美国国家科学基金会资助信息的交互式可视化分析

Shixia Liu, Nan Cao, Hao Lv
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引用次数: 12

摘要

本文提出了一个交互式可视化工具箱,用于导航和分析美国国家科学基金会(NSF)资助信息。我们的设计建立在树形图布局和堆叠图的基础上,为视觉导航和与NSF项目和提案的分层数据交互提供定制技术,支持视觉搜索和分析,并允许用户做出明智的决策。在这个可视化工具包中,我们提出了两种可视化技术来简化分层数据的导航:2.5维树图,使分层结构更容易被识别;标签树图,帮助用户清楚地了解结构的内容,并使矩形的内部面积对应于数据集的权重。在此基础上,采用增量布局的方法进行大规模的信息处理。改进的树图可视化有助于对静态资金数据进行可视化分析,并利用堆叠图对时间序列数据进行分析。通过这些可视化分析技术,可以快速识别出NSF的研究趋势和热门项目。主要贡献是展示了有效呈现和分析NSF资助数据的新方法。
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Interactive Visual Analysis of the NSF Funding Information
This paper presents an interactive visualization toolkit for navigating and analyzing the National Science Foundation (NSF) funding information. Our design builds upon the treemap layout and the stacked graph to contribute customized techniques for visually navigating and interacting with the hierarchical data of NSF programs and proposals, supporting visual search and analysis, and allowing the user to make informed decision. In this visualization toolkit, we propose two visualization techniques to simplify the navigation of the hierarchical data: 2.5 Dimensional treemaps to make the hierarchical structure more easily to be recognized, and labeled treemap to help the user to get a clear overview of the content of the structure and make the internal area of rectangles correspond to the weights of the data set. Furthermore, an incremental layout method is adopted to handle information on a large scale. The improved treemap visualization will help to visually analyze the static funding data and the stacked graph is utilized to analyze the time-series data. Through these visual analysis techniques, research trends of NSF, popular NSF programs are quickly identified. The primary contribution is a demonstration of novel ways to effectively present and analyze NSF funding data.
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