Algorithms can exploit models and measures of human perception to generate scatterplot designs.
Scatterplots are widely used in various disciplines and areas beyond sciences to visually communicate relationships between two data variables. Yet, very few users realize the effect the visual design of scatterplots can have on the human perception and understanding. Moreover, default designs of scatterplots often represent the data poorly, and manually fine tuning the design is difficult.
HIIT, Aalto and KTH researchers have recently found an algorithmic approach to automatically improve the design of scatterplots by exploiting models and measures of human perception.
See press release and project webpage.
Article: Micallef, L., Palmas, G., Oulasvirta, A. & Weinkauf, T. (2017). Towards Perceptual Optimization of the Visual Design of Scatterplots. IEEE Transactions on Visualization and Computer Graphics 23(6) : 1588-1599.
Contact persons: Luana Micallef, Antti Oulasvirta