Prediction of data visibility in two-dimensional scatterplots. (April 2017) - Urribarri, Dana K; Castro, Silvia M
The result of a visualization process depends on the user's decisions along it. With the intention of accelerating this process and guaranteeing an appropriate visualization of the data, we are looking to semi-automatize the process to help the users with the decision-making along it. To contribute to this semi-automation, it is useful to have metrics that characterize different important aspects of the visualization techniques, such as data representation visibility. Besides, scatterplots are a widely used technique to visualize scalar datasets. In this context, this work presents a metric that evaluates data representation visibility considering glyph visibility in scatterplots. We defined a metric that estimates the proportion of glyphs that will be visible regardless of the drawing order, and it depends on the number of items in the dataset, the size of the window, and the size of the glyphs that will represent the data. To define and approximate the metric, we experimented with several random datasets for which both dimensions followed a normal distribution. This metric constitutes an alternative to characterize scatterplots and collaborates in the semi-automation of the user's decisions along the visualization process.