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    Visualizing spatial correlations in large 3D ensembles is now feasible. Adaptive sampling within chord diagrams with hierarchical edge bundling efficiently estimates correlations, reducing computational constraints.

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    Area of Science:

    • Data Visualization
    • Scientific Computing
    • Computational Geometry

    Background:

    • Visualizing spatial correlations in large 3D ensembles presents significant memory and time challenges.
    • Pre-computing all pairwise correlations is computationally infeasible for large datasets.

    Purpose of the Study:

    • To develop an efficient method for visualizing spatial correlations in large 3D ensembles.
    • To overcome computational constraints associated with analyzing extensive datasets.

    Main Methods:

    • Embedding adaptive correlation sampling into chord diagrams with hierarchical edge bundling.
    • Utilizing space-filling curves for entity arrangement and Bayesian optimal sampling for correlation estimation.
    • Implementing GPU-accelerated linear and non-linear correlation measures.

    Main Results:

    • The proposed method effectively alleviates memory and time constraints in correlation analysis.
    • Hierarchical edge bundling reduces visual clutter, highlighting key correlation structures.
    • GPU implementations enable analysis of correlations in ensembles up to 1000 members.

    Conclusions:

    • The novel approach significantly enhances the feasibility of visualizing spatial correlations in large 3D ensembles.
    • The method provides efficient context and focus views for detailed correlation analysis.
    • GPU acceleration makes complex correlation analysis accessible for massive datasets.