Information Visualization: Perception for Design

2000

970 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4cece3972628d0ce5384a5fe2ef5ceb964cba0d8

Citations in the corpus

• Mastering the Information Age - Solving Problems with Visual Analytics (2010, unknown): “Ware[122] discusses preattentive processing quite extensively.”
• The Role of Uncertainty, Awareness, and Trust in Visual Analytics (2016, unknown): “Other studies have looked at human analysts behaviours while building trust in the knowledge generation process, with respect to perception [79], cognitive biases [31], and analytic roadblocks [48].”
• Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations (2009, unknown): “…we perceive visualizations indicates that the spatial positioning of data items may be the most salient of the possible visual encodings such as position, colour, shape, etc. [3] [21], it may well be important to preserve positioning of data items while providing set membership visual containers.”
• Designing interaction, not interfaces (2004, unknown): “For example, information visualization relies on preattentive processing of visual information to extract patterns [35].”
• iForest: Interpreting Random Forests via Visual Analytics (2019, unknown): “Since humans may have problems in perceiving more than ten categorical colors [55] at the same time, iForest colors the top ten features with largest importance.”
• Rethinking Visualization: A High-Level Taxonomy (2004, unknown): “For a review of discrete visualization techniques, see [4, 8, 19].”
• Useful junk?: the effects of visual embellishment on comprehension and memorability of charts (2010, unknown): “Ware identifies ‘memory extension’ as one of the ways in which visualizations extend human cognition [23].”
• Design Study Methodology: Reflections from the Trenches and the Stacks (2012, unknown): “The relevant literature instead focuses on methods for designing [1, 42, 66, 79, 82, 90, 91] and evaluating [13, 33, 39, 50, 68, 69, 76, 80, 85, 86, 95] visualization tools.”
• VizLinter: A Linter and Fixer Framework for Data Visualization (2021, unknown): “Consequently, four common issues are categorized from the expert interviews and previous literature related to visualization principles [14,33,39,58] and visualization grammar [60].”
• Graph Visualization and Navigation in Information Visualization: A Survey (2000, unknown): “Ware's book [123] is also an interesting source of information for this topic.”
• Information Visualization and Visual Data Mining (2002, unknown): “Nice overviews of the approaches can be found in a number of recent books [2], [3], [4], [5].”
• Visualization analysis & design (2015, unknown): “matching visual channels to data type [Mackinlay 86], and books on visualization [Ware 13] and cartography [MacEachren 95].”
• Human factors in visualization research (2004, unknown): “Ware defines visualization as “a graphical representation of data or concepts,” which is either an “internal construct of the mind” or an “external artifact supporting decision making.””
• Matrix2png: a utility for visualizing matrix data (2003, unknown): “The display of numerical values as color maps dates to the 19th century and have a very wide use in data visualization (see Feinberg, 1979 for review; see Tufte, 1990 and Ware, 2000 for practical issues relating to the use of color for visualization).”
• ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology[OPEN] (2017, unknown): “When researchersmust devote a portion of their cognitive load to a computer interface instead of the subjects they are-exploring, their train of thought will be disrupted and their overall productivity will decrease (Ware, 2012).”
• Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations (2016, unknown): “Since over-encoding can impede interpretation [42], Compass penalizes encodings that use multiple retinal encodings (e.”
• Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, unknown): “Spatial proximity is one of the strongest visual indicators of grouping [21].”