It’s hard for me to imagine a world in which data visualization existed long before the advent of the PC. The fact that Florence Nightingale was able to track the mortality rate during the Crimean War (comparing deaths by combat versus disease) and the fact that Charles Minard could outline the play-by-play of Napoleon’s defeat in Russia long before the birth of the computer is nothing short of phenomenal.
I believe that the main difference in data visualization before and after the invention of the PC has to with the increasing accessibility to computers. In the first chapter of his book “Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations,” Scott Berinato describes the “disruptive, democratizing” aspect of widespread dataviz use (p. 26). He writes: “This century has brought broad access to digital visualization tools, mass experimentation, and ubiquitous publishing and sharing” (p. 25). In other words, dataviz tools became more accessible to us non-scientists and non-researchers – with all the design programs and tools provided by computers and Internet, data visualization evolved into an art form, versus a pure science. I would argue that over time, the art that illustrates a data set became almost just as important as the information being presented by it. Data visualizers are, in the words of Berinato, “attempting to understand dataviz as a physiological and psychological phenomenon…borrowing from contemporary research in visual perception, neuroscience, cognitive psychology, and even behavioral economics” (p. 26). Essentially, the words “statistics” and “data visualization” cannot be used interchangeably. Statistics looks at the numbers, while the representation of those numbers speaks to how humans (in all our complexities and thoughts) should perceive this information. The benefits of technological advancements in data visualization over time are their accessibility for nearly anyone with a computer and some degree of visual literacy, the tools available that allow one to manipulate data into different, visually compelling mediums, and the fact that visualizing data can be done with relative ease (versus in the days when computers didn’t exist). But some of the cons that come with these technological advancements are the ease with which one can just as quickly manipulate data to tell false stories, and perhaps for some, being ingrained with the idea that how “pretty” a chart looks is more important than the accuracy of the information being conveyed is. For example, one could spend weeks on end creating a data visual where all the information is set up in a beautiful format, but in actuality, is a non-functional chart. In my opinion, a good chart is one that relays numerical information in a way that can be easily digested. It should also be visually appealing – fewer things turn off an audience more than a bland visual with what otherwise contains compelling data. For example, some of the most interesting and engaging charts have been ones that go far beyond the scope of your typical Microsoft Excel-generated bar and line graphs. During one of my earlier Quinnipiac courses, Visual Design, we were prompted to think about what examples of data visualization were most compelling to us and why. The KANTAR “Information is Beautiful” Awards is a great resource for those looking for examples of data that is presented in both a logical and aesthetically pleasing way. One particular data visual that was recognized by the Information is Beautiful Awards was published to Pro Publica this month. The visual, called “What Happened to All the Jobs Trump Promised?” provides a visual representation of just how much the current POTUS has delivered on his promise of creating 8.9 million jobs during his administration. The creators behind the illustration asked poignant questions, such as how much credit the president deserves for creating an average of 188,000+ jobs each month during his time in office, how many job opportunities were already in motion prior to his presidency, and what was delivered as far as job opportunities versus what was promised. The visual uses small graphics of people, each representing 4,000 jobs, to put matters into perspective (click the previous link to get visual). What makes this a “good” chart? To me, it’s informative, simple (but not unmemorable), clear, and engaging. Had the creators used rudimentary line graphs to showcase information about job opportunities during the Trump administrative, the visual might’ve been functional but ultimately uninteresting. A second example of a “good” chart that I’ll share is National Geographic’s “Simulated Dendrochronology of U.S. Immigration 1790-2016.” In short, this illustration shows the growth in immigration to the U.S. between these years and from which continents migrants came. In tree ring image (click link), one can see that in more recent years, there has been significant growth in immigration from Latin American regions. Albeit a little confusing (requiring more than just one glance for me), this tree ring chart is a compelling illustration of how times have changed in regard to the ebbs and flows of migration. While it’s a bit abstract, however, it is certainly more visually engaging than your standard line graph or pie chart.
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