Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua

TL;DR

This paper introduces a novel visualization method that combines disease risk estimates and their uncertainty on maps by varying pixel size, improving the communication of spatial uncertainty in disease burden assessments.

Contribution

The authors propose a new pixelation technique to visualize both predictions and their uncertainty simultaneously on maps, enhancing interpretability of spatial data.

Findings

Effective visualization of uncertainty in disease maps

Applicable to various spatial continua with uncertainty

Improves understanding of spatial risk estimates

Abstract

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.