Data visualization leverages the same cognitive processing system that evolved to spot savanna cats skulking in tall grass, recognize emotions in other human faces, and distinguish between food that is and is not safe to eat. We’ve evolved to perceive the world, and as primates, a lot of that perception is visual. The visual system is incredibly fast and remarkably accurate. Noah Iliinsky provides a good, high-level description of the visual processing center in his article, “Why is Data Visualization So Hot“:
… fundamentally, our visual system is extremely well built for visual analysis. There’s a huge amount of data coming into your brain through your eyes; the optic nerve is a very big pipe, and it sends data to your brain very quickly (one study estimates the transmission speed of the optic nerve at around 9Mb/sec). Once that data arrives at the brain, it’s rapidly processed by sophisticated software that’s extremely good at tasks such as edge detection, shape recognition, and pattern matching.
How do we go from dodging danger to comprehending charts? First, we need to understand how photons get turned into shapes and color; how we group objects together based on visual cues; how we understand motion. Scientists have been studying the visual system for a long time, and while there is still more to learn, we know a lot.
We know that unconscious, low-level processing in the visual system quickly groups objects by properties such as color, size, and shape. We know that when we focus on one part of an image or a movie, we can be blind to other changes. We know that our brain fills in information, creating shapes where none exist.
How do we map that knowledge onto data? As we discussed earlier this semester, there are types of measurement: nominal, ordinal, interval, and ratio. Most data that you are interested in will be one of those four types. Mapping human capability to these levels of measurements is the key to visualizing data. For example, we can easily distinguish between the colors blue and red (at least most sighted people can). However, blue and red don’t have a natural ordering. There is no reason to think that something colored red is worth more or greater than something colored blue. Color is good at distinguishing members in a group, otherwise known as nominal measurements, but would be a poor choice for differentiating ordered elements, or ordinal measurements. For ordinal measurements, shades of grey work well. Shades of grey are easy to distinguish and have a natural ordering.
Data visualization relies on vision, but vision is just one sense. We have four others. Students, I want you to think about taste, touch, smell, and hearing. Can you create a “visualization” system for people who can’t see? I want you think about your senses. Pick a non-visual sense and do some research. Figure out what is and isn’t easy to distinguish within the boundaries of that sensory data, and create your own mapping from sensory experience to type of measurement.