Figurative frames: A critical vocabulary for images in information visualization. (January 2019)

• Record Type: Journal Article
• Authors: Byrne, Lydia; Angus, Daniel; Wiles, Janet
• Is Part Of: Information visualization. Volume 18:Number 1(2019)
• Page Start: 45
• Page End: 67
• Publication Date: 2019-01
• Subjects: Information visualization -- figurative visualization -- conceptual model -- figurative frames -- information density; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/1473871617724212
• Languages: English
• ISSNs: 1473-8716

Abstract

Critical analyses provide information visualization practitioners with insight into the range and suitability of different techniques for visualization. Theory provides the necessary models and vocabulary to deconstruct, explain and classify visualizations, allowing the analysis and comparison of alternate designs, and evaluation of their success. While the critical vocabulary for information visualization in general is well developed, the same cannot be said for 'hybrid' information visualizations which combine abstract representation of data with figurative elements such as illustrations. Figurative elements are widely used in information visualization in practice and are increasingly recognized as beneficial for memorability. However, the information encoded by a figurative image and how that information contributes to the overall content of the visualization lacks robust definition within visualization theory. To support critical analysis of hybrid visualization, we provide a model of the information content of a figurative image, which we call the figurative frame model . We use the model to classify hybrid visualizations along two dimensions: information density in the images (defined as the number of features and preserved measurements) and integration of figurative and abstract forms of representation. The new vocabulary for analysing hybrid visualizations reveals how the figurative images expand the expressiveness of information visualization by integrating descriptive and abstract information and allows the formulation of new measures of visualization quality which can be applied to hybrid visualizations.