Evaluating Overall Quality of Graph Visualizations Indirectly and Directly

Visualization is one of the popular methods that are used to explore and communicate complex non-visual data. However, representing non-visual data in a visual form does not automatically make the process of exploration and communication effective. The same data can be visualized in many different ways and different visualizations affect the process differently. Therefore, it is important to have the resultant visualizations evaluated so that their quality in conveying the embedded information to the end users can be understood. In designing an evaluation study, at least three issues need to be addressed: what kind of quality is to be evaluated?What methods are to be used? And what measures are to be used? A range of methods and measurements have been used to evaluate visualizations in the literature. Overall quality is often considered as a multidimensional construct and the elements of the construct have limitations in evaluating overall quality. In this chapter, we introduce two one-dimensional measures. The first one is an indirect measure called visualization efficiency that is based on task performance and mental effort measures, while the second is a direct measure that is based on aesthetic criteria. These new measures take into consideration the elements of its corresponding multidimensional construct and combine them into a single value. We review related work, explain how these measures work and discuss user studies that were conducted to validate them.