Readings in information visualization - using vision to think

By Stuart Card, Jock D. Mackinlay, Ben Shneiderman. 1999

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Citations in the corpus

Visualizing Large-scale and High-dimensional Data (2016, background): “Instead, most visualizations of large data sets have to ﬁrst layout a summary or a coarse aggregation of the data and then reﬁne a subset of the data (a region of the visualization) if the user zooms in [5].”
Visual analytics of movement: An overview of methods, tools and procedures (2013, unknown): “[30], information visualization, with its techniques for user-display interaction supporting exploratory data analysis [20], and geovisualization, with its interactive maps and associated”
Dynamic visualizations and learning: getting to the difficult questions (2004, background): “Recent advances in information technology and graphics have made it possible to produce powerful visualizations of scientiﬁc phenomena and more abstract information (Card, Mackinlay, & Schneiderman, 1999; Spence, 2001).”
Designing interaction, not interfaces (2004, background): “For example, Information Visualization [14] illustrates the benefits of complementing human capabilities: it combines the power of computers to create visualizations and human perceptual abilities to extract patterns.”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, methodology): “Attempts have been made to classify interaction for information visualisation[25].”
Agent-based computing from multi-agent systems to agent-based models: a visual survey (2011, background): “This allows the viewer to look at a large corpus and develop deeper insights based on a high level view of the map(Card et al. 1999).”
Decision making with visualizations: a cognitive framework across disciplines (2018, background): “As Card, Mackinlay, and Shneiderman (1999) point out, we can use vision to think , meaning that visualizations can capitalize on visual perception to interpret a visualization without effort when the visual biases elucidated by the visualization are consistent with the correct interpretation.”
Visualization analysis & design (2015, unknown): “The Big Picture The framework presented here was inspired in part by the many taxonomies of data that have been previously proposed, including the synthesis chapter at the beginning of an early collection of infovis readings [Card et al. 99], a taxonomy that emphasizes the division between continuou”
Visualizing Consolidation in the Global Seed Industry: 1996–2008 (2009, unknown): “By denoting key patterns within complex data sets in such a compact space, visualization facilitates communication with much wider audiences, particularly those outside academia [46].”
A Diagram is Worth a Dozen Images (2016, background): “Much of the existing literature on graphic representations [27,28,29] covers only speciﬁc types of graphics or speciﬁc aspects of their syntactic structure.”
Interpreting informational cues: An explorative study on information use among prospective homebuyers (2009, unknown): “Information visualization has rapidly spawned a widely used family of commercial and research tools that enable users to discover relationships, patterns, clusters, gaps, outliers and other features ( Card, Mackinlay and Shneiderman 1999; Chen, 2004; Thomas and Cook, 2005).”
Knowledge Generation Model for Visual Analytics (2014, unknown): “propose the reference model for information visualization [5] that describes visualizations as data connected to visual mappings perceived by humans.”
D³ Data-Driven Documents (2011, methodology): “A second class of framework [9, 14] explicitly instantiates the InfoVis Reference Model [5, 12] using a set of composable operators for data management, visual encoding, and interaction.”