PrintMultivariate Graphics
The examples we have explored so far have visualized two or three variables at once. Occasionally, you may want to visualize even more. One possible solution is to design data graphics that can then be incorporated into your map. A classic example of this is the use of pie charts as proportional symbols: an example is shown in Figure 7.5.1.
Interperting the information in Figure 7.5.1 is rather straight-forward. The circle diameters represent the weight of butchered meat in kilograms supplied by departements to Paris. The individual colors assiged to the "pies" includes black = ox or cow, red = veal, and green = sheep.
A more recent (and more complicated) example is shown in Figure 7.5.2.
Though the introduction of data graphics does permit the addition of many variables onto the map, this does not mean it is always the best solution. As shown in Figure 7.5.2, including a large amount of data in a map using mutiple symbolization methods can make it challenging to interpret. Additionally, multivariate graphics in general—and pie charts in particular—have well-documented disadvantages in terms of reader comprehension (Tufte 2001). Adding graphics that are already challenging for people to understand to maps tends to exacerbate such issues. Furthermore, the size of the graphics may lead them to obscure data in underlying layers. This is not to say that they should never be used, however—just with caution. And fortunately, there are ways in which such maps can be made easier to interpret.
Glyphs
One way that multivariate maps can be made more comprehensible is through the addition of user interaction. Figure 7.5.3, for example, is challenging to interpret as a static image, particularly as the glyphs (i.e., a pictograph) used are quite small and some of the color choices are not easily distinguishable. However, this is an interactive map. Clicking on a state creates an informative pop-up, shown in Figure 7.5.4.
While you will explore interactivity in this assignment, we will discuss the merits and challenges of map interactivity further in Lesson 8.
Student Reflection
Explore the use of multivariate glyphs to explore data about well-being(link is external). Can you think of ways in which this data might be symbolized instead as a static map or maps?
Chernoff Faces
Despite the difficulty of creating maps with multivariate glyphs, cartographers have long attempted to tackle this challenge through interesting experimentation. One particularly whimsical example of this is Chernoff faces. Chernoff faces are glyphs created by mapping variables onto facial attributes. When mapping the variable average household income, for example, a bigger smile might indicate a higher income level.
The Chernoff face technique was first proposed by Herman Chernoff in 1973. Chernoff's intention was to capitalize on the ability of humans to intuitively interpret differences in facial characteristics. One the one hand, humans can subconsciously note important differences in expressions that are almost unmeasurable. In addition, humans can ignore large differences that are common between faces (Chernoff 1973). Chernoff also noted that his method was desirable as it permitted the designer to map many variables (as many as 18!) onto just one graphic.
Chernoff’s original application of his technique used fossil and geological data, but Chernoff mapping is more commonly used to depict social thematic data such as well-being, or other topics related to human emotion. Chernoff mapping has been a contentious method since its introduction— some Chernoff maps such as this one: Life in Los Angeles by Eugene Turner, 1977 [14], have been heavily criticized for their use of stereotypical facial attributes and a cartoonish over-simplification of complex issues.
In response to these critiques, some cartographers have developed techniques for utilizing the advantages of Chernoff faces without some of the contentiousness. Heather Rosenfeld and her colleagues, for example, proposed using “Zombieface” glyphs rather than human faces—maintaining the emotive content and still capitalizing on people's ability to intuitively interpret facial features, but removing the human context and thus lowering the likelihood of reinforcing harmful stereotypes (Figure 7.5.6).
Take a closer look at the legend of this map—which demonstrates how the hazardous waste data was mapped to Zombie facial attributes—in the image below. As you can see, the map focuses on visualizing the presence of unknowns and uncertainty in the mapped dataset (we'll discuss further techniques for visualizing uncertainty later in this lesson).
Chernoff Zombies are among several creative solutions recently proposed: a fun example is shown in the following quasi-Chernoff map: Mapping Happiness(link is external). It maps happiness, or well-being, across the United States using emoticons. Though these icons do not encode as many variables as Chernoff faces, they share the benefit of visualizing data at-a-glance using facial expressions.
Of course, the novelty of this "zombie-chernoff" symbolization method is interesting. As shown in the legend, there are many subtle differences in symbols that may be difficult for readers to distinguish. Thus, despite the inventiveness of such symbolziation experimentations, the cartographer should always question how easily it is for the map reader to encode the information and make sense of what they see.
Recommended Reading
Esri Blog: Chernoff Faces(link is external) by John Nelson.