I recently had a post at the Cross Validated blog about how small multiple graphs, AndyW says Small Multiples are the Most Underused Data Visualization. In that post I give an example (taken from Carr and Pickle, 2009) where visualizing multiple lines on one graphs are very difficult. A potential solution to the complexity is to split the line graph into a set of small multiples.
In this example, Carr and Pickle explain that the reason the graphic is difficult to comprehend is that we are not only viewing 6 lines individually, but that when viewing the line graphs we are trying to make a series of comparisons between the lines. This suggests in the graph on the left there are a potential of 30 pairwise comparisons between lines. Whereas, in the small multiple graphics on the left, each panel has only 6 potential pairwise comparisons within each panel.
Another recent example that I came across in my work that small multiples I believe were more effective was plotting multiple points elements on the same map. And the two examples are below.
In the initial map it is very difficult to separate out each individual point pattern from the others, and it is even difficult to tell the prevalence of each point pattern in the map including all elements. The small multiple plots allow you to visualize each individual pattern, and then after evaluating each pattern on their own make comparisons between patterns.
Of course there are some drawbacks to the use of small multiple charts. Making comparisons between panels is surely more difficult to do than making comparisons within panels. But, I think that trade off in the examples I gave here are worth it.
I’m just starting to read the book, How Maps Work, by Alan MacEachren, and in the second chapter he gives a similar example many element point pattern map compared to small multiples. In that chapter he also goes into a much more detailed description of the potential cognitive processes that are at play when we view such graphics (e.g. why the small multiple maps are easier to interpret). Such as how locations of objects in a Cartesian coordinate system take preference into how we categorize objects (as opposed to say color or shape). Although I highly suggest you read it as opposed to taking my word for it!
Carr, Daniel & Linda Pickle. 2009. Visualizing Data Patterns with Micromaps. Boca Rotan, FL. CRC Press.
MacEachren, Alan. 2004. How maps work: Representation, visualization, and design. New York, NY. Guilford Press.