Gentle readers, our first map of the new year is one that I am finally getting to eleven months after it was brought to my attention by a reader, Matthew. It concerns a favorite subject of mine, American English dialects, and was produced by hobbyist Richard Aschmann.
The style of this work will be familiar to those with an interest in language mapping, with boundary lines delineating different pronunciations and vocabularies. Here’s another one from the Telsur Project at the University of Pennsylvania:
While Mr. Aschmann’s work is of a conventional type, it is also by far the most complex I’ve ever seen, and therein we find the problem. There is simply too much going on in this one map to be comprehensible.
One of the primary things a map reader is going to want to do is look for spatial patterns. After all, this is quite probably the entire point of having a thematic map — showing a relationship between what happens and where it happens. If you there isn’t one, then you might as make a table, instead. Now, in the case of Mr. Aschmann’s map, there’s certainly a connection between where people live and the sorts of speech patterns that come up. The problem here, though, is that this pattern is nearly impossible to discern.
To be able to see how dialects change over space requires that you look at a certain region, determine its characteristics, then look at a second region and do the same, then a third, and so on, comparing them all along the way. Your eyes sweep across the map, and each time you take a quick read and compare with what you’ve already seen. But this only works if that read can indeed be quick. With Mr. Aschmann’s map, figuring out what’s going on in any one location is a significant chore. There are so many possible symbol types, sorting through the legend is a challenge. Just figuring out which set of lines your target area falls within can be difficult, given how many layers crop up. Even if a reader is interested only in looking up data on a single place, and not making comparisons or seeing patterns, the density makes it nearly too much trouble to be worth checking. Once you’ve successfully figured out what’s going on with one region, you can move on to the next region to compare. But by the time you’ve waded through the decoding process a second time, you’ve already forgotten what the first region means. Comparison, and therefore pattern recognition, is nearly impossible, because your brain simply can’t hold that much complexity at a time or absorb it fast enough.
Compare this with a simpler map of rainfall, below. Here, it’s easy for you to quickly spot the distribution. The color pattern is simple, and you need only look for one data set, instead of twenty. There are a couple of other reasons that this map is a bit simpler to read, as well, having to do with the symbology type, but the great majority of the difference is simply in complexity.
I understand well the urge to include multiple data sets on a map, and longtime readers may recall seeing an overly complex, multivariate map of my own on this site. The more complexity you can show, the richer the story and the more versatile the product. The map quickly begins to be more than the sum of its parts. Putting two thematic layers on a map gives you three data sets — one each for the layers, plus allowing you to visualize the relationship between the two layers. One plus one equals three. But all of this is worthless if it becomes so complex as to be unclear. A map with one clear data set is worth more than a map with fifteen data sets you can’t read. Good mapmaking is about making space intelligible — otherwise, why make a map?
This map needs to be split into a series, each of which tells its portion of the story clearly. The topic it is attempting to portray is deep and rich and complex, and any single map that attempts to encompass so much is likely to end up like Mr. Aschmann’s: uselessly dense. Not every subject can be condensed into a single visual statement, and there is no shame in breaking it down into a series of simpler points in order to clarify.
Before I leave off, I’ll also mention one other thing. This map, like so many others, is going to be even less intelligible to the millions of people out there with color vision impairments. If you happen to have standard color vision and would like to see what I’m talking about, check out Color Oracle by Bernhard Jenny.
I’ve been trying of late to focus more on major items in my critiques, rather than dealing with too many nitpicky details, in order to not repeat too many points from earlier posts. Thus, I leave discussion of the rest (such as the quality of the labeling) to you, dear readers.
One Nice Thing: Mr. Aschmann has done a valiant job of trying to ensure that everything is layered clearly, which is no small task given how many data sets are crammed in. No one data set actually obscures another. There’s still far too much going on to be useful, but it’s not impossible to pull some information out of it if you’re willing to sit down and work at it.