Visualizing spatial structures 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.

翻译：三维集合体中空间结构的可视化因需传达海量信息而极具挑战性。受限于内存与时间约束，预计算并存储所有域点对之间的相关性不可行。我们提出将自适应相关性采样嵌入具有层次化边缘捆绑的弦图中，以缓解这些约束。代表空间区域的实体通过空间填充曲线沿圆形弦布局排列，并采用贝叶斯最优采样高效估计不同区域任意两点间最大可能出现的相关性。层次化边缘捆绑可减少视觉混乱并突出主要相关结构。用户通过选择边缘触发焦点图，此时仅通过该边缘连接的两个区域被细化并在第二个弦布局中以特定方式排列。为可视化两个不同变量间不再对称的相关性，我们切换至展示完整相关矩阵，从而避免以不同相关值重复绘制同一条边。我们引入线性和非线性相关性度量的GPU实现，进一步减少生成上下文视图与焦点视图所需时间，甚至支持对包含1000个成员的集合体进行相关性分析。