Affective Palettes for Scientific Visualization: Grounding Environmental Data in the Natural World

Francesca Samsel, Greg Abram, Stephanie Zeller, Daniel Keefe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE VIS Arts Program, VISAP 2021
EditorsYoon Chung Han, Charles Perin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages20-34
Number of pages15
ISBN (Electronic)9781665440219
DOIs
StatePublished - 2021
Event2021 IEEE VIS Arts Program, VISAP 2021 - Virtual, Online, United States
Duration: Oct 24 2021Oct 29 2021

Publication series

NameProceedings - 2021 IEEE VIS Arts Program, VISAP 2021

Conference

Conference2021 IEEE VIS Arts Program, VISAP 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/24/2110/29/21

Bibliographical note

Funding Information:
This work is supported by the National Science Foundation under awards IIS-1704604 & IIS-1704904. We would like to thank the following scientists for sharing their data: Mark Petersen, Phillip Wolfram, and Xylar Asay-Davis (E3SM, LANL), Rod Linn, John Patchett (FIRETEC, LANL), Jiaxu Zhang (UW), Wilbert Wiejer, and William Daughton (LANL).

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Affect
  • Art
  • Color
  • Environment
  • Nature
  • Scientific Visualization

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