Affective Palettes for Scientific Visualization: Grounding Environmental Data in the Natural World
Francesca Samsel, G. L. Abram, Stephanie Zeller, Daniel F. Keefe · 2021
As computing capacity increases and data grows in both size and complexity, we are capable of understanding our surroundings with increasing nuance. Visualizing this often-multivariate environmental data presents complex visual scenes to be navigated, parsed, analyzed, and communicated. We draw from both the natural world and artistic color theory to present 1) a new color system, designed to establish an affective connection between big environmental data and its original source material, 2) a tool for extracting these workable palettes from natural imagery, and 3) a selection of pre-made linear colormaps and discrete color sets drawn from natural environments.