1.1 Historical Perspective
There is little doubt about it: we live in a golden age for data visualization. Rendering and dashboarding tools are everywhere – R, ggplot2, seaborn, plotly, Power BI, Tableau, among others – and it seems as though we cannot turn around without bumping into another amazing book on the topic.1 Notable examples include [1],[2],[3],[4],[5],[6],[7],[8],[9],[10],[11],[12].
If we take the position that data visualization is simply another way for us to take in our environment (and its stimulii), and represent things in a way that will allow us to make decisions and to attempt to better grasp it (and hopefully, to control it?), then we have been conducting data visualization for millions of years.
Of course, data visualization as we understand it today is not usually viewed under this all-encompassing lens: instead, we are dealing with datasets which have been collected, transformed, and processed with specific analytical goals in mind, and results conveyed to an interested audience using a common vocabulary which relies on visual tropes and storytelling conventions.
It is traditional, at this stage, for authors to introduce historical data visualization and to spend some time discussing their strengths, weaknesses, and “first-to-market” claims. Frequently discussed charts include William Playfair’s The Commercial and Political Atlas [1786] (see Figure 1.1, 3rd row), John Snow’s Map of the London Cholera Outbreak of 1854 (bottom row, left), Charles Minard’s March to Moscow [1869] (2nd row), Florence Nightingale’s Diagram of the Causes of Mortality in the Army in the East [1858] (1st row), William DuBois’ The Exhibit of American Negroes at the 1900 Paris World Exposition (4th row), and/or Charles de Fourcroy’s Tableau Poléométrique [1784] (bottom row, right), which have all been covered extensively in other sources.
Figure 1.1: A helping of historically significant and meaningful data visualizations.
We have come a long way over the last 250 years or so when it comes to visualizing our data insights, of course, but in a very real sense, we are still more or less following our progenitors’ lead: exploring, describing, explaining, and persuading.
In one major advance for the field, however, the current consensus is that data visualization is an analytical method in its own right (we will discuss this further in Module 2).
References
Cough, cough!↩︎