The elements of graphing data

By I. Fujishiro. The Visual Computer, 2005

795 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/80e1b3691efb64233fa7c5e39f15f1eff7f14110

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In Computer Vision and Pattern Recognition

Citations in the corpus

Useful junk?: the effects of visual embellishment on comprehension and memorability of charts (2010, background): “Cleveland [3] similarly proposed a series of recommendations for graph design, based on his research into graphical perception, and emphasized a minimalist approach to presenting data graphically.”
A simple more general boxplot method for identifying outliers (2004, background): “Carling (2000) points out that various authors, such as Cleveland (1985), Freund and Perles (1987), Frigge et al. (1989), Hyndman and Fan (1996), Harrell and Davis (1982), Hoaglin et al. (1983) and Hoaglin and Iglewicz (1987) have proposed a variety of deÿnitions for quartiles.”
An insight-based methodology for evaluating bioinformatics visualizations (2005, background): “…and effectiveness criteria [18], data density and data/ink [19], criteria for representation and interaction [20], high-level design guidelines [21], principles based on preattentive processing and perceptual independence [22], and rules for effectiveness of various visual properties [23].”
Conditioned Choropleth Maps and Hypothesis Generation (2005, methodology): “First, it requires assessing the vertical distance between two curves, a task not readily performed by our eye-brain system (Cleveland 1985).”
Graph-theoretic scagnostics (2005, methodology): “This display was first published by John Hartigan [19] and was popularized by Tukey and his associates at Bell Laboratories [9].”
An improved effect size for single-case research: nonoverlap of all pairs. (2009, background): “Given a large sample, a uniform probability distribution can indicate discriminability (Cleveland, 1985).”
The Effects of Mindfulness Meditation: A Meta-Analysis (2012, methodology): “LOWESS is a procedure to fit a curve into a linearly or nonlinearly associated pair of variables (see Cleveland 1985).”
The Multilevel Cycle of Anthropogenic Zinc (2005, methodology): “Bar graphs are used where the parameter being plotted is a magnitude, “lolliplots” (Cleveland 1985) where the parameter is a ratio or percentage and where bars signifying “everything from the bottom to the top of the bar” would be inappropriate.”
DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization (2015, neutral): “Research on automatic visualization generation aims to establish design principles for effective visual presentation based on data type and user task [7, 15, 23].”
Data Analysis Using Regression and Multilevel/Hierarchical Models: Understanding and summarizing the fitted models (2006, background): “(2003), and the latin square example comes from Snedecor and Cochran (1989). Models for covariance matrices have been presented by Barnard, McCulloch, and Meng (1996), Pinheiro and Bates (1996), Daniels and Kass (1999, 2001), Daniels and Pourahmadi (2002).”
A researcher's guide to regression, discretization, and median splits of continuous variables (2015, background): “Graphical communication is paramount in facilitating an understanding and appreciation of data (Cleveland, 1993, 1994; Robbins, 2013; Tufte, 2001; Tukey, 1977).”
Change detection in hydrological records—a review of the methodology / Revue méthodologique de la détection de changements dans les chroniques hydrologiques (2004, background): “of the general principles of EDA can also be found in Cleveland (1994) and Tufte (1983).”
Arrows in Comprehending and Producing Mechanical Diagrams (2006, neutral): “…& Hegarty, 1998; Novick, 2001; Pinker, 1990; Stenning & Oberlander, 1995; Tversky, 1995, 2001; Tversky et al., in press), statisticians (e.g., Cleveland, 1985; Wainer, 1984; Wilkinson, 1999), semioticians (Bertin, 1983; McCloud, 1994), computer scientists (e.g., Card, Mackinlay, &…”
The Cognitive Science of Visual-Spatial Displays: Implications for Design (2011, methodology): “In a classic task analysis, Cleveland first analyzed the basic perceptual tasks that had to be carried out to encode the information in different common kinds of statistical graphs, such as pie charts, bar charts, scatter plots, etc. (Cleveland, 1985; Cleveland & McGill, 1984).”
Data Mining with R: Learning with Case Studies (2010, background): “A more formal follow-up of this work is the book The Elements of Graphing Data (Cleveland, 1995) by the same author.”