Exploring Spatio-Temporal Data Modeled as Dynamic Weighted Relations

Michael Burch, Michael Raschke, Daniel Weiskopf
2013 Deutsche Jahrestagung für Künstliche Intelligenz  
Eye tracking studies lead to spatio-temporal data in the form of gaze trajectories that show the behavior of gaze positions over time. Such data can be modeled as a dynamic graph that expresses the transitions of gaze positions between Areas of Interest (AOIs) by time-varying weighted relations. Moreover, a hierarchical organization of the AOIs may be of interest, resulting in a dynamic compound AOI digraph. Traditionally, this kind of time-based relational data is represented by animated
more » ... ink diagrams that are laid out with respect to a list of aesthetic graph drawing criteria. In our work, we propose a visual metaphor for displaying relational data that uses space-filling circle sectors to encode dynamic relations between hierarchy elements. The idea benefits from the fact that dynamic compound digraphs can be visualized with reduced visual clutter compared to node-link diagrams for dense graphs. Finally, we illustrate how interaction methods can be used to explore a dataset for trends, countertrends, and/or anomalies.
dblp:conf/ki/BurchRW13 fatcat:ypoog4yx7bablepzcecmgikiq4