Visualizing Data

By B. Fry. 2007

1,124 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6336407227c981bcb1dd298793033005b7257e39

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Citations in the corpus

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).”
Decadal trends in shark catches and effort from the New South Wales, Australia, Shark Meshing Program 1950–2010 (2011, methodology): “Data analyseswere based on linear regressionmodels, a Loess smoothing function, which employs locally weighted linear regression (Cleveland 1993), and the Kolmogorov–Smirnov two-sample test (Siegel and Castellan 1988), using MS Excel, Sigmaplot 10 (SPSS, Chicago, IL, USA), and STATISTICA 6 (StatSoft”
Statistical inference for exploratory data analysis and model diagnostics (2009, methodology): “Davison and Hinkley (1997) in their book on bootstrap methods (Section 4.”
Visualization analysis & design (2015, neutral): “Statistical Graphics A book by statistician Bill Cleveland has an excellent and extensive discussion of the use of many traditional statistical charts, including bar charts, line charts, dot charts, and scatterplots [Cleveland 93b].”
A CROSS‐CULTURAL EXPLORATORY STUDY OF THE LINKAGE BETWEEN EMOTIONAL INTELLIGENCE AND MANAGERIAL EFFECTIVENESS (2003, background): “The results are summarized in a series of six contour plots depicted in Figure 2 Figure 2 is a trellis plot (Cleveland, 1993), where each panel represents an individual levelplot for a specific combination of country and skill.”
Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation (2008, methodology): “Keim [17] presents a taxonomy of primarily multidimensional visualization techniques, listing categories such as standard 2D/3D displays, geometrically transformed displays [7], iconic displays [6], dense pixel displays [18], and stacked displays [22].”
Principles of Uncertainty (2011, background): “3 References There are many fine books on graphics, for example Tufte’s volumes (Tufte (1990, 1997, 2001, 2006)) and Cleveland (1993, 1994). An interesting comparative review of five books on graphics is given by Kosslyn (1985). There are also many excellent books on R.”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, neutral): “Some of the early works in InfoVis were inspired by considerations from statistics [61], [62], [63].”
Fitting concentration and load rating curves with generalized linear models (2008, background): “A useful scale on which to look for heteroscedasticity is √|residual|, as taking absolute values folds negative residuals into positive residuals and taking square roots provides some correction for the resulting skewness (Cleveland, 1993).”
Tests for Regression Models Fitted to Survey Data (2014, background): “A simple regression of systolic blood pressure on sodium intake shows a negative correlation, but the conditioning plot (Cleveland 1993) in Figure 2 suggests this is confounding by age: younger people, with lower blood pressure, tend to consume more sodium.”
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis (2020, methodology): “…Representations. hidden representations for the samples in the testing sets . of no regularization ( α = 0 , β = 0), modality-invariance is not Figure 4: Visualization of the modality-invariant and -specific sub-spaces in the testing set of MOSI and UR_FUNNY datasets using t-SNE projections [28].”
Green turtle somatic growth dynamics in a spatially disjunct Great Barrier Reef metapopulation (2004, methodology): “Overall, the GAM regression models were good fits to the data evident in small residual deviances relative to residual degrees of freedom, acceptable nonparametric R2values, and no evidence of aberrant residual behavior assessed using data visualization (see Cleveland 1993).”
Crowdsourcing graphical perception: using mechanical turk to assess visualization design (2010, background): “, due to background luminance [30]), plotting density [11, 30], and changes to chart size [12], scale [6], or aspect ratio [1, 5].”