Visualisation of uncertainty using the HSI colour model: computations with colours

Paper describes two GIS methods for visualisation of uncertainty associated with spatial prediction of continuous and categorical variables. In the case of continuous variables, the key issue is to visualise both predictions and the prediction error at the same time, while in the case of categorical data, the key issue is to visualise multiple memberships and confusion in-between them. Both methods are based on the Hue-Saturation-Intensity (HSI) colour model and calculations with colours using the colour mixture (CM) concept. The HSI is a psychologically appealing colour model – hue is used to visualise values or taxonomic space and whiteness (paleness) is used to visualise the uncertainty. In the case of continuous variables, a twodimensional legend was designed to accompany the visualisations – vertical axis (hues) is used to visualise the predicted values and horizontal axis (whiteness) is used to visualise the prediction error. In the case of categorical variables, a circular legend is used – perimeter (hues) defines the taxonomic space and radial distance represents the confusion. The methods are illustrated using two examples: (a) interpolation of soil thickness using regression-kriging and (b) fuzzy k-means classification of landforms classes.