertainty that typically varies spatially. However, this
uncertainty is difficult to map with the estimate itself and is often not
included as a result, thereby generating a potentially misleading sense of
certainty about disease burden or other important variables. To remedy this, we
propose simultaneously visualising predictions and their associated uncertainty
within a single map by varying pixel size. We illustrate our approach using
examples of malaria incidence, but the method could be applied to predictions
of any spatial continua with associated uncertainty.
