This is a simple visualization of big data taken out of context. Nevertheless, what do you see when you look at this image?
Some of you may say: I see circles. Some may say: I see circles of different sizes, or I see the colors red, blue, purple, and yellow. And those who take an extra second will say: I see circles of different colors
This set of observations reflects the pre-attentive processes in visual perception. When we scan the visual scene we look at, we detect various visual features such as size, orientation, color, lines and line ends, contrasts, curvatures, movement, spatial location, and more. This process is quite fast, between 200 to 250 milliseconds. We tend to do it without really thinking about it. It is considered pre-attentive because it is done immediately before we focus our attention on anything specific in the visual scene. Such ability in our visual perception is extremely useful in searching and detecting targets we look for, and discriminating them from any other element possessing other features. For example, in the image above, if our task would be to find the largest red circle, it would be easy and we would get the correct answer very fast.
However, when it comes to visualizing big data, the objective is, and should be, to convey the big picture rather than have people search for specific items. In that case, we want to take advantage of the pre-attentive process to guide our attention to what could be more important and more interesting.
Thus, there could be another and completely different set of answers to the question what do you see in the image above. Some may say: I see a rising linear trend. Some may even say: I see some clusters within a rising linear trend.
This set of answers reflects the more attentive and constructive nature of visual perception. In such processes, we go beyond the basic features and construct new perceptions such as trends and shapes (often referred to as Emergent Features). In these processes, our visual perception is organized by principles such as the Gestalt Principles.
For example, the principles of Proximity and Similarity would help us perceive all the little purple circles, which are spatially close to each other as a cluster separate from the rest. The principle of Proximity and Continuity would help us perceive all the circles as making up a rising linear trend.
There is even evidence that perceiving the global structure of a visual scene tends to precede perception of any specific features. Findings suggest that people respond faster to perceiving the letter ‘H’ in both the left and the right elements below, compared to identifying from which characters the large ‘H’ is composed of (‘H’ in the left-hand stimulus, and ‘S’ in the right-hand stimulus). This is referred to as “Global Precedence”. In general, the global precedence hypothesis claims that the processing of a visual scene begins with attention to the global properties first followed by local as time progresses.
Perceiving global structures such as clusters and trends, among others, is to perceive the big picture. It is seeing the forest and not the trees. However, what does it all mean? To leverage the pre-attentive and attentive/constructive processes in visual perception into a meaningful big picture we must add the frame.
The framing could be the meaning of the pre-attentive features, such as the meaning of color and size. The framing could also be adding a system of axes providing additional meaning to the spatial configuration of all the elements.
In summary: when taking big data to convey the big picture, consider the following:
· We have preattentive processes whereby we perceive basic visual features fast and without effort.
· These may guide attention to perceive additional, emergent, features in the visual scene such as trends and shapes.
· Framing all the elements can facilitate the understanding of the perceived features and their organization.
Some relevant readings on information visualization with emphasis on human visual perception:
· Now you see itby Stephen Few, Analytics Press, 2009.
· Information Visualizationby Chaomei Chen, Springer Verlag, 2004.
· Information Visualizationby Colin Ware, Morgan Kaufmann, 2004.
· The Craft of Information Visualization: Readings and Reflectionsby Ben Bederson and Ben Shneiderman, editors, Morgan Kaufmann, 2003.
· Information Visualizationby Robert Spence, ACM Press, 2000.