Information visualization is the pictorial representation of data.
- Successful visualizations capitalize on our capacity to recognize and understand patterns presented in information displays.
- Conversely, they require that writers of scientific papers, software designers and other providers of visual displays understand what works and what does not work to convey their message.
This course will examine a variety of issues related to data visualization from a largely psychological perspective, but will also touch upon other related communities of research and practice related to this topic:
- history of data visualization,
- computer science and statistical software,
- visual design,
- human factors.
We will consider visualization methods for a wide range of types of data from the points of view of both the viewer and designer/producer of graphic displays.
Overview & Introduction
- Books, readings, blogs & web resources
- Goals of visualization; visualization as communication
- Roles of graphics in data analysis & presentation
- Effective data display
- Graphs: good/bad, excellent/evil
Bold face items are considered essential.
Varieties of information visualization
- Lecture notes: 1up PDF || 4up PDF
- From the readings that you have done so far, find one example of a data graph that attempts to tell an interesting story of a useful topic. How well does it succeed? How could it be improved?
- Data graphs: 1D – 3D
- Thematic maps
- Network and tree visualization
- Animation & interactive graphics
History of data visualization
- Overview: The Milestones Project
- The first statistical graph
- The Big Bang: William Playfair
- Moral statistics: the birth of social science
- Graphs in the public interest: Nightingale, Farr and Snow
- The Golden Age
- Case study: Re-Visions of Minard
- Friendly, M. A Brief History of Data Visualization
- Jeff Heer, A Brief History of Data Visualization, gives a lecture on his take on this history, interpreting and extending my work from a computer science perspective.
- Friendly, M. The Golden Age of Statistical Graphics. Statistical Science, 2008, 23, 502-535.
- Friendly etal. The First (Known) Statistical Graph: Michael Florent van Langren and the “Secret” of Longitude
- Friendly, M. & Denis, D. The early origins and development of the scatterplot
- Phan et al. Flow Map Layout, paper; see also: Web site
- Check out Additional resources for Session 3
- Perception & Cognition
- Encoding, decoding
- Top-down vs. bottom-up processing
- Perceptual aspects
- Gestalt factors
- Accuracy of decoding
- Cognitive aspects
- Cleveland & McGill (1984) Graphical Perception… JASA A foundation paper on understanding aspects of graph perception.
- Christopher Healey Perception in Visualization A web page on this topic, including interactive demos, animations and lots of examples
- Kennedy Elliot 39 studies abpout human perception in 30 minutes
- Gordon & Finch (2015) “Statistician Heal Thyself: Have We Lost the Plot?”, JCGS, 1210-1229,
- Zeileis etal. (2009) Escaping RGBland: Selecting Colors for Statistical Graphics, Computational Statistics & Data Analysis, 53, 3259–3270.
- Zeileis et al. (2020) colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes This is the latest, definitive work on designing color palettes for R. The associated web pages, http://colorspace.r-forge.r-project.org/index.html, have many vignettes and interactive color apps, also online, HCLwizard.
- Ware (2013), Information Visualization: Perception for Design, Chapter 4 (Color)
- Why Should Engineers and Scientists Be Worried About Color?
- Stephen Few Practical Rules for Using Color in Charts
- Thomas Lin Pedersen Scico and the Colour Conundrum
- Check out Additional resources for Session 4
Human factors research: How to tell what works
- Human factors in graphic & information design
- Empirical study of graphs
- Experimental methods
- Accessibility of data visualization
- Graphical inference
- Franconeri etal (2021), The Science of Visual Data Communication: What Works
- Heer & Bostock (2010), Crowdsourcing Graphical Perception…
- Skau & Kosara (2016), Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts
- Haroz, Kosara, & Franconeri (2015), ISOTYPE Visualization - Working Memory, Performance, and Engagement with Pictographs
- Buja et al. (2009), Statistical inference for exploratory data analysis and model diagnostics
The Language of Graphs: from Bertin to GoG to ggplot2
- Early attempts at standardization of graphs
- Bertin: Semiology of Graphics
- Graphics programming languages
- Wilkinson: The Grammar of Graphics
- Wickham: ggplot2
- Wilkinson et al (2000), The Language of Graphics, JCGS, 9(3), 530-543
- Wickham (2010), A layered grammar of graphics, JCGS, 19(1), 3-28; also a local copy
- Palsky (1999), The debate on the standardization of statistical maps and diagrams (1857-1901)
- Kruchten (2020), Remaking Figures from Bertin’s Semiology of Graphics
- Check out Additional resources for Session 6
The next two sessions, devoted to developing graphs with
ggplot2 and related methods will take place in the Hebb lab, Rm 059 BSB if possible.
ggplot2: Going further in the tidyverse
- Data wrangling: getting your data into shape
- Visualizing models: broom
- ggplot2 extensions
- tables in R
A collection of other R examples is available as R scripts, with some markup so that you can run them with Compile Report (Ctrl+Shift+K).
- Problems with uncertainty in visualization
- Visualizing distributions
- “Error bars”
- Uncertainty in fitted curves
- Hypothetical outcome plots
- Cartographic uncertainty
2022 Student presentations
These will take place in the last week of class. Details will be posted later. Students page.
Copyright © 2018 Michael Friendly. All rights reserved. ||
friendly AT yorku DOT ca