AbstractCommon visualisation techniques such as bar-charts and scatter-plots are not sufficient for visual analysis of large sets of complex multidimensional data. Technological advancements have led to a proliferation of novel visualisation tools and techniques that attempt to meet this need. A crucial requirement for efficient visualisation tool design is the development of objective criteria for visualisation quality, informed by research in human perception and cognition.
This thesis presents a multidisciplinary approach to address this requirement, underpinning the design and implementation of visualisation software with the theory and methodology of cognitive science. An opening survey of visualisation practices in the research environment identifies three primary uses of visualisations: the detection of outliers, the detection of clusters and the detection of trends. This finding, in turn, leads to a formulation of a cognitive account of the visualisation comprehension processes, founded upon established theories of visual perception and reading comprehension. Finally, a psychophysical methodology for objectively assessing visualisation efficiency is developed and used to test the efficiency of a specific visualisation technique, namely an interactive three-dimensional scatterplot, in a series of four experiments. The outcomes of the empirical study are three-fold. On a concrete applicable level, three-dimensional scatterplots are found to be efficient in trend detection but not in outlier detection. On a methodological level, ‘pop-out’ methodology is shown to be suitable for assessing visualisation efficiency. On a theoretical level, the cognitive account of visualisation comprehension processes is enhanced by empirical findings, e.g. the significance of the learning curve parameters. All these provide a contribution to a ‘science of visualisation’ as a coherent scientific paradigm, both benefiting fundamental science and meeting an applied need.
|Date of Award||Oct 2011|
|Supervisor||Kenneth Scott-Brown (Supervisor), James Bown (Supervisor) & Andrea Szymkowiak (Supervisor)|
- Cognitive psychology
- Information visualisation
- Visual analytics
Measuring comprehension of abstract data visualisations
Shovman, M. (Author). Oct 2011
Student thesis: Doctoral Thesis