FlowingData, if you don't already read them: start now

FlowingData has a nice post today rounding up 20 data visualizations related to crime and criminals. Most revolve around maps with temporal components shoehorned into a few. We love maps here at Graphient. We love them for how they visually describe space, and we really love them for the way they provide a fixed contextual grid for organizing other kinds of data. The map visualizations presented at FlowingData stop short of adding in other data and stick to simply reporting the facts of crime, but those maps could have been used to introduce and connect all sorts of other (possibly sensitive) socioeconomic data and maybe tell a deeper story about crime.

I have an art and design background. One of the key ideas in art (and design) is negative space. Negative space is the area of a drawing or sculpture or whatever that is not the subject. In the illustration bellow The negative space is everything that is not the flowers the vase or the table.

160k in art school education deployed here.

The importance of negative space in establishing things like scale becomes clear when you compare the A and B images. The negative space creates the context for the flowers, and the context in turn helps the viewer infer certain things about the flowers.

The same thing can be true when visualizing data. Placing data sets onto maps creates context around the data. The context allows us to see relationships. Similarly creating a visual timeline out of a list of events (or data points) lets us see better how those events relate by showing us the space between them. FreeTime uses a fixed contextual framework (time) to combine very different data sets. It is this context that allows FreeTime to transform data into information.