PROJECT OVERVIEW
While significant work has been done in the practical applications of improving the visual vocabularies of scientific visualization, the majority of this work has focused on the perceptual components of visual elements, how they interact, and whether they improve speed or performance in accurately reading data visualizations. However, as data becomes increasingly more complex, layered, multivariate, and especially volumetric and time varying, such perceptual metrics are increasingly difficult to measure, while other, less well-defined variables become more important to consider.
We see one of these essential variables as Affect: the holistic emotional impact that an image or visualization produces in a viewer. Much has been done to investigate how we might quantify the effects of Affect, particularly in advertising, the arts, and communication.
This research thrust focuses on how we might operationalize Affect theory from the arts to improve environmental visualization through form, texture, and color relationships. How do these relationships evolve over time? How do they influence the preconceived notions different audiences form about a visualization? How can affective relationships between visualization components improve analysis capability amongst scientists and researchers?
These and many more questions form the basis of our work in this area, along with the goal of more closely integrating arts and visualization practice by bringing artists and designers into the process.
Natural Color
If color is the “first principle of place,” we might ask not only what the net effect of color is on understanding and emotional connection with a data-image, but also what color is doing, what agency it possesses in its ontological encounter with, specifically, the human visual system and its socio-cultural constructs.
Parsing the detail and complexity of the world draws on evolved traits of our visual systems: top-down and bottom-up attentional processing, strategic visual grouping, exploration, value assignment, and hierarchical determinants. As many of these tasks rely on color, we’re able to perceive minute differences in natural hues, especially green.
Pine needles, prairie grasses, mesquite bushes, cacti, moss, juniper trees: each reflect distinct shades of green which vary drastically, even within the same plant. Primary hues are much less common in nature than secondary or tertiary hues, and are employed sparingly by flora and fauna alike to contrast with a largely analogous backdrop and to signal danger, spread seeds, or attract a mate or pollinator.
When disembodied and decontextualized from their sources, do these myriad greens continue to index their botanical origins? Devoid of form, do they possess the same agency, retrieve the same memories, produce the same sense of Place?
Decoding a scientific visualization, or any other kind of information-dense visual scene, draws on the same color-based evolved capabilities to accomplish similar analysis and decision-making tasks. Color contributes to the experience of the image; another datapoint that provides information and subtext for the “text” of the visualization, producing a set of relationships specific to that image.
By appropriating the earth’s palette, we might strategically leverage our innate sense-making operations to compose richer, more effective, and less cacophonous visualizations—a fruitful avenue which remains untapped thus far in both scientific and geographic communities.
Here, 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.
