Facet Views of Varying Emphasis

Often, analysts are required to study multiple perspectives of their data simultaneously. For example, space, time, and descriptive attributes. This is especially difficult when looking at small multiples of data. Working with data analysts at West Midlands Police, we aimed to overcome this issue by superimposing these perspectives on top of each other, rather than the traditional juxtaposition (placing them side-by-side). This approach defies conventional wisdom and likely results in visual and informational clutter. For this reason we propose designs at three levels of abstraction for each perspective (see below).

FaVVEs Design Framework

Chart 1 – Left – The abstraction framework for FaVVEs. Right – by combining different abstraction levels, analysts can change their focus depending on their task.

The videos below show both the design concept and the FaVVEs prototype, which I developed in Java with the processing.org graphics library. It shows how we can pack a vast amount of data into a small amount of space, whilst still maintaining visualisation legibility. Through interaction, analysts can flexibly vary the abstraction level, bringing certain perspectives into, or out of, focus.