Glyph from Icon -- Automated Generation of Metaphoric Glyphs

TL;DR

This paper presents an automated method for generating metaphoric glyphs from icons, enhancing data visualization by systematically modifying visual variables to improve readability and domain relevance.

Contribution

The paper introduces the Glyph-from-Icon (GfI) approach, enabling automated, scalable creation of metaphoric glyphs using diffusion curves and perceptual evaluation.

Findings

Effective visual variable combinations identified for perception

Perceptual monotonicity and readability achieved

Application demonstrated on COVID-19 data visualization

Abstract

Metaphoric glyphs enhance the readability and learnability of abstract glyphs used for the visualization of quantitative multidimensional data by building upon graphical entities that are intuitively related to the underlying problem domain. Their construction is, however, a predominantly manual process. In this paper, we introduce the Glyph-from-Icon (GfI) approach that allows the automated generation of metaphoric glyphs from user specified icons. Our approach modifies the icon's visual appearance using up to seven quantifiable visual variables, three of which manipulate its geometry while four affect its color. Depending on the visualization goal, specific combinations of these visual variables define the glyphs's variables used for data encoding. Technically, we propose a diffusion-curve based parametric icon representation, which comprises the degrees-of-freedom related to the geometric and color-based visual variables. Moreover, we extend our GfI approach to achieve scalability of the generated glyphs. Based on a user study we evaluate the perception of the glyph's main variables, i.e., amplitude and frequency of geometric and color modulation, as function of the stimuli and deduce functional relations as well as quantization levels to achieve perceptual monotonicity and readability. Finally, we propose a robustly perceivable combination of visual variables, which we apply to the visualization of COVID-19 data.