In English, we use many different words to describe the same basic objects. In one survey, researchers Dieth and Orton explored which words were used for the place where a farmer might keep his cow, depending on where the speaker resided in England. The results include words like byre, shippon, mistall, cow-stable, cow-house, cow-shed, neat-house […]
In English, we use many different words to describe the same basic objects. In one survey, researchers Dieth and Orton explored which words were used for the place where a farmer might keep his cow, depending on where the speaker resided in England. The results include words like byre, shippon, mistall, cow-stable, cow-house, cow-shed, neat-house or beast-house. We see the same situation in visualization, where a two-dimensional chart with data displayed as a collection of points, using one variable for the horizontal axis and one for the vertical, is variously called a scatterplot, a scatter diagram, a scatter graph, a 2D dotplot or even a star field.
There have been a number of attempts to form taxonomies, or categorizations, of visualizations. Most software packages for creating graphics, such as Microsoft Excel focus on the type of graphical element used to display the data and then sub-classify from that. This has one immediate problem in that plots with multiple elements are hard to classify (should we classify a chart with a bars and points as a bar chart, with point additions, or instead classify it as a point char, with bars added?). Other authors have started with the dimensionality of the data (one-dimensional, two-dimensional, etc.) and used that as a basic classification criterion, but that has similar problems.
Visualizations are too numerous, too diverse and too exciting to fit well into a taxonomy that divides and subdivides. In contrast to the evolution of animals and plants, which did occur essentially in a tree-like manner, with branches splitting and sub-splitting, information visualization techniques have been invented more by a compositional approach. We take a polar coordinate system, combine it with bars, and achieve a Rose diagram. We put a network in 3D. We add texture, shape and size mappings to all the above. We split it into panels. This is why a traditional taxonomy of information visualization is doomed to be unsatisfying. It is based on a false analogy with biology and denies the basic process by which visualizations have been created: composition.
Within SPSS we have adopted a different approach – looking at charts and visualizations as a language in which we compose “parts of speech” into sentences. This approach was pioneered by Leland Wilkinson in his book The Grammar of Graphics. Consider natural language grammars. A sentence is defined by a number of elements which are connected together using simple rules. A well-formed sentence has a certain structure, but within that structure, you are free to use a wide variety of nouns, verbs, adjectives and the like. In the same way, a visualization can be defined by a collection of “parts of graphical speech”, so a well-formed visualization will have a structure, but within that structure you are free to substitute a variety of different items for each part of speech. In a language, we can make nonsensical sentences that are well-formed. In the same way, under the graphical grammar, we can define visualizations that are well-formed, but also nonsensical. One reason not to ban such seeming nonsense is that you never know how language is going to change to make something meaningful. A chart that a designer might see no use for today becomes valuable in a unique situation, or for some particular data. “The tasty aged phone whistles a pink” might be meaningless, but “the sweet young thing sings the blues” is a useful statement, and grammatically similar. In our grammar-based approach, we have a set of different “parts of speech” that we compose:
- data – the variables that are to be used.
- coordinates – the basic system into which data will be displayed, together with any transformations of the coordinate systems, lik polarization, reflection, etc.
- elements – the graphic glyphs used to represent data; points, line, areas,…
- statistics – mathematical and statistical functions used to modify the data as it is drawn into the coordinate frame.
- aesthetics – mappings from data to graphical attributes like color, shape, size, …
- faceting – dividing up a graphic into multiple smaller graphics, also known as paneling, trellis, …
- guides – axes, legends and other items that annotate the main graphic
- interactivity – methods for allowing users to interact with the graphics; drilldown, zooming, tooltips, …
- styles – decorations for the graphic that do not affect its basic structure, but modify the final appearance; fonts, default colors, padding and margins, …
The core concept behind our approach is that you should be able to take a chart and modify the language to replace one part by a similar part, and have a well defined and potentially useful result. The result is a system where the limits of what you can display are neither based on how well you can do graphical programming, or how well the computer program you use has implemented a feature, but instead is based simply on combining well-known parts into novel systems.
