| DOI: | 10.2172/1235087
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| 报告号: | LBNL--1003958
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| 报告题名: | Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis |
| 作者: | Hoa T. Nguyen; Stone, Daithi; E. Wes Bethel
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| 出版年: | 2016
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| 发表日期: | 2016-01-01
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| 总页数: | 7
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| 国家: | 美国
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| 语种: | 英语
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| 英文关键词: | nonparmetric statistics
; visualizing data variation
; information interfaces and presentation
; multi-variate
; multi-resolution projection
; simulation and modeling
; simulation output analysis
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| 英文摘要: | An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging. |
| URL: | http://www.osti.gov/scitech/servlets/purl/1235087
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| Citation statistics: |
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| 资源类型: | 研究报告
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/42309
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| Appears in Collections: | 过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响
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| 1235087.pdf（808KB） | 研究报告 | -- | 开放获取 | | View
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| Recommended Citation: | |
Hoa T. Nguyen,Stone, Daithi,E. Wes Bethel. Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis. 2016-01-01.
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