Environmental Visualization: Moving Beyond the Rainbows
Stephanie Zeller, Francesca Samsel, Paul Navártil · Practice and Experience in Advanced Research Computing · 2020
Pseudo-coloring is a well-established, fundamental tool for visualizing scientific data. As the size and density of data grows, increasingly more discriminatory power is required to extract optimum feature resolution. The environmental community, in particular, relies heavily on this technology to dissect and interpret a huge variety of visual data. These scientists often turn to traditional rainbow colormaps, despite their well-documented deficiencies in rendering dense detail. A popular default, the desaturated rainbow’s non-monotonically varying luminance range misrepresents data. Despite increasing overall feature resolution, this variance creates hue simultaneity and vibration, introducing false artifacts, hindering exploration of swaths of data and impeding analysis [13, 21, 26]. Drawing on artistic color theory, we hypothesized the desaturated rainbow could be improved by increasing luminance ranges, decreasing saturation, and employing hue-cycling to boost discriminatory power. These adjusted maps exhibit algorithmically corroborated higher feature resolve, a primary objective of all scientists interviewed, without distorting data in discordant false coloring. Our studies indicate that our maps are preferred by these domain scientists, thereby providing a potential alternative for effective, human-centric colormapping.