Infographics and Data Graphics

I’d like to set the record straight about two types of graphical documents floating around the internet. Most people don’t make a distinction between infographics and data graphics. Here are some of each – open them in new tabs and see if you can tell them apart.

No peeking!

No, really, stop reading and do it. I can wait.

Okay, had a look and made your categorizations? As I see it, dog food, energy, and job titles are infographics, and Chicago buildings, movie earnings, and gay rights are data graphics. Why? Here are some distinctions to look for, which will make much more sense now that you’ve seen some examples. Naturally these are generalizations and some documents will be hard to classify, but not as often as you might think.

Infographics emphasize typography, aesthetic color choice, and gratuitous illustration.
Data graphics are pictorially muted and focused; color is used to convey data.

Infographics have many small paragraphs of text communicate the information.
Data graphics are largely wordless except for labels and an explanation of the visual encoding.

In infographics, numeric data is scant, sparse, and piecemeal.
In data graphics, numeric data is plentiful, dense, and multivariate.

Infographics have many components that relate different datasets; sectioning is used.
Data graphics have single detailed image, or less commonly multiple windows into the same data.

An infographic is meant to be read through sequentially.
A data graphic is meant to be scrutinized for several minutes.

In infographics, the visual encoding of numeric information is either concrete (e.g. world map, human body), common (e.g. bar or pie charts), or nonexistent (e.g. tables).
In data graphics, the visual encoding is abstract, bespoke, and must be learned.

Infographics tell a story and have a message.
Data graphics show patterns and anomalies; readers form their own conclusions.

You may have heard the related term visualization – a data graphic is a visualization on steroids. (An infographic is a visualization on coffee and artificial sweetener.) A single bar, line, or pie chart is most likely a visualization but not a data graphic, unless it takes several minutes to absorb. However, visualizations and infographics are both generated automatically, usually by code. It should be fairly easy to add new data to a visualization or data graphic; not so for infographics.

If you look at sites like visual.ly which collects visualizations of all stripes, you’ll see that infographics far outnumber data graphics. Selection bias is partially at fault. Data graphics require large amounts of data that companies likely want to keep private. Infographics are far better suited to marketing and social campaigns, so they tend to be more visible. Some datasets are better suited to infographics than data graphics. However, even accounting for those facts, I think we have too many infographics and too few data graphics. This is a shame, because the two have fundamentally different worldviews.

An infographic is meant to persuade or inspire action. Infographics drive an argument or relate a story in a way that happens to use data, rather than allowing the user to infer more subtle and multifaceted meanings. A well-designed data graphic can be an encounter with the sublime. It is visceral, non-verbal, profound; a harmony of knowledge and wonder.

Infographics already have all the answers, and serve only to communicate them to the reader. A data graphic has no obvious answers, and in fact no obvious questions. It may seem that infographics convey knowledge, and data graphics convey only the scale of our ignorance, but in fact the opposite is true. An infographic offers shallow justifications and phony authority; it presents that facts as they are. (“Facts” as they “are”.) A data graphic does not foster any conclusion upon its reader, but at one level of remove, provides its readers with tools to draw conclusions. Pedagogically, infographics embrace the fundamentally flawed idea that learning is simply copying knowledge from one mind to another. Data graphics accept that learning is a process, which moves from mystery to complexity to familiarity to intuition. Epistemologically, infographics ask that knowledge be accepted on little to no evidence, while data graphics encourage using evidence to synthesize knowledge, with no prior conception of what this knowledge will be. It is akin to memorizing a fact about the world, or accepting the validity of the scientific method.

However, many of the design features that impart data graphics with these superior qualities can be exported back to infographics, with compelling results. Let’s take this example about ivory poaching. First off, it takes itself seriously: there’s no ostentatious typography and the colors are muted and harmonious. Second, subject matter is not a single unified dataset but multiple datasets that describe a unified subject matter. They are supplemented with non-numeric diagrams and illustrations, embracing their eclectic nature. Unlike most infographics, this specimen makes excellent use of layout to achieve density of information. Related pieces are placed in close proximity rather than relying on sections; the reader is free to explore in any order. This is what an infographic should be, or perhaps it’s worthy of a different and more dignified name, information graphic. It may even approach what Tufte calls “beautiful evidence”.

It’s also possible to implement a data graphic poorly. Usually this comes down to a poor choice of visual encoding, although criticism is somewhat subjective. Take this example of hurricanes since 1960. The circular arrangement is best used for months or other cyclical data. Time proceeds unintuitively counterclockwise. The strength of hurricanes is not depicted, only the number of them (presumably – the radial axis is not labeled!). The stacked bars make it difficult to compare hurricanes from particular regions. If one wants to compare the total number of hurricanes, one is again stymied by the polar layout. Finally, the legend is placed at the bottom, where it will be read last. Data graphics need to explain their encoding first; even better is to explain the encoding on the diagram itself rather than in a separate legend. For example, if the data were rendered as a line chart (in Cartesian coordinates), labels could be placed alongside the lines themselves. (Here is a proper data graphic on hurricane history.)

An infographic typically starts with a message to tell, but designers intent on honesty must allow the data to support their message. This is a leap of faith, that their message will survive first contact with the data. The ivory poaching information graphic never says that poaching is bad and should be stopped, in such simple words. Rather it guides us to that conclusion without us even realizing it. Detecting bias in such a document becomes much more difficult, but it also becomes much more persuasive (for sufficiently educated and skeptical readers). Similarly, poor data graphics obscure the data, either intentionally because they don’t support the predecided message, or unintentionally because of poor visual encoding. In information visualization, as in any field, we must be open to the hard process of understanding the truth, rather than blithely accepting what someone else wants us to believe.

I know which type of document I want to spend my life making.

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