EuroVisShort2024

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https://diglib.eg.org/handle/10.2312/3606997

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Browsing EuroVisShort2024 by Subject "Empirical studies in visualization"

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    Mapping the Avantgarde: Visualizing Modern Artists' Exhibition Activity
    (The Eurographics Association, 2024) Tuscher, Michaela; Filipov, Velitchko; Kamencek, Teresa; Rosenberg, Raphael; Miksch, Silvia; Tominski, Christian; Waldner, Manuela; Wang, Bei
    In this paper, we address a crucial challenge for art historians by proposing a visual analytics approach consisting of multiple views designed to facilitate exploration and comparative analysis of artists and their exhibitions. Existing tools to support art-historical research are scarce and lack analytical means to navigate and analyze artists' exhibition activities. Our approach addresses this gap by supporting the identification of geospatial and temporal patterns and offering insights into the multifaceted exhibition behavior of artists in the early 20th century. To demonstrate the efficacy and validate our approach, we present a case study conducted by an art historian in the form of an expert interview. The discussion presents details about insights that were obtained and valuable feedback about the utility of the visual encodings and interactions. By integrating geospatial and temporal facets along with features to perform comparative analysis our approach emerges as a valuable asset for art historians providing a comprehensive look into artists' exhibition histories.
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    Revisiting Categorical Color Perception in Scatterplots: Sequential, Diverging, and Categorical Palettes
    (The Eurographics Association, 2024) Tseng, Chin; Wang, Arran Zeyu; Quadri, Ghulam Jilani; Szafir, Danielle Albers; Tominski, Christian; Waldner, Manuela; Wang, Bei
    Existing guidelines for categorical color selection are heuristic, often grounded in intuition rather than empirical studies of readers' abilities. While design conventions recommend palettes maximize hue differences, more recent exploratory findings indicate other factors, such as lightness, may play a role in effective categorical palette design. We conducted a crowdsourced experiment on mean value judgments in multi-class scatterplots using five color palette families-single-hue sequential, multihue sequential, perceptually-uniform multi-hue sequential, diverging, and multi-hue categorical-that differ in how they manipulate hue and lightness. Participants estimated relative mean positions in scatterplots containing 2 to 10 categories using 20 colormaps. Our results confirm heuristic guidance that hue-based categorical palettes are most effective. However, they also provide additional evidence that scalable categorical encoding relies on more than hue variance.