Feature-Based Uncertainty Visualization

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Author(s): Keqin Wu (University of Maryland Baltimore County, USA)and Song Zhang (Mississippi State University, USA)
Copyright: 2014
Pages: 26
Source title: Innovative Approaches of Data Visualization and Visual Analytics
Source Author(s)/Editor(s): Mao Lin Huang (University of Technology, Sydney, Australia)and Weidong Huang (CSIRO, Australia)
DOI: 10.4018/978-1-4666-4309-3.ch004

Keywords: Data Analysis & Statistics / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this chapter, a suite of feature-based techniques is developed to address these issues. First, an interactive visualization tool for exploring scalar data with data-level, contour-level, and topology-level uncertainties is developed. Second, a framework of visualizing feature-level uncertainty is proposed to study the uncertain feature deviations in both scalar and vector data sets. With quantified representation and interactive capability, the proposed feature-based visualizations provide new insights into the uncertainties of both data and their features which otherwise would remain unknown with the visualization of only data uncertainties.

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