Browsing by Author "Shimabukuro, Mariana"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Abbreviating Text Labels on Demand
    (IEEE, 2017-10) Shimabukuro, Mariana; Collins, Christopher
    Long text labels is a known challenge in information visualizations.There are some techniques used in order to solve this problem like setting a very small font size. On the other hand, sometimes the font size is so small that the text can be difficult to read. Wrapping sentences, dropping letters and text truncation are some techniques do deal with this problem. In order to investigate a solution for labeling long words we ran a study on how people create and interpret word abbreviations. Based on the study data we designed a new algorithm to automatically make words as short as they need to fit the text. Examples applications of this algorithm are presented in this paper.
  • Thumbnail Image
    Item
    Perceptual Biases in Font Size as a Data Encoding
    (IEEE, 2017-07-04) Alexander, Eric; Chang, Chih-Ching; Shimabukuro, Mariana; Franconeri, Steven; Collins, Christopher; Gleicher, Michael
    Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word’s font with a word’s length, the number of letters it contains, or with the larger or smaller heights of particular characters (‘o’ vs. ‘p’ vs. ‘b’). We present a collection of empirical studies showing that such factors—which are irrelevant to the encoded values—can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases, and describe a strategy to mitigate them.