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

TL;DR

This paper introduces an adaptive sampling method integrated with hierarchical chord diagrams and GPU acceleration to efficiently visualize complex 3D spatial correlations, enabling detailed focus+context analysis in large ensembles.

Contribution

It presents a novel adaptive correlation sampling approach combined with hierarchical edge bundling and GPU-based computation for scalable 3D correlation visualization.

Findings

Efficient estimation of maximum correlations using Bayesian sampling.

Hierarchical edge bundling reduces visual clutter in correlation diagrams.

GPU implementations enable analysis of large ensembles with 1000 members.

Abstract

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.