Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization

Christoph Neuhauser, Josef Stumpfegger, Rudiger Westermann

Research output: Contribution to journalArticlepeer-review


Visualizing spatial correlations in 3D ensembles is challenging due to the vast amounts of information that need to be conveyed. Memory and time constraints make it unfeasible to pre-compute and store the correlations between all pairs of domain points. We propose the embedding of adaptive correlation sampling into chord diagrams with hierarchical edge bundling to alleviate these constraints. Entities representing spatial regions are arranged along the circular chord layout via a space-filling curve, and Bayesian optimal sampling is used to efficiently estimate the maximum occurring correlation between any two points from different regions. Hierarchical edge bundling reduces visual clutter and emphasizes the major correlation structures. By selecting an edge, the user triggers a focus diagram in which only the two regions connected via this edge are refined and arranged in a specific way in a second chord layout. For visualizing correlations between two different variables, which are not symmetric anymore, we switch to showing a full correlation matrix. This avoids drawing the same edges twice with different correlation values. We introduce GPU implementations of both linear and non-linear correlation measures to further reduce the time that is required to generate the context and focus views, and to even enable the analysis of correlations in a 1000-member ensemble.

Original languageEnglish
Pages (from-to)1608-1623
Number of pages16
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number2
StatePublished - 1 Feb 2024


  • Chord diagrams
  • correlation sampling
  • ensemble analysis


Dive into the research topics of 'Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization'. Together they form a unique fingerprint.

Cite this