Perceptually Complementary Visualisations to Facilitate Understanding of Complex Data

Information visualisation is an essential tool that presents the data during exploration and analysis in a way that is understandable and not overwhelming. The current use of single visualisations is not sufficient to facilitate understanding of complex data. A single visual representation can convey particular features of a dataset, but may only provide a partial view, or may have limitations in what it can represent. To address this issue, we propose a new method that is using perceptually complementary visualisations to facilitate understanding of complex data. This doctoral dissertation proposes and evaluates perceptually complementary views of two data types.