Building a perceptual visualization architecture

Journal article

C. G. Healey
Behaviour and Information Technology, vol. 19(5), 2000, pp. 349-366

View PDF Semantic Scholar DBLP DOI


Healey, C. G. (2000). Building a perceptual visualization architecture. Behaviour and Information Technology, 19(5), 349–366.

Healey, C. G. “Building a Perceptual Visualization Architecture.” Behaviour and Information Technology 19, no. 5 (2000): 349–366.

Healey, C. G. “Building a Perceptual Visualization Architecture.” Behaviour and Information Technology, vol. 19, no. 5, 2000, pp. 349–66.


Scientific datasets are often difficult to analyse or visualize, due to their large size and high dimensionality. A multistep approach to address this problem is proposed. Data management techniques are used to identify areas of interest within the dataset. This allows the reduction of a dataset's size and dimensionality, and the estimation of missing values or correction of erroneous entries. The results are displayed using visualization techniques based on perceptual rules. The visualization tools are designed to exploit the power of the low-level human visual system. The result is a set of displays that allow users to perform rapid and accurate exploratory data analysis. In order to demonstrate the techniques, an environmental dataset being used to model salmon growth and migration patterns was visualized. Data mining was used to identify significant attributes and to provide accurate estimates of plankton density. Colour and texture were used to visualize the significant attributes and estimated plankton densities for each month for the years 1956-1964. Experiments run in the laboratory showed that the chosen colours and textures support rapid and accurate element identification, boundary detection, region tracking and estimation. The result is a visualization tool that allows users to quickly locate specific plankton densities and the boundaries they form. Users can compare plankton densities to other environmental conditions like sea surface temperature and current strength. Finally, users can track changes in any of the dataset's attributes on a monthly or yearly basis.