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Multivariate Mapping
Published in Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard, Thematic Cartography and Geovisualization, 2022
Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard
When more than two maps are displayed simultaneously, the result is termed a small multiple. Although small multiples can be useful for comparing patterns on multiple maps, they are difficult to interpret when comparing subregions within each map. A common alternative to the small multiple is the multivariate point symbol or glyph. Examples of glyphs include the star (in which multiple rays extend from a central circle), the snowflake (in which rays of the star are connected), 3-D bars (in which bars of varying height are placed alongside one another), data jacks (in which triangular spikes extend from a square central area), and Chernoff faces (in which distinct facial features are used). Although a considerable number of attributes can be represented by such methods, it is questionable whether map readers can fully understand the resulting symbols.
Vector Visualization
Published in Alexandru Telea, Data Visualization, 2014
The trade-off between the power of expression of glyphs, or number of attributes they can encode, and minimal screen size needed by a glyph is an important characteristic of glyph-based visualizations. To understand this better, let us compare these for a moment with the color-mapping visualizations discussed in Section 5.1. In both cases, the data attributes are available only at the discrete sample points of a dataset D. However, color mapping is typically applied at every point of the dataset D, either via texture-based interpolation or via the vertex-based color interpolation provided by the polygon rendering machinery. We say that color mapping produces a dense visualization, where every pixel represents an (interpolated) data value. In contrast, most glyph-based visualizations for vector data cannot have this freedom. Since a glyph takes more space than just a pixel, we cannot draw one glyph at every pixel of a given dataset. All glyph visualizations share this inherent discreteness, or sparseness, of the output. This affects the inverse image-to-data mapping (see Chapter 4) at the core of the visualization process.
A grammar for graphics
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Texts in Statistical Science, 2017
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
In the grammar of ggplot2, an aesthetic is an explicit mapping between a variable and the visual cues that represent its values. A glyph is the basic graphical element that represents one case (other terms used include “mark” and “symbol”). In a scatterplot, the positions of a glyph on the plot—in both the horizontal and vertical senses—are the visual cues that help the viewer understand how big the corresponding quantities are. The aesthetic is the mapping that defines these correspondences. When more than two variables are present, additional aesthetics can marshal additional visual cues. Note also that some visual cues (like direction in a time series) are implicit and do not have a corresponding aesthetic.
How the Preattentive Process is Exploited in Practical Information Visualization Design: A Review
Published in International Journal of Human–Computer Interaction, 2023
Luisa Barrera-Leon, Fulvio Corno, Luigi De Russis
Additionally, we found that some visual elements could extend main preattentive attributes in InfoVis. We identified two possible new visual preattentive elements: Glyphs and Glares. Glyphs integrate diverse preattentive attributes like color, form, and orientation to present more information in one graph (Cai et al., 2015; Ostendorp et al., 2016; Park et al., 2019). We contemplate Glyphs as an attentive attribute because it integrates the most commonly used preattentive elements, and its preattentive impact could affect the understanding of the graph data (Ostendorp et al., 2016). The other, more recent, the visual element is simulated Glares presented by Zhou et al. (2020). This technique uses brightness to guide the observer’s attention to identify the most relevant information. In addition, this technique enhances the visibility of the secondary data because it generates a high contrast. Therefore, we consider that Glares could be a new attentive attribute because it highlights specific data, and at the same time, the secondary data also get more attention. However, we acknowledge that those new attributes should deserve a thorough investigation as there is currently little literature on the subject. Therefore, further research is needed to establish the attention impact of these attributes in the preattentive visual-cognitive process.
Enhanced data narratives
Published in Journal of Management Analytics, 2021
Judd D. Bradbury, Rosanna E. Guadagno
Visual Data Narratives summarize a quantitative data set through a process of graphically encoding data elements and presenting them in an overall visual frame (Hullman et al., 2013; Hullman & Diakopoulos, 2011; Segel & Heer, 2010; Ziemkiewicz & Kosara, 2008). Among others, visually encoded objects known as glyphs are presented in a visual view as representations of summarized variables and values from a quantitative data set (Borgo et al., 2013; Munzner, 2014; Ward, 2008). Information is communicated through a visual frame or a series of frames that engage the audience in a review of glyphs as representations of many data values. Documentary Data Narratives utilize a similar style of visually encoded data viewing with the enhanced structure of full motion video (Bradbury & Guadagno, 2020). Often the presentation of evidence in Documentary Data Narrative is delivered using verbal “voice-of-god” narration as a second channel of communication further improving the knowledge transfer experience. The enhanced capabilities provided by a moving video experience in Documentary Data Narrative opens up the possibility for exploration of animated data visualizations in a short film (Amini, Henry Riche, Lee, Hurter, & Irani, 2015; Kwon, Stoffel, Jäckle, Lee, & Keim, 2014; Robertson, Fernandez, Fisher, Lee, & Stasko, 2008; Yee, Fisher, Dhamija, & Hearst, 2001). Computer Generated Text Data Narratives summarize quantitative data sets with totals, ratios and values woven into a journalistic text-based story (CITO Research & Narrative Science, 2015). The story is composed using algorithmic text in place of the human author, constructing stories with typical phrases found in human communication.