Abstract
Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.
Highlights
A central problem in data visualization is to plot multivariate data into a single map that conveys valuable information about the data
We evaluate our temporal scatterplots by showing their effectiveness for various datasets, and by comparing our visualization to established techniques
The core of our work is to find an overall projection of the temporal and high-dimensional data, which is not addressed by the aforementioned scatterplot enhancement techniques
Summary
A central problem in data visualization is to plot multivariate data into a single map that conveys valuable information about the data. We present a technique to visualize temporal-multivariate data in a single static plot. The key idea is to find a projection plane onto which the high-dimensional data may be projected, so as to best present the data trajectories. After using this plane to embed the data in 2D, a subset of trajectories is selected and visualized with enhanced strokes to visually convey the overall temporal progression. The guiding assumption here is that time is not just another dimension of the data, but a unique dimension along which other multivariate data is coherent, and visually perceiving the progression of the data over time is the main need
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