Data visualization is the vehicle by which Big Data can be humanized, or at least re-rendered in a format that can be instantly digested by our powerful pattern recognition modules built deep in our brains. In fact, our brains are evolved to primarily do pattern recognition for particular contexts—most of those contexts that don’t involve facial recognition are all about spatial recognition. Powerful visualization tools take advantage of this.
The image below, as data, would be a vast sheet of numbers—X,Y,Z coordinates with timestamps and possibly other sundry information. To the eye, it would be a meaningless barrage of data that the brain would attempt to make sense of (apply pattern to) but to most it would just be a jumble of characters. However, once put into a 3D space, plotted out, it takes on an entire new meaning.
The image is taken from an article on FlowingData and it displays a point-cloud of 11.3m player deaths in the video game Just Cause 2.
As this is a point-map of player deaths from impacting terrain or other objects in game numerous features become visible: mountain roads, skyscrapers, buildings, …a blimp.
But it’s much more than that. This is also a map of how people engage the game. It’s a visual map of where they go and how they get there—and to a certain extent it even displays the risky behavior that people get up to in certain regions. We can make all sorts of interesting inferences about the number of people who visit (and jump from?) that blimp. We can see that people like to visit those skyscrapers in the middle of the town as well. And we can even tell that the winding mountain road is particularly dangerous.
Now imagine that we took data like this, compiled from GPS and time stamps of vehicular crashes in an entire state. Certainly we could upload the data to a service like Google’s Public Data Explorer with its extremely powerful visualization and sharing engine but we could also render it similarly to the above point cloud.
With this sort of data, city planners could see where the most dangerous spots in the city are at a glance. By choosing variables (or queries) they could set spans of time and exposure, to see if certain seasons cause particular regions to be more or less trafficked or hazardous. They might use that to choose to update road traction, signage, signaling, guard rails, etc. in order to enhance citizen safety on the road.
We’ve seen similar contexts used to streamline traffic with a project between IBM Research and California Department of Transportation to predict traffic congestion. Also, as smartphone mapping gets much smarter and more visual this sort of thing could be compiled and rapidly personally generated for mobile people to find their way around the city using current (and predicted) conditions.
All of the above are gigantic datasets, mostly containing superfluous and difficult to discern information. Yet, the moment that we distill it with the alchemy of visualization patterns emerge that our brains are well designed to discern; possibly well before some of our most advanced Big Data sifting algorithms might do so.