Humans are visual animals and our understanding of the world is largely in the context of vision and visual imagination. Accordingly, a huge portion of our brain is dedicated to the act of seeing. Put your hand on the back of your head. The area it covers is roughly the amount of your brain cortex that is dedicated to simply processing (as opposed to understanding and acting upon) the basic elements of the visual world.
With this cognitive emphasis on visual understanding it’s not surprising that we largely communicate our most complex concepts visually. And no where is this more true then in science.
Scientists strive to understand the data that describes an incredibly complex facet of the world to a point where it can be communicated simply and clearly with as few visual elements as possible.
But what happens when the complexity of the data you are trying to understand beings to approach the complexity of the phenomena it describes?
This is not an abstract question but problem increasingly faced by contemporary scientists. As sensors become smaller; as computer memory and processing had become cheaper, faster and more abundant; and as the subjects of study become more intricate the amount and the complexity of data that needs to be processed explodes.
Add to this a new the phenomena of data it’s self being what needs to be understood. Consider the vast amounts of data being generated and deposited on the internet ever second. Unlike typical datasets that are gathered with a specific questions in mind this data is unattached to any original question. This vast, mysterious swarm of data is describing something important about us — but what?
So the question becomes: How do you visually comunicate huge amounts of complex data so that you can even being to understand that data its self?
Go check out Visual Complexity and you’ll start to find out.
It’s a site that is dedicated to pooling information about the tools and methods of visualizing complex data sets and it’s as beautiful as it is fascinating.
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