Visualizing Paths in Context

Data about movement through a space is increasingly becoming available for capture and analysis. In many applications, this data is captured or modeled as transitions between a small number of areas of interests, or a finite set of states, and these transitions constitute paths in the space. Similarities and differences between paths are of great importance to such analyses, but can be difficult to assess. In this work we present a visualization approach for representing paths in context, where individual paths can be compared to other paths or to a group of paths. Our approach summarizes path behavior using a simple circular layout, including information about state and transition likelihood using Markov random models, together with information about specific path and state behavior. The layout avoids line crossovers entirely, making it easy to observe patterns while reducing visual clutter. In our tool, paths can either be compared in their natural sequence or by aligning multiple paths using Multiple Sequence Alignment, which can better highlight path similarities. We applied our technique to eye tracking data and cell phone tower data used to capture human movement