Being taught that the message of your work should influence how you present it (the medium), I started looking at different visualization techniques a couple of weeks ago in order to improve my visual vocabulary. I came across the paper ” Tour through the Visualization Zoo” by Jeffrey Heer, Michael Bostock and Vadim Ogievetsky. Here you’re shown a few of the more sophisticated and unusual techniques for visualizing and interacting with diverse data sets. The visualizations were created using an open source language, so that you get to play around with them on your own if you’d like to.
Computer scientists, psychologists, and statisticians have studied how well different encodings facilitate the comprehension of data types such as numbers, categories, and networks. For example, graphical perception experiments find that spatial position (as in a scatter plot or bar chart) leads to the most accurate decoding of numerical data and is generally preferable to visual variables such as angle, one-dimensional length, two-dimensional area, three-dimensional volume, and color saturation. Thus, it should be no surprise that the most common data graphics, including bar charts, line charts, and scatter plots, use position encodings. Our understanding of graphical perception remains incomplete, however, and must appropriately be balanced with interaction design and aesthetics.
I’m currently working on my second draft of Visualization Species (first draft here), and I think I’ll be using the categories that Heer, Bostock and Ogievetsky suggest in this paper:
- Time-series data (ex streamgraph, small multiples, horizon graphs)
- Statistical distributions (ex stem plots, parallel coordinates)
- Maps (ex flow maps, cartograms)
- Hierarchies (ex node-link diagrams, dendrograms, sunburst, treemaps)
- Networks (ex force-directed layouts, arc diagrams, matrix views)