Abstract
Understanding air quality data is important for many environmental and climatic applications that are crucial to our daily life. It is often challenging to handle these 3D datasets due to their large number of time steps and multiple interactional chemicals. In this paper, we design and generate knowledge templates for visually analyzing multi-field, time-varying 3D air quality data. Specifically, we design a set of multi-level knowledge templates to capture important statistical data properties based on the distribution features of air quality data. We develop a fast template synthesis method to generate suitable templates according to user intentions. We have also developed an integrated visualization system for visually comparing multiple templates and volume datasets. Our approach can automatically synthesize suitable knowledge templates to assist the visualization and analysis tasks of multiple related datasets. The experimental results demonstrate that appropriate knowledge templates can significantly improve the exploration and analysis processes of air quality data by encapsulating long-time range knowledge into understandable visual formats.
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Lu, A., Chen, W., Ribarsky, W., Ebert, D. (2009). Year-Long Time-Varying 3D Air Quality Data Visualization. In: Ras, Z.W., Ribarsky, W. (eds) Advances in Information and Intelligent Systems. Studies in Computational Intelligence, vol 251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04141-9_14
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DOI: https://doi.org/10.1007/978-3-642-04141-9_14
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