Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design

If you need an accessible version of this item, please email your request to digschol@iu.edu so that they may create one and provide it to you.
Date
2018
Language
American English
Embargo Lift Date
Department
Committee Members
Degree
Degree Year
Department
Grantor
Journal Title
Journal ISSN
Volume Title
Found At
ACM
Abstract

Continuous 'pseudocolor' maps visualize how a quantitative attribute varies smoothly over space. These maps are widely used by experts and lay citizens alike for communicating scientific and geographical data. A critical challenge for designers of these maps is selecting a color scheme that is both effective and aesthetically pleasing. Although there exist empirically grounded guidelines for color choice in segmented maps (e.g., choropleths), continuous maps are significantly understudied, and their color-coding guidelines are largely based on expert opinion and design heuristics--many of these guidelines have yet to be verified experimentally. We conducted a series of crowdsourced experiments to investigate how the perception of continuous maps is affected by colormap characteristics and spatial frequency (a measure of data complexity). We find that spatial frequency significantly impacts the effectiveness of color encodes, but the precise effect is task-dependent. While rainbow schemes afforded the highest accuracy in quantity estimation irrespective of spatial complexity, divergent colormaps significantly outperformed other schemes in tasks requiring the perception of high-frequency patterns. We interpret these results in relation to current practices and devise new and more granular guidelines for color mapping in continuous maps.

Description
item.page.description.tableofcontents
item.page.relation.haspart
Cite As
K. Reda, P. Nalawade, K. Ansah-Koi. Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design. Proceedings of CHI'18: ACM Conference on Human Factors in Computing Systems. 2018. ACM
ISSN
Publisher
Series/Report
Sponsorship
Major
Extent
Identifier
Relation
Journal
Source
Alternative Title
Type
Article
Number
Volume
Conference Dates
Conference Host
Conference Location
Conference Name
Conference Panel
Conference Secretariat Location
Version
Full Text Available at
This item is under embargo {{howLong}}