16 March 2017 |
It Is Not the Size That Counts: Small Visualizations Are Preferable to Large Visualizations
All else being equal, small visualizations are better than big visualizations. There is no need to take my word for it. You can prove it to yourself with a simple experiment. Once you have finished reading this sentence, focus on the big, orange number 1, and, at the same time, try and see how far you can get down the Snellen chart.
Perhaps you can make out the E, the F, and the P. While doing this, my guess is that you feel your eyes flickering in an effort to cheat. Now, focus on the blue number 2. Unless you are special, you likely cannot make out a single letter.
The principle here is one you know well. It governs where we place our TVs and monitors. It also helps us work out where to stand in an art gallery. We find it easier to recognize shapes when they are in a narrow field of vision. We even have an old adage to describe this: you cannot see the forest for all the trees.
Let us do the same thing with a simple chart. Look at the chart to the right. It is pretty easy to see the pattern.
Now, look at the bigger chart below. More is clearly less. It takes a lot more movement of the eyes before we can see the story. Unless you access this post on a device with a small screen!
The leading practitioner of the art of small visualizations today is perhaps The Economist magazine, which publishes lots of informative and perfectly-size visualizations. So, the next time you create a visualization that exactly fits onto a page in PowerPoint, or within the margins of a blog post, see if you can shrink it. Sure some people with bad eyes may struggle, but they all know how to zoom.
The Snellen chart is by Jeff Dahl, and was downloaded from Wikipedia. It is based on the public domain document: CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=4262200
Author: Tim Bock
Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q www.qresearchsoftware.com, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.