NOTE: Due to the weather I am not able to attend class in person today, but please work through the lab and ping me with any questions or comment on the post.
This week we are going to explore some dos and don’ts of data visualization as you prepare for your final projects. Edward Tufte is widely considered one of the world’s leading data visualization gurus, and has been called everything from the “Leonardo da Vinci of data” to the “Galileo of graphics.” Tufte will be our guide as we think through what good visualizations say and how bad data displays can lie and distort or even undermine your intended argument.
The Minard Map
It may well be the best statistical graphic ever drawn.
—Edward Tufte, The Visual Display of Quantitative Information (1983)
The Classic Discussed
This chart is famous for being a sterling example of data viz done right. In stark colors, simple lines, and minimal accompanying text, it not only tells a story, but offers an historical argument as well. Read the blog post below to be guided through the elements of the chart in an English translation.
- Michael Sandberg’s analysis on his DataViz History blog
- This is part 5 of a 10+ part deep dive series into Minard’s map. If you want more, see all the entries tagged Minard on his blog.
Does Minard’s map live up to the hype?
Do you agree with the glowing praise?
Improving(?) Minard’s Map
Even though it is often praised as the epitome of information design, many people have attempted to improve on Minard’s map. Explore the static and interactive variants below.
Are these versions better/more informative than the original?
Static variants (Do they add anything?)
- Menno-Jan Kraak’s variant from his 2013 book Mapping Time (Figure 3)
- An annotated version by Michael Sandberg
Interactive variants (Does it help to be able to manipulate the map?)
- A custom coded instance at http://www.masswerk.at/minard/
- An instance using the Neatline plugin for Omeka
- And the developer’s discussion of his process on the Scholar’s Lab blog
Questions
- Why is this considered such a landmark visualization, if not the best ever?
- What are the key features that make it stand out?
- How would you improve on it, if you were to take a stab?
Keeping it Honest: How Not to Lie with Pictures
This may well be the worst graphic ever to find its way into print.
—Edward Tufte, The Visual Display of Quantitative Information (1983)
We’ve already discussed how not to lie with maps, but it’s easy to do with visualizations as well. One of the biggest issues that Tufte stresses in his seminal work is how to stay honest with infographics. One of the easiest errors to make, for instance, is to scale the radius of circles, or one axis of two dimensional shapes, which results in massively larger areas than your data actually warrants.
- Explore
- this gallery of images illustrating “The Lie Factor”
- Think about how the literal measurements of the images contradict the implicit argument the graphic is trying to make.
- You can also explore the r/dataisugly subreddit for some good examples of bad data viz
- this gallery of images illustrating “The Lie Factor”
- Explore some more of Michael Friendly’s gallery of the Best and the Worst of Statistical Graphics
- What mistakes did you not think of before that you might want to avoid?
- What examples might you like to emulate for your own projects?
- Why?
Animation (unlocking the 4th dimension)
As we think about data viz for historical topics in particular, it is often the case that animation provides the most intuitive way to visualize change over time. Below are some classic examples of animating data viz in order to tell a story or make an argument.
Isao Hashimoto’s “2053”
What story does this video tell?
What are the data?
Why is the presentation so effective?
Google Motion Charts (Gapminder)
One of the most impressive data visualization breakthroughs of recent years was Hans Rosling’s invention of Gapminder: an application that really unleashed the “fourth dimension” of time and allowed data to be animated in an immediately understandable and powerful way. His TED talk below illustrating global health data with the tool is legendary.
Google bought the technology and made it available for all to use as Motion Charts. You can use the API or create a Motion Chart from data in a Google Sheet.
Resources
We’ve already explored some easy entry visualization environments, but here are some more very impressive tools and languages to check out:
- Tableau Public (an online visualization studio where you can upload your data and produce impressive, easily shareable data viz)
- D3.js (the current reining king of interactive online visualization)
- R graph gallery (R is a statistical package that generates very nice visualizations with very little code)
Choose one and check it out to see what people are creating.
- What works well with these visualizations and what could be improved?
- If you don’t have the JavaScript or R chops to create one of these currently, how might you implement the design decisions in programs you know?
Download or join a service and see if you can create something.
- Upload some of your own data, or download data sets from around the web
- One interesting source is JSTOR for Research, which aggregates all the scholarly literature housed by JStor
Assignment
The assignment for next time is twofold:
- Prepare for the midterm by reviewing the previous exercises
- Continue to make progress on the Final Project. Assignment 2 will be due on Wednesday of next week.
