Proceedings: GI 2016

Assessing the Readability of Stacked Graphs

Alice Thudt (University of Calgary), Jagoda Walny (University of Calgary), Charles Perin (University of Calgary), Fateme Rajabiyazdi (University of Calgary), Lindsay MacDonald (University of Calgary), Riane Vardeleon (University of Calgary), Saul Greenberg (University of Calgary), Sheelagh Carpendale (University of Calgary)

Proceedings of Graphics Interface 2016: Victoria, British Columbia, Canada, 1-3 June 2016, 167-174

DOI 10.20380/GI2016.21

  • BibTex

    author = {Thudt, Alice and Walny, Jagoda and Perin, Charles and Rajabiyazdi, Fateme and MacDonald, Lindsay and Vardeleon, Riane and Greenberg, Saul and Carpendale, Sheelagh},
    title = {Assessing the Readability of Stacked Graphs},
    booktitle = {Proceedings of Graphics Interface 2016},
    series = {GI 2016},
    year = {2016},
    issn = {0713-5424},
    isbn = {978-0-9947868-1-4},
    location = {Victoria, British Columbia, Canada},
    pages = {167--174},
    numpages = {8},
    doi = {10.20380/GI2016.21},
    publisher = {Canadian Human-Computer Communications Society / Soci{\'e}t{\'e} canadienne du dialogue humain-machine},
    keywords = {Visualization, Streamgraphs, Stacked Area Charts, Interaction Technique, Readability},


Stacked graphs are a visualization technique popular in casual scenarios for representing multiple time-series. Variations of stacked graphs have been focused on reducing the distortion of individual streams because foundational perceptual studies suggest that variably curved slopes may make it difficult to accurately read and compare values. We contribute to this discussion by formally comparing the relative readability of basic stacked area charts, ThemeRivers, streamgraphs and our own interactive technique for straightening baselines of individual streams in a ThemeRiver. We used both real-world and randomly generated datasets and covered tasks at the elementary, intermediate and overall information levels. Results indicate that the decreased distortion of the newer techniques does appear to improve their readability, with streamgraphs performing best for value comparison tasks. We also found that when a variety of tasks is expected to be performed, using the interactive version of the themeriver leads to more correctness at the cost of being slower for value comparison tasks.