Visual analytics of ensemble data using coupled subspaces
In this work, we introduce a novel visual analysis method to explore correlations between locations and realizations of ensemble data with large realization set. In our approach, the ensemble data are transformed into a matrix with two dimensions: location and realization. The matrix is subdivided i...
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| Published in | Journal of visualization Vol. 27; no. 5; pp. 867 - 884 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1343-8875 1875-8975 |
| DOI | 10.1007/s12650-024-00976-0 |
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| Abstract | In this work, we introduce a novel visual analysis method to explore correlations between locations and realizations of ensemble data with large realization set. In our approach, the ensemble data are transformed into a matrix with two dimensions: location and realization. The matrix is subdivided into cells which indicate behaviors of corresponding location-realization combinations. Biclustering is employed to simultaneously partition the location domain and the realization set, the two dimensions of the matrix, into several subspaces, where every intersection subspace shares similar coordinated behaviors. With the matrix subspaces and their coordinated behaviors, a visual analytics workflow is designed to support visualization and analysis of location-realization correlations and their variation across different location and realization. Case studies show that our system is able to reveal location-realization correlations, such as the opposite behaviors of realization subspaces in different regions, which are normally difficult to be discovered by previous methods.
Graphical abstract |
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| AbstractList | In this work, we introduce a novel visual analysis method to explore correlations between locations and realizations of ensemble data with large realization set. In our approach, the ensemble data are transformed into a matrix with two dimensions: location and realization. The matrix is subdivided into cells which indicate behaviors of corresponding location-realization combinations. Biclustering is employed to simultaneously partition the location domain and the realization set, the two dimensions of the matrix, into several subspaces, where every intersection subspace shares similar coordinated behaviors. With the matrix subspaces and their coordinated behaviors, a visual analytics workflow is designed to support visualization and analysis of location-realization correlations and their variation across different location and realization. Case studies show that our system is able to reveal location-realization correlations, such as the opposite behaviors of realization subspaces in different regions, which are normally difficult to be discovered by previous methods. In this work, we introduce a novel visual analysis method to explore correlations between locations and realizations of ensemble data with large realization set. In our approach, the ensemble data are transformed into a matrix with two dimensions: location and realization. The matrix is subdivided into cells which indicate behaviors of corresponding location-realization combinations. Biclustering is employed to simultaneously partition the location domain and the realization set, the two dimensions of the matrix, into several subspaces, where every intersection subspace shares similar coordinated behaviors. With the matrix subspaces and their coordinated behaviors, a visual analytics workflow is designed to support visualization and analysis of location-realization correlations and their variation across different location and realization. Case studies show that our system is able to reveal location-realization correlations, such as the opposite behaviors of realization subspaces in different regions, which are normally difficult to be discovered by previous methods. Graphical abstract |
| Author | Mei, Xiyao Yuan, Xiaoru Hong, Fan Shao, Hanning Pang, Alex |
| Author_xml | – sequence: 1 givenname: Fan surname: Hong fullname: Hong, Fan organization: National Key Laboratory of General Artificial Intelligence and School of Intelligence Science and Technology, Peking University – sequence: 2 givenname: Hanning surname: Shao fullname: Shao, Hanning organization: National Key Laboratory of General Artificial Intelligence and School of Intelligence Science and Technology, Peking University – sequence: 3 givenname: Xiyao surname: Mei fullname: Mei, Xiyao organization: National Key Laboratory of General Artificial Intelligence and School of Intelligence Science and Technology, Peking University – sequence: 4 givenname: Alex surname: Pang fullname: Pang, Alex organization: Computer Science Department, University of California – sequence: 5 givenname: Xiaoru surname: Yuan fullname: Yuan, Xiaoru email: xiaoru.yuan@pku.edu.cn organization: National Key Laboratory of General Artificial Intelligence and School of Intelligence Science and Technology, Peking University, National Engineering Laboratory for Big Data Analysis and Application, Peking University |
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| Copyright | The Visualization Society of Japan 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| SubjectTerms | Classical and Continuum Physics Computer Imaging Correlation Data analysis Engineering Engineering Fluid Dynamics Engineering Thermodynamics Heat and Mass Transfer Pattern Recognition and Graphics Regular Paper Subspaces Two dimensional analysis Vision Workflow |
| Title | Visual analytics of ensemble data using coupled subspaces |
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