READING SUMMARIES & RESPONSE FOR HISTORY OF DATA VISUALIZATION
Jumping into ICM529, Data Visualization with Prof. Courtney Marchese
In the beginning, visual recording of information began when amoebas began to sense the light of the sun passing through their membranes in ancient oceans…oh wait…sorry. Those notes are for a much longer post about the history of data visualization, let’s not go back that far. We can skip ahead and simply say that in a short history “The Hiz” of DataViz, there have been many contributors from seemingly unrelated human practices such as hunting, mining and shipping that have led to its development. Humankind has been attempting to communicate the findings of large data sets and complex ideas quickly through charts, graphs and maps since the Age of Enlightenment. Astronomers and explorers would record the stars and terrain with the naked eye and translate their measurements into tables and cartograms. As nation states emerged and hierarchical societies grew, they required better management. Information and data visualizations became tools for scientists, engineers and sociologist seeking better understandings and insights. Most notably, the 1786 publication of William Playfair’s “Commercial and Political Atlas” in which he attempted to represent the economic history of England with line graphs and bar charts and Florence Nightingale’s 1858 ”Coxcomb Diagrams” which undeniably demonstrate more losses to the British army caused by disease than battle.
The industrial revolution brought with it a new set of challenges and questions. As we improve printing methods, reproduction and precision in measurement (statistics, logic, mathematics) we also see these advancements applied to data display and graphic representations motivated by business needs. Accepted best practices and methods are codified and the first books are published on the subject of the relationship of data, perception and visualization. Authors include Brinton (1914), Spear (1952), Bertin (1967). Most of us relate modern infographics to rise of computers, but we can see that data visualization is rooted in communication, science and art centuries beforehand.
What are some of the main differences between dataviz before and after the introduction of the PC?
The primary differences between DataViz before and after the introduction of the personal computer are as follows:
- Data visualization branches off into two primary approaches: Scientific which is driven by accuracy, minimalism and information volume/validity and Journalistic with an emphasis on storytelling, clarity of narrative as perceived by reader, and design.
- The democratization of visualization practices as a result of broad access to computer applications (design, spreadsheet, database languages) and data has launched what Scott Berinato in his book “Good Charts” refers to as a reformation period. Whereas, in earlier periods only trained engineers and craftsmen could produce graphics for distribution, now there is widespread participation and experimentation.
- Collection of data has become ubiquitous and easy. We live in the information age where the internet and network connected devices have introduced an unprecedented ability to acquire massive amounts of quantitative data. Both automatically (sensors) and through user input (surveys, clicks, interactivity) the difficulty of recording precise data have never been so low.
- Synthesis and calculation of data becomes faster and now increases exponentially in tangent with the rate of computing speed. Quantum computing and artificial intelligence promise new frontiers in real time visualization, models and analysis.
What are the pros and cons of technological advances in dataviz?
As with any technological disruption, we find that that with the positive effects there are also some unintended outcomes. The fairly low threshold for producing a graph has led to a lot of “bad graphs”. Unclear, inconsistent and misleading presentations also become commonplace. Just as, there is a lot of bad videos on youtube, audiences may be subjected to an overload of DataViz in quantity without quality. There is a need for visual literacy training and reestablishment of best practices in the new age, similarly to how developers of web browsers and smartphones (and many other industries) needed to agree on a set of standards to improve user experience and production.
Another challenge is our ability to interpret data into meaningful insights in the face of mountains of information. Just as added channels have increased the amount of “news” we receive daily, the editorial function and analysis of the facts have been diminished in the age of social media, cable networks and special interest based broadcasting.
In your opinion, what makes a “good chart”?
As Mark Twain once famously said “There are lies, damned lies and then there’s statistics…” First and foremost, a chart must be truthful. There must be some accuracy in the data that is being presented and a visual should not be used to support a weak argument. Maybe this is too much to ask, but usually, human nature leads flawed communication. Which leads to my other measure of a good chart.
Second, a good data visualization should clearly communicate a message. It can be worth 10,000 words. Well crafted charts can take lengthy writing and instantly express it in one visual. In terms of developing a message, a data visualization can also help frame or inform a perspective or challenge assumptions.
What do YOU most strongly respond to (in terms of a “good chart”) and why? Please use examples.
Personally, I respond to color. As a boy I loved big atlases, the bigger, the better. If I had to hold a book with both arms and could not tuck it under my arms, I felt compelled to pull it down from the shelf in the reference section of the public library and open it up on one of those round reading tables. I would get up on my knees and lean in taking in all the details…. especially the key. The key is what explained everything: capitals, elevations roads, population density, etc. I would lay books like that down on the floor and flip the pages and if there was a center bi-fold it was a delight. The color was important and once I learned the key and it had enough contrast ( I am slightly color blind, more color dysfunction which I learned later in life) I could spend time taking it all in in a non-linear way.
For example, in this time line chart by David McCandless, Omid Kashan, Stephanie Smith, Karl Webster entitled “East before West: What Middle-Eastern thinkers discovered long before the west” color is critical in allowing the user to trace and quickly see parallels in discoveries between epochs.
Shape, form and composition are also important. In the infographic below, color is used as a secondary aesthetic indicator simply to divide the page into two main sections while the outlines and shapes are what really draw the eyes to the key messages and differences between ideologies.
Finally, here is an example of great and interesting information but a bland presentation. I looked up this topic as it relates to a possible upcoming project theme in this class. It is easy to read but the clip art icons and flat color do little to engage the reader. I will use it in comparison to the creative presentation by Tim Leong entitled “”The Walking Dead” Kill Counter” below it where the use of color and upside down bar chart gives the whole chart the very creative impression of dripping blood. The figures however are not as clear and presentable as the “Fears” chart. I prefer to reach a balance between these two and create something that is both visually appealing and informative. Somewhere between cart junkyard and monochrome Mondrian. I hope I will have a chance (and be have the skill) to do that in this class.
You will find more infographics at Statista
Have a great Memorial Day weekend all and avoid zombies at all costs as they account for 78% of all fatalities on The Walking Dead!
Shawn is an Information Technology manager in Washington D.C. and a graduate student at Quinnipiac University pursuing his masters in Interactive Media and Communications.
References and Resources for Further Exploration
Friendly, M., Chen, C.-h., Härdle, W., & Unwin, A. (2008). A Brief History of Data Visualization. ResearchGate, 15–56. doi: 10.1007/978-3-540-33037-0_2
Data visualization – Wikipedia. (2019, May 19). Retrieved from https://en.wikipedia.org/wiki/Data_visualization
William Playfair – Wikipedia. (2019, May 25). Retrieved from https://en.wikipedia.org/wiki/William_Playfair
Nightingale’s ‘Coxcombs’ | Understanding Uncertainty. (2019, May 25). Retrieved from https://understandinguncertainty.org/coxcombs
Is Beautiful, I. (2019, May 25). Left vs. Right (US) — Information is Beautiful. Retrieved from https://informationisbeautiful.net/visualizations/left-vs-right-us
Is Beautiful, I. (2019, May 25). What Islamic Golden Age Thinkers Discovered Long before the West — Information is Beautiful. Retrieved from https://informationisbeautiful.net/visualizations/what-islamic-golden-age-thinkers-discovered-long-before-the-west
Infographic: Americans’ Top Fears Of 2017. (2019, May 25). Retrieved from https://www.statista.com/chart/11551/americans-top-fears-of-2017
Leong, T. (2013). Super Graphic: A Visual Guide to the Comic Book Universe. Chronicle Books. Retrieved from https://www.amazon.com/Super-Graphic-Visual-Guide-Universe/dp/1452113882
Berinato, S. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press. Retrieved from https://www.amazon.com/Good-Charts-Smarter-Persuasive-Visualizations/dp/1633690709
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