Course Description
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.
These web pages
reflect the course when it was last taught in 2023. Most will remain the
same, but details, readings and assignments are being reconsidered for
this year. If you find a link that doesn’t work, or could be replaced by
something better or more recent, please let me know by
filing an issue.
History of data visualization
Lecture notes
Topics:
- 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
Readings:
- 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
Graphical Perception
Lecture notes
Topics:
- Perception & Cognition
- Encoding, decoding
- Top-down vs. bottom-up processing
- Perceptual aspects
- Illusions
- Gestalt factors
- Accuracy of decoding
- Cognitive aspects
Readings:
- 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
Topics:
- Human factors in graphic & information design
- Empirical study of graphs
- Experimental methods
- Accessibility of data visualization
- Graphical inference
Readings
- 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
- Check out Additional resources for
Session 5
The Language of Graphs: from Bertin to GoG to ggplot2
- Bonus lecture: Deep questions of Data
Visualization 1up PDF ||
4up
Topics:
- Early attempts at standardization of graphs
- Bertin: Semiology of Graphics
- Graphics programming languages
- Wilkinson: The Grammar of Graphics
- Wickham: ggplot2
Readings:
- 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
Data Journalism
Readings:
2024 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. ||
lastModified :
friendly AT yorku DOT ca
orcid.org/0000-0002-3237-0941