. Left: the TransGraph of the earthquake data set with dynamic tracking. We select a volume region corresponding to the earthquake's epicenter at time step 34, which is highlighted in its original color saturation in (a). Right: (a) to (f) are the dynamic tracking results in the volume at selected time steps 34, 49, 69, 94, 124, and 179, respectively. These images show the propagation of data transition over time. The corresponding nodes (states) and edges (transitions) at those time steps are highlighted in red and black, respectively, in the TransGraph. Abstract-A fundamental challenge for time-varying volume data analysis and visualization is the lack of capability to observe and track data change or evolution in an occlusion-free, controllable, and adaptive fashion. In this paper, we propose to organize a timevarying data set into a hierarchy of states. By deriving transition probabilities among states, we construct a global map that captures the essential transition relationships in the time-varying data. We introduce the TransGraph, a graph-based representation to visualize hierarchical state transition relationships. The TransGraph not only provides a visual mapping that abstracts data evolution over time in different levels of detail, but also serves as a navigation tool that guides data exploration and tracking. The user interacts with the TransGraph and makes connection to the volumetric data through brushing and linking. A set of intuitive queries is provided to enable knowledge extraction from time-varying data. We test our approach with time-varying data sets of different characteristics and the results show that the TransGraph can effectively augment our ability in understanding time-varying data.
Gu and Wang REF compute time-dependent state transition probabilities for volumetric data and visualize a 3D view of the volume together with a 2D graph representation of the transitions.
TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Medicine Computer Science