This paper describes a new method for visualizing complex information spaces as painted images. Scientific visualization converts data into pictures that allow viewers to "see" trends, relationships, and patterns. We introduce a formal definition of the correspondence between traditional visualization techniques and painterly styles from the Impressionist art movement. This correspondence allows us to apply perceptual guidelines from visualization to control the presentation of information in a computer-generated painting. The result is an image that is visually engaging, but that also allows viewers to rapidly and accurately explore and analyze the underlying data values. We conclude by applying our technique to a collection of environmental and weather readings, to demonstrate its viability in a practical, real-world visualization environment.
Christopher G. Healey