Having been firmly immersed in the “New Age” world, for many years, even before I began work as a massage therapist, the first thing that comes to mind when I see the words “creative” and “visualization in the same sentence is a book by someone named Shakti Gawain called Creative Visualization, first published in 1978. But in this new world of massive amounts of information, I believe using data visualization creatively to both make sharing information beautiful as well as meaningful may have a much greater potential for creating a better world.
Our assignment this week was to find articles or blogs that discussed visualization theory and creativity. What kept catching my eye were various websites that listed tools for data visualization as well as some lists of beautiful and effective visualizations. Some examples are:
In my wandering, I came across a website for data visualization software called Tableau and discovered the software is free for students so downloaded it and took a tour. I pulled my Florida LMT Excel file into it an played around a bit. Unfortunately, I am traveling this weekend and my laptop isn’t powerful enough to handle it and I forgot to get some screenshots of the visualizations before I left, so maybe I will put some up later. However, the company has a free whitepaper: Visual Analysis Best Practices: Simple Techniques for Making Every Data Visualization Useful and Beautiful that offers some useful guidelines for data visualization. I also downloaded 5 best practices for telling great stories with data from the website.
In Visual Analysis Best Practices, taking good visualizations to “great” begins with asking questions. Being clear about what you are trying to say and who it is you are saying it to are among the primary questions to answer as you begin the process of designing your data visualization. Finding the answers to these questions helps you decide which data to use as well as what type of chart or other tool to use, which is the next step. For example, the paper recommends using certain types of bar charts for demonstrating trends over time and other types for comparison and showing rank. Looking for correlations in the data through scatter plots or bringing two different types of charts together can be used to learn about or demonstrate relationships.
In the section on creating effective views, recommendations include emphasizing the most important data through the use of size and color. Other advice is to create legible views and organize the visualization with good design. The rest of this paper deals with tools in the Tableau platform such as the dashboard which is a way to organize and present various visualizations to your audience. The limitations of this guide and the other is the slant towards business and marketing uses of data visualizations, as in the recommendation to choose your data to tell the story you want to tell. In scientific data visualization, all data must be represented.
I also stumbled upon a blog on ethos3.com with several relevant posts by Leslie Belknap. The Science of Memorable Data Visualizations reviews a 2013 report What Makes a Visualization Memorable?. 3 Simple Tips for Creative Data Visualization by the same author is a short and sweet guide with more useful links.
Belknap begins the more recent 3 Simple Tips with the statement, “…you have likely noticed that even though there is significant chatter about big data, there is also a surprising lack of insight into the best practices for presenting data to the masses.” While I am not sure I agree, I have seen some pretty amazing data visualizations just in the past three weeks, the post still has useful pithy content.
Tip #1, “Get Real With Your Data” recommends avoiding bar charts as well as pie charts because although they may be easy to create they are also common and unimpressive; they are not visually memorable. This also means using easily recognizable objects such as images of houses to represent housing or actual images of cash to represent money (income, wealth, etc.).
Tip #2 reminds us to use “scale to tell the story.” This is reminiscent of several concepts in the lecture. To me the idea of scale is a bit about similarity and proximity. Although, what this author is saying seems to oppose the idea that similar things are grouped together in the brain, I think it may also allow the brain to more easily grasp the difference in scale by using for example, a single dollar to represent low income to a large pile of dollars to represent the opposite. It is also the way the size of the bubbles were used in Hans Rosling’s 200 Countries, 200 Years, 4 Minutes video showing longevity related to GDP. Rosling’s visualization uses scale, similarity and proximity to tell the story.
Tip #3 suggests we use metaphors to add and create more meaning for our viewers. Finding real world concepts and images to ground the meaning in your data and to connect what may be an abstract concept to a representation. The author uses a visualization regarding cloud computing that is done with an image of actual clouds in a blue sky to represent this concept. It is easy for the brain to grasp this metaphor and assign meaning to it.
In The Science of Memorable Data Visualizations, the author summarizes some of the points in the report What Makes a Visualization Memorable? The first is that simple visualizations are not quite as memorable as those that are more visually “rich.” I am not sure I agree with this but the author is clear that complexity is not necessarily a drawback and if well-done it will enhance the memorability of your visualization. I would have liked to have seen some examples of this and may spend more time on this site in the future. I also plan to read the actual report for myself, more on that later.
The next point is related to tip #1 above, and that is to use images or graphics that are easily recognizable rather than abstract as well as forgetting about those all too common bar charts and pie charts. The recommendation is also to use seven or more colors rather than six or fewer.