Temporal Merge Tree Maps: A Topology-Based Static Visualization for Temporal Scalar Data

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Wiebke Köpp, Tino Weinkauf

 View presentation:2022-10-20T20:45:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T20:45:00Z

Exemplar figure, described by caption below — A temporal merge tree map (top) is a static visualization of a time-dependent scalar field (bottom). Our method uses augmented merge trees to map the data samples of each time step to a vertical slice. An optimization scheme is employed to achieve a temporally coherent mapping. The shown Storms data set represents storm activity over Europe in December 1999 and contains 744 time steps, arranged from left to right. Cyclones can easily be identified as dark blue lines and compared with each other while still being shown in the context of the entire data.

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Abstract

Creating a static visualization for a time-dependent scalar field is a non-trivial task, yet very insightful as it shows the dynamics in one picture. Existing approaches are based on a linearization of the domain or on feature tracking. Domain linearizations use space-filling curves to place all sample points into a 1D domain, thereby breaking up individual features. Feature tracking methods explicitly respect feature continuity in space and time, but generally neglect the data context in which those features live. We present a feature-based linearization of the spatial domain that keeps features together and preserves their context by involving all data samples. We use augmented merge trees to linearize the domain and show that our linearized function has the same merge tree as the original data. A greedy optimization scheme aligns the trees over time providing temporal continuity. This leads to a static 2D visualization with one temporal dimension, and all spatial dimensions compressed into one. We compare our method against other domain linearizations as well as feature-tracking approaches, and apply it to several real-world data sets.