In Lesson 1, Part I: Visual Communication, we introduced both the discipline of semiotics and the idea that map symbols are a set of sign-vehicles that the map maker uses to communicate some idea or concept to map readers. In this concept gallery item, we will focus on how cartographers create individual map sign-vehicles and map sign systems. One analogy that we can use to understand the concept of sign-vehicles and sign systems is that of the alphabet. Most western languages use the same set of sign-vehicles (i.e. the letters of the alphabet), but different sign systems (i.e. they have different arrangements of letters that stand for the same referents (the real-world objects or ideas). For example, different people may use the words mother (English), Mutter (German) or madre (Spanish) to refer to the same referent. It is only possible to understand a sign-vehicle if you know which sign system is being used and understand that sign system. In most maps, the legend is the primary way that cartographers help map readers understand what sign system is being used in the map.
We can make it easier for map readers to interpret maps if we match the visual characteristics of the map symbols to the characteristics of the data. Because there is no standard cartographic sign-system that map readers have agreed upon (e.g., that a red square is always used to represent a school), cartographers can match any symbol to any type of map feature. However, creating map symbols (i.e., sign vehicles) without carefully thinking through how they might be interpreted is likely to result in map readers drawing incorrect conclusions about what they see in the map. For this reason, cartographers (and other information visualization designers) have come up with a series of guidelines about how to match the different visual characteristics that we can use for creating symbols to the characteristics of a data set. In the remainder of this concept gallery item, we will review these guidelines.
Before we talk about the visual characteristics you can use to create map symbols, it is important to think about two data characteristics that might influence how you choose which visual characteristics to use: spatial dimensionality and level of measurement.
The spatial dimensionality of an object is a topological measure of what the object can cover. Another way of thinking about dimensionality is that it is the number of directions over which you can make a size measurement. For example, you can measure the size of an area by measuring its length and width (2-D). A true line (1-D) can only have it’s length measured (i.e., if it has width, it is really an area, not a line). A point has neither a length nor a width; it only has a location (see Figure 2.cg.6, below). One distinction we need to make is between the number of dimensions a feature has in reality and the number of dimensions we use to represent the feature in the computer. For example, in a small scale data set (e.g., of the entire U.S.), cities are probably represented as point locations. However, your experience with cities in reality has likely shown you that cities always cover an area. At larger scales (e.g., the San Francisco metropolitan area), cities are likely to be represented as 2-D objects. For the purposes of designing symbols, you should focus on the number of dimensions that the features you want to represent have in the particular data set you are using.
Data can be collected at various levels of measurement: nominal, ordinal, interval and ratio. This concept was reviewed in detail in Lesson 3, Part II of Geog 482 (note: go to sections 8, 9, 10). Although the distinctions between ordinal, interval and ratio data can be very important in some contexts (e.g., statistical testing), for cartographic purposes, we will group ordinal, interval and ratio measurements into one group and really only make a distinction between data that record differences in kind (i.e., nominal data) and data that record differences in amount (i.e., ordinal data).
Now that you have an idea of how data characteristics might have an impact upon symbol design, we can begin discussing what visual characteristics (or variables) you might choose to use when creating map symbols. A French cartographer and graphic designer named Jacques Bertin was the first researcher to try to create a comprehensive set of guidelines about how to match visual variables with different levels of measurement and the spatial dimensionality of map features. Since then, several other cartographers have expanded upon and modified Bertin’s original system. Here, we present a set of examples (based on the work of many cartographers) of each of the visual variables for each spatial dimension and a set of guidelines for matching the variables to different levels of measurement.
Nominal visual variables are best for emphasizing differences in kind, or qualitative differences. The four visual variables that work best for showing nominal differences are: color hue, shape, arrangement and orientation. See Figure 2.cg.7, below for examples of these four nominal visual variables used each in point, linear and areal symbols.
Ordinal visual variables are most effective for depicting differences in amount, or quantitative differences. The visual variables that do a good job of showing ordinal differences are: color value, color saturation, size and texture/grain. See Figure 2.cg.8, below for examples of these four ordinal visual variables used each in point, linear and areal symbols.
When you are designing your own maps, you may find that you need to use a combination of visual variables to best represent the phenomenon you are depicting. It is important to carefully consider how you combine variables so that the combinations you choose logically represent the relationships you are trying to emphasize in the data. For example, imagine you are making a map of the location of different farms that produce various fruits and vegetables (e.g., apples, oranges, grapes, cantaloupe, watermelon, lettuce, carrots, potatoes, green beans and beets). One symbolization option might be to just randomly assign color hues to each type of produce. However, the distinction between fruits and vegetables might also be important to you. In this case you might combine color hue and shape to emphasize these differences (see Figure 2.cg.9, below). We will talk about multivariate symbolization in more detail in Lesson 6’s Multivariate Symbolization concept gallery item.
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If you are interested in investigating this subject further, I recommend the following:
- Carpendale, M.S.T. 2003. Considering Visual Variables as a Basis for Information Visualisation. http://dspace.ucalgary.ca/bitstream/1880/45758/2/2001-693-16.pdf University of Calgary.
- Bertin, J. 1967/1983. Semiology of graphics: Diagrams, Networks, Maps. Madison: University of Wisconsin Press. (French edition, 1967).