Visualizing Data

By William S. Cleveland. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Visualizing the effects of predictor variables in black box supervised learning models (2016, background): “Conditioning plots (coplots) (Chambers and Hastie, 1992; Cleveland, 1993), conditional response plots (Cook, 1995) and individual conditional expectation plots (Goldstein et al., 2015) plot quantities like f.xj, x\j/ against xj for a collection of discrete values of x\j (conditional response and…”
“Some of My Best Friends Are …”: Interracial Friendships, Class, and Segregation in America† (2007, methodology): “The plot employs a locally weighted scatterplot smoother (loess) to directly detect nonlinear patterns in the data (Cleveland 1993).”
Residency and depth movements of a coastal group of Atlantic cod (Gadus morhua L.) (2006, methodology): “A Loess smoothing technique was employed following Cleveland (1993). Each time series was modelled as”
Climate change, reproductive performance and diet composition of marine birds in the southern California Current system, 1969-1997 (2001, methodology): “We used robust locally weighted regression (LOWESS, Cleveland, 1993) with 90% band widths to illustrate trends in reproductive performance for each species at each location through time.”
Polaris: a system for query, analysis and visualization of multi-dimensional relational databases (2000, methodology): “Cleveland [7][8] used theoretical and experimental results to determine how well people can use these different retinal properties to compare quantitative variations.”
Harmonization of multi-site diffusion tensor imaging data (2017, methodology): “The Bland-Altman plot [Bland and Altman, 1986], also know as the Tukey mean-difference plot [Cleveland, 1993] or MA-plot [Dudoit et al., 2002, Bolstad et al., 2003] has been used extensively in the genomic literature to compare treatments and investigate dye bias.”
Above- and below-ground effects of aspen clonal regeneration and succession to conifers (2001, methodology): “LOESS curves are derived by fitting a series of weighted linear regressions to data, where the weights decrease with dis- tance from the point of interest (Cleveland 1993).”
Displaying Uncertainty With Shading (2008, methodology): “Contours of constant density are then calculated and filled with a constant shade, using the interpolation algorithm described by Cleveland (1993) as implemented in the filled.contour() function in R (R Development Core Team 2008).”
A protocol for data exploration to avoid common statistical problems (2010, background): “Another very useful, but highly neglected, graphical tool to visualize outliers is the Cleveland dotplot (Cleveland 1993).”
Scatterplot3d - an R package for visualizing multivariate data (2003, unknown): “Lattice is an implementation of Trellis Graphics , which is a framework for data visualization developed at the Bell Labs by Becker et al. (1996), extending ideas presented in Cleveland (1993).”
Migration Timing, Growth, and Estimated Parr‐to‐Smolt Survival Rates of Wild Snake River Spring–Summer Chinook Salmon from the Salmon River Basin, Idaho, to the Lower Snake River (2007, background): “—Box plots (Cleveland 1993) of estimated parr-to-smolt survival to Lower Granite Dam (Snake River) for wild spring–summer Chinook salmon tagged in the Salmon River basin, Idaho, by tagging site (upper panel) and migration year (lower panel).”