Name
Title | ||
|---|---|---|
Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives |
Abstract | ||
|---|---|---|
While information visualization frameworks and heuristics have traditionally been reluctant to include acquired codes of meaning, designers are making use of them in a wide variety of ways. Acquired codes leverage a user's experience to understand the meaning of a visualization. They range from figurative visualizations which rely on the reader's recognition of shapes, to conventional arrangements of graphic elements which represent particular subjects. In this study, we used content analysis to codify acquired meaning in visualization. We applied the content analysis to a set of infographics and data visualizations which are exemplars of innovative and effective design. 88% of the infographics and 71% of data visualizations in the sample contain at least one use of figurative visualization. Conventions on the arrangement of graphics are also widespread in the sample. In particular, a comparison of representations of time and other quantitative data showed that conventions can be specific to a subject. These results suggest that there is a need for information visualization research to expand its scope beyond perceptual channels, to include social and culturally constructed meaning. Our paper demonstrates a viable method for identifying figurative techniques and graphic conventions and integrating them into heuristics for visualization design. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TVCG.2015.2467321 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
data analysis,data visualisation,content analysis,data visualization,figurative visualization,graphic elements,infographics,information visualization,perceptual primitives,user experience,visualization design,Design Methodologies,Illustrative Visualization,Taxonomies,Visual Design | Graphics,Computer vision,Communication design,Data visualization,Information visualization,Infographic,Computer science,Visualization,Heuristics,Artificial intelligence,Computer graphics | Journal |
Volume | Issue | ISSN |
22 | 1 | 1077-2626 |
Citations | PageRank | References |
5 | 0.43 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lydia Byrne | 1 | 7 | 0.78 |
| Daniel Angus | 2 | 5 | 0.43 |
| Janet Wiles | 3 | 72 | 6.12 |