Visual Cluttering Reduction for Visualizing Large Spatio-temporal Data Sets

Visual analytic techniques are useful for studying patterns and relationships hidden in very large data sets. However, a common problem in visualizing large data sets is visual cluttering. When data get large, visual elements often get crowded, making it difficult for viewers to conduct analysis. In this paper, we present a novel method that reduces visual cluttering in spatial-temporal data visualization. This visualization technique consists of two vertically parallel axes to represent location and a horizontal axis to represent time. Locations are represented as location lines connecting data points on the two vertical axes. As the number of unique locations increase, the number of location lines increase as well, resulting in visual clutter. Our solution is to introduce a new edge bundling technique that reduces the number of crossed lines. We discuss the constraints and simplification of the edge bundling idea and demonstrate this method using two large data sets.