Bertin extended to text (pt 2)

In a previous post, I’d talked about Bertin’s previous writings on text attributes in visualization in the classic text Sémiologie Graphique (only the French edition – not translated into English). In particular, I noted that Bertin has been highly influential in the fields of visualization and cartography — not only because he provided a framework for creating visualizations, but also because he created hundreds of examples to illustrate the breadth of possibilities.

Figure 1. Bertin’s dataset of
populations in 90 departments.

So now, 50 years after Bertin, I decided to mash up some of the text-based visualization ideas that I’ve been using with Bertin’s original French population dataset.

Bertin originally used a small data set of 90 French departments, with population counts for three different occupations (agriculture, manufacturing, and services). The dataset was small enough for Bertin to publish on half a page (page 100 of the English edition, shown here in Figure 1). The additional columns are totals and ratios.

Bertin takes the dataset and then creates nearly 100 different visualizations: bar charts, scatterplots, ternary plots, parallel coordinate plots, maps, cartograms, and so on (pages 101-137 in the English edition). A small subset are shown in Figure 2 below. But none of them use text.

Figure 2. Just a few of the many different visualizations that Bertin constructs from the same small dataset of populations per department.

I take the same dataset (why not?) and then create a dozen new, text-rich visualizations (shown in Figure 3). Typically, I use the names of the departments in the visualizations. Individual departments can be identified directly in the visualizations: there is no need to cross-reference to tables, no need to rely on interactions.

Figure 3: 12 new text-dense visualizations based on the same dataset as Bertin.

For example, I’ve previously talked about microtext line charts. In the center is a parallel coordinates plot where the lines connecting columns have been replaced with microtext – shown as a much larger image in Figure 4. Color is based on percent of occupation: green for high agriculture, red for high manufacturing and blue for high services. At a macro-level you can see the inverse relationship between agriculture and manufacturing. At a detail level you can trace the lines a bit more easily and directly identify them.

Figure 4: Microtext parallel coordinates chart. Names and codes for each department are along each line. Click for big version.

A full research paper on these visualizations is in a special issue of the Cartography and Geographic Information Systems journal (CaGIS) (volume 46, issue 2). The issue is specifically on the 50th anniversary of Jacques Bertin. Full volume description here and this link is a free view to the paper (first 50 viewers only).


About richardbrath

Richard is a long time visualization designer and researcher. Professionally, I am one of the partners of Uncharted Software Inc. I have recently completed a PhD in data visualization at LSBU. The opinions on this blog are related to my personal interests in data visualization, particularly around research interests related to my PhD work- this blog is about exploratory aspects of data visualization not proven principles.
This entry was posted in Data Visualization, Microtext, Text Visualization. Bookmark the permalink.

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