TY - JOUR
T1 - Nested Tracking Graphs
AU - Lukasczyk, Jonas
AU - Weber, Gunther
AU - Maciejewski, Ross
AU - Garth, Christoph
AU - Leitte, Heike
N1 - Publisher Copyright:
© 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
PY - 2017/6
Y1 - 2017/6
N2 - Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of features for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to each other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We demonstrate the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations.
AB - Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of features for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to each other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We demonstrate the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations.
KW - Categories and Subject Descriptors (according to ACM CCS)
KW - Data [Computer Graphics]: Data Structures—Graphs and Networks
UR - http://www.scopus.com/inward/record.url?scp=85022175109&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022175109&partnerID=8YFLogxK
U2 - 10.1111/cgf.13164
DO - 10.1111/cgf.13164
M3 - Article
AN - SCOPUS:85022175109
SN - 0167-7055
VL - 36
SP - 12
EP - 22
JO - Computer Graphics Forum
JF - Computer Graphics Forum
IS - 3
ER -