Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

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Tyson Neuroth, Martin Rieth, Myoungkyu Lee, Konduri Aditya, Jacqueline Chen, Kwan-Liu Ma

 View presentation:2022-10-19T19:48:00ZGMT-0600Change your timezone on the schedule page
2022-10-19T19:48:00Z

Exemplar figure, described by caption below — We decompose volume data hierachically based on isobands, connected components, and then restricted centroidal Voronoi tessellation of the connected components. These segments are then summarized with statistics and the data and the summaries are sorted so that each feature is contiguous on disk at each level. This provides an efficient method for out-of-core extraction to support efficient interactive visualization of the large multivariate data.

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Abstract

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features.