I remember learning the basics of chart creation in Excel back in high school; using data generated by the students in my class. We learned how to make bar charts of our birth months, pie charts showing what proportion of our class lived in which Auckland suburbs and line charts plotting the water level in an ice cream container situated in the courtyard at hourly intervals during a rainy afternoon.
Today, as a data visualisation storyteller and D3.js developer, I use the basic visual elements of height, width, area, slope, volume, colour, angle and volume to create visual representations of much more complex data, drawing on the knowledge imparted my school maths teachers and assuming a similar basic level of understanding and interpretation capacity on the part of my audience.
Visual representations of data are so ubiquitous in our culture, and are such a standard component of basic numeracy today, that it’s easy to feel as though this practice of using visual elements to represent numbers, quantities, proportions and the passage of time is a self-evident system for understanding information. But of course, this isn’t the case. Like written language, the language of data visualisation developed slowly.
The abacus is perhaps the oldest example of a data visualisation tool. It was invented more than four and a half thousand years ago. It’s one of the earliest instances of physical representations of a quantity being employed to allow the user to stand back from the figures themselves and see an overall picture. The abacus is also one of the earliest computation tools; that’s a parallel historical narrative that’s equally important to the development of the modern computational dataviz tools we use today. However, the story of data visualisation takes a detour through the slow evolution of religious and natural sciences, diagrams and mapping that is quite independent of the evolution of computation and statistics. Before people learned to visualise data, they had to develop a conception of how to visualise knowledge, using diagrams to explain and illustrate important cultural concepts. Learning how to communicate information through a diagram was an important intellectual precursor to the development of data-driven diagrams such as charts.
If an abacus is the oldest data visualization tool, then diagrams such as these examples I found during a short scroll through Google images seem, to me, to be precursors of the concept of an infographic. Diagrams, after all, are pictures that can be used to carry out and illustrate reasoning. Instead of replicating a physical form, a diagram is capable of representing a process, a theory, a mechanism or an abstract concept. I like these examples from different cultures and times because they’re all using circles as a mechanism to express information visually but in very different ways. Each one of them uses a circular diagram to draw a connection between the natural order of the world as it was understood, and where people belong inside that structure.
This ancient Egyptian Astronomical diagram, which Wikipedia tells me is from the Tomb of Senemut, around 1473 BC, shows circumpolar constellations in the form of discs, each divided into 24 sections which suggest a 24 hour time period, lunar cycles, and their relationship to Egypt’s sacred celestial deities.
Leonardo’s famous diagram Vitruvian Man is a cool example of an early infographic, illustrating the correlation of human proportions with geometry.
This representation of the Great Chain of Being, a concept first developed by the ancient Greeks, describes the natural world as a static hierarchy, from God and angels all the way through animals and vegetables to minerals at the bottom of the chain.
I love this diagram from 1848, drawn just prior to Darwin’s publication of Origin of Species, depicting the presence of various groups of species in the layers of the earth’s crust. I like its clever use of a circle to represent a cross-section of the earth and its layers, its colour key, its segmentation of the animal kingdom into classifications, and its use of length and volume of bars to represent information. If you look closely, you can see the labels on the inner rings that indicate the “reign of fishes”, and the origins of reptiles, mammals and man. I think this is a brilliant format, and one of the best examples of an early infographic that I could find.
I find these illustrations fascinating because, like the infographics we’re used to seeing today, they straddle the divide between mathematics, science and art, and are examples of the way in which graphical representations of information evolved as tools to inform and describe.
It wasn’t until the 17th century that the science of measurement evolved to produce charts that we would recognise today. The great breakthrough that paved the way for charting as we know it was Descartes’ invention of the coordinate system in the 1630s, which replicated the axes of an abacus on what became known as the ‘Cartesian Grid’. Descartes invention created a systematic link between
Euclidian geometry and algebra, making it possible to create a graph of a function and laid the foundation for plotting statistics the way we do today in modern charts.
Descartes’ Cartesian Grid was the platform from which William Playfair, who began his work by creating technical drawings for an engineering firm in 1759, developed the forerunners of the charts we’re familiar with in our Excel toolbar today. At the age of 25, Playfair published his Commercial and Political Atlas which contained the first instances of fever charts (which today we’d be more likely to describe as line charts), and bar charts, detailing economic data.
Playfair’s graphs were so innovative and brand new in his day that he had to publish them alongside long-winded explanations of how to read and interpret them and make highly specific labels and disclaimers prominent. I’m amazed to consider these visual renderings of data in this context, as groundbreaking innovations that required detailed instruction for the reader.
Playfair wasn’t much celebrated for his inventions during his own lifetime and seems to have been largely forgotten by history. But his groundbreaking work laid the foundations for modern chart making and over the following centuries, innovators built upon Playfair’s work to create timeless examples of data visualisation that added layers of sophistication to his creations.
Next week, I’ll look at how data visualisation techniques grew and developed from these early foundations, and evolved into decision-making tools, data-driven art, and vehicles for public engagement with data.
Data is beautiful – Sarah
Sarah blogs about how data can be made aesthetic as well as informative.
We run regular business intelligence courses in both Wellington and Auckland.