The three articles that were assigned all had to do with different aspects of visualization. The first article, “On Visualizing Data Well” written by Ben Jones, compares a book that William Zinser wrote about how to be a good writer to visualizations. The second article, “Visualizations and Historical Arguments”, written by John Theibault, goes over history of visualizations as well as problems that are commonly encountered. The third article “How to Lie with Data Visualization”, written by Ravi Rarikh, shows how you have to watch how you interpret visualization that you come across because they can be very misleading.
William Zinser wrote an inspiring book “On Writing Well” and had it published in 1976. This book was a classic guide to writing nonfiction. Inspired by Zinser, Ben Jones writes the first article, “On Visualizing Data Well”, to discuss 7 principles of creating visualizations. The seven principles that he uses include transaction, simplicity, clutter, style, the audience, words, and usage. Jones compares each one of the principles in his article to principles that Zinser’s came up with in his book. The first principle, transaction, shows that “the product that any writer has to sell is not the subject being written about, but who he or she is.” (Zinser, 1976) It is important for the reader to feel the emotions of the person who created it. The second principle is simplicity, which means don’t over complicate the message. The next principle is clutter, which means to only use what is useful and nothing more. The fourth principle, style, is to create a data visualization that is both clear and beautiful. The fifth principle is the audience. This principle shows that you need to create for yourself, and not for an audience. This sixth principle is words, which basically says that you should avoid cheap and made up words that are cliché, by caring deeply about the words you use. Lastly, the seventh principle is usage. This is where you determine whether the information should be “ushered in” as an accepted practice, or “thrown out on [its] ear”.
“Visualizations and Historical Arguments” goes over how visualizations slowly integrated into the lives of almost everyone today. An idea that started off as just mainly being used by mathematicians and social scientists, is now being used by every field, including history. John Theibault states that the two most important dimensions of visualizations are density and transparency. Density is how much information can be stored in a small space, and transparency is how easy it is to understand the data shown. Visualizations are used to enhance presentation of arguments, as well as a means of quickly identifying patterns in a large data set. From the early 1900’s to now, visualizations have dramatically changed, making it more feasible to fit more information into a much smaller area of space. An example of this would be animated visualizations and interact with the reader as they more their cursor around different areas on the screen. Also, color coding different sections of information is an easy way to give more information without having to go into great detail about it. One of the challenges of historians using visualizations in their work is to “align the rhetoric with the audience’s ability to follow it”. Authors who use visualizations must understand how much background the reader has in order for them to interoperate what they are seeing. The very beginning of statistical analysis began with SPSS and SAS, which made doing so a lot easier. From there, the concept continued to grow, and still continues to grow today. The realm of visualizing information is still expanding today. An important point that Theibault made was that today’s historians will have to accustom themselves to reading network diagrams as well as they do regular maps and diagrams.
The third article “How to Lie with Data Visualization” went over three different ways that visualizations can be very misleading. The first was that Ravi discussed is Truncated Y-axis. Most of the time when you look at a chart, the Y-axis starts at zero and goes to the highest value. To mislead people into thinking that differences are a lot larger than they are, the writer can manipulate the Y-axis to look very dramatic. The second way that visualizations are misleading is with cumulative graphs. Instead of showing the important information over a long period of time, the writer can “zoom in” on the data and only show a portion of data, which gives the misleading idea that things are going well, when in fact they are not. The third way, ignoring conventions, is misleading because it violates standard practices. It goes beyond what you are used to seeing. For example, a pie chart that goes past 100%.
Visualizations have become very common when it comes to showing large amounts of data in a small amount of space. They make it easier to process a lot of information at a much faster pace. They can be very informative or misleading. They can be quick to understand, or very confusing. Hopefully as time goes on, and more people learn how to correctly read and understand visualizations, the less problems that will be encountered.
- Have you ever seen a visualization that was very misleading? What was it about?
- What do you believe that the future of visualizations entails?
- What did you think about the seven principles that were presented in the first article?