Progressive multiples for communication-minded visualization

This paper describes a communication-minded visualization called progressive multiples that supports both the forensic analysis and presentation of multidimensional event data. We combine ideas from progressive disclosure , which reveals data to the user on demand, and small multiples [21], which allows users to compare many images at once. Sets of events are visualized as timelines. Events are placed in temporal order on the x-axis, and a scalar dimension of the data is mapped to the y-axis. To support forensic analysis, users can pivot from an event in an existing timeline to create a new timeline of related events. The timelines serve as an exploration history, which has two benefits. First, this exploration history allows users to backtrack and explore multiple paths. Second, once a user has concluded an analysis, these timelines serve as the raw visual material for composing a story about the analysis. A narrative that conveys the analytical result can be created for a third party by copying and reordering timelines from the history. Our work is motivated by working with network security administrators and researchers in political communication. We describe a prototype that we are deploying with administrators and the results of a user study where we applied our technique to the visualization of a simulated epidemic.