The bedrock of visualization knowledge

Data visualization is not easy. Let's make that clear from the start. It should be genuinely viewed as a craft. It is a unique convergence of many different skills and requires a great deal of practice and experience, which clearly demands time and patience.

Above all, it requires a deep and broad knowledge across several traditionally discrete subjects, including cognitive science, statistics, graphic design, cartography, and computer science.

This multi-disciplinary recipe unquestionably makes it a challenging subject to master but equally provides an exciting proposition for many. This is evidenced by the field's popular participation, drawing people from many diverse backgrounds.

If we look at this subject convergence at a more summary level, data visualization could be described as an intersection of art and science. This combination of creative and scientific perspectives represents a delicate mixture. Achieving an appropriate balance between these contrasting ingredients is one of the fundamental factors that will determine the success or failure of a designer's work.

The art side of the field refers to the scope for unleashing design flair and encouraging innovation, where you strive to design communications that appeal on an aesthetic level and then survive in the mind on an emotional one. Some of the modern-day creative output from across the field is extraordinary and we'll see a few examples of this throughout the chapters ahead.

The science behind visualization comes in many shapes. I've already mentioned the presence of computer science, mathematics, and statistics, but one of the key foundations of the subject comes through an understanding of cognitive science and in particular the study of visual perception. This concerns how the functions of the eye and the brain work together to process information as visual signals.

One of the other most influential founding studies about visual perception emerged from the Gestalt School of Psychology in the early 1900s, specifically in the shape of the Laws of Perceptual Organization (http://www.interaction-design.org/encyclopedia/data_visualization_for_human_perception.html).

These laws provide an organized understanding about the different ways our eyes and brain inherently and automatically form a global sense of patterns based on the arrangement and physical attributes of individual elements.

Here, we can see two visual examples of Gestalt Laws.

On the left-hand side is a demonstration of the "Law of Similarity". This shows a series of rows with differently shaded circles. When we see this our visual processes instantly determine that the similarly shaded circles are related and part of a group that is separate and different to the non-shaded rows. We don't need to think about this and wait to form such a conclusion; it is a preattentive reaction.

Images republished from the freely licensed media file repository Wikimedia Commons, source: http://en.wikipedia.org/wiki/File:Gestalt_similarity.svg and http://en.wikipedia.org/wiki/File:Gestalt_proximity.svg

On the right-hand side is a demonstration of the "Law of Proximity". The arrangement of closely packed-together pairs of columns means we assume these to be related and distinct from the other pairings. We don't really view this display as six columns, rather we view them as three clusters or sets.

At the root of visual perception knowledge is the understanding that our visual functions are extremely fast and efficient processes whereas our cognitive processes, the act of thinking, is much slower and less efficient. How we exploit these attributes in visualization has a significant impact on how effectively the design will aid interpretation.

Consider the following examples, both portraying analysis of the placement of penalties taken by soccer players.

When we look at the first image, the clarity of the display allows us to instantly identify the football symbols, their position, and their classifying color. We don't need to think about how to interpret it, we just do. Our thoughts, instead, are focused on the consequence of this information: what do these patterns and insights mean to us? If you're a goalkeeper, you'll be learning that, in general, the penalty taker tends to place their shots to the right of the goal.

Image republished under the terms of "fair use", source: http://www.facebook.com/castrolfootball

By contrast, this second display's attempt to portray the same type of data presentation causes significant visual clutter and confusion. Rather than using a simple and relatively blank image like the previous one, this display includes strong colors and imagery in the background. The result is that our eyes and brain have to work much harder to spot the footballs and their colors because the data layer has to compete for attention with the background imagery. We are therefore unable to rely on the capabilities of our preattentive visual perception (determined by the Law of Similarity) because we cannot easily perceive the shapes and their attributes representing the data. This delays our interpretative processes considerably and undermines the effectiveness and efficiency of the communication exchange.

Image republished under terms of "fair use", source: http://www.mirror.co.uk/sport/football/euro-2012-where-italy-will-place-their-penalties-907506

This is just a single, simple example but it does reveal the significance of understanding and obeying visual perception laws when portraying our data.

When we design a visualization, we need to take advantage of the strengths of the visual function and avoid the disadvantages of the cognitive functions. We need to minimize the amount of thinking or "working out" that goes into reading and interpreting data and simply let the eyes do their efficient and effective job.

Through the pioneering studies and development of theories acquired and refined over many years by the Gestalt School of Psychology as well as influential academics and theorists like Jacques Bertin, Francis Anscombe, John W Tukey, Jock McKinlay, and William Cleveland, we now have a greater understanding of how to achieve effective and efficient visualization design.

There is still a great amount of empirical evidence to gather, studies to conduct, and firm answers to unearth, but the wealth of knowledge available to us is a significant help to remove an undue amount of instinct in our design work.