Data visualization is a joint function of visual perception and computer graphics (Green, 1991). As data visualization is a tool to decode information and present them to people in the most understandable ways, the effectiveness and performance of data visualization cannot be evaluated without understanding the mechanism of human visual system (Dastani, 2002). In other word, people have to understand how we perceive data visually in order to know how to improve the accuracy and prevent bias. The study of perception has enormously advanced in the past three decades and its impact to modern data visualization becomes more and more significant (Ware, 2012). This essay is to discuss about the role of perception in visualization and why it is important.

An effective visualization depends on the capabilities of the human visual system, which requires the presentation of the data coincide with the perceptual structure of the visualization. Dastani (2002) established a process model of effective data visualization as shown in Figure 1.

The process of interpreting visual data starts with perception and the process of perception determines perceivable relations among visual elements on the basis of their visual attribute values (Dastani, 2002). The ten basic visual elements, i.e. perceptual building blocks, consist of position, length, direction, area, angle, shading, volume, curvature, and color saturation. (Cleveland & McGill, 1984). By performing one or more of these elementary perceptual tasks, the viewers extract quantitative information from the constructed graphs and translate them into meaningful conclusions. These visual perceptual elements are also used to determine the performance of a graph in terms of the graph’s effectiveness and accuracy (Green, 1991).

The patterns used to present the data directly impact how the data will be readily perceived by the viewers, therefore, understanding perception is important to the data visualization. If we can understand how perception works, we can translate the knowledge into guidelines for displaying information (Ware, 2012). By following the perception-based guidelines, we can present the data in effective ways and avoid misleading the viewers.

References:

Cleveland, W. S., & McGill, R. (1984, September). Graphical Perception: Theory, Experimentation,and Application to the Development of Graphical Methods. Journal of the American Statistical Association , 79, pp. 531-554.

Dastani, M. (2002). The Role of Visual Perception in Data Visualization. Journal of Visual Languages and Computing , 13, pp. 601-622.

Green, M. (1991). Toward a Perceptual Science of Multidimensional Data Visualization:Bertin and Beyond. ERGO/GERO Human Factors Science.

Ware, C. (2012). Information Visualization: Perception for Design. Elsevier.