C. G. Healey Seminar Abstract

MURI Visualization Seminar
Friday, February 7, 4:00pm, 306 Soda Hall

Choosing Effective Colours for Data Visualization

Abstract:

Data visualization is a relatively new and rapidly expanding area of computer graphics. In its simplest form, visualization can be described as a mapping of data attributes onto visual features like spatial location, colour, shape, size, and so on. This leads to a number of important questions:

How can we choose the visual features to use for different data attributes?
How can we divide a visual feature to effectively represent the range of possible values for a given data attribute?

My talk will focus on the second question, specifically, how can we use colour to effectively represent a multi-valued data attribute? How do we allow rapid and accurate identification of individual data elements through the use of colour? What factors determine whether a "target" element's colour will make it easy to find, relative to differently coloured "non-target" elements? Finally, how many colours can we display at once, while still allowing for rapid and accurate target identification?

Previous research suggests that we need to consider three separate factors during colour selection: colour distance, linear separation, and colour category. We describe a simple method for measuring and controlling all of the above effects. Our method was tested by performing a set of target identification studies; we analysed the ability of thirty-eight observers to find a colour target in displays which contained differently coloured background elements. Results showed our method can be used to select a group of colours which will provide good differentiation between data elements during data visualization. I will conclude the talk with examples of applying our techniques to real-world visualization problems.