The lie and story of a Symap

A Symap is a map which uses the densities and shapes of typographical characters to convey scale data. They were commonly used in the heyday of typewriters and character-based printers which could print "graphics" by using contrasting ASCII characters.

My attempt at a Symap deals with NYC noise complaints per capita per borough, as seen below (full-screen version here):

There are a number of problems with this map. Though the characters are not nearly different enough to make a convincing story, the bigger issue is the per capita scale. The five boroughs vary greatly – 26 complaints per thousand residents in Manhattan versus less than 5 complaints per thousand residents on Staten Island, with Brooklyn, Queens, and the Bronx all somewhere in the middle.

Unless I am explicit in my terminology, I could unintentionally mislead you by showing a graphic derived from one calculation while you assume it comes from another. In this graph I used the borough populations so as to only compare the boroughs to themselves (e.g. 42,048 complaints divided among 1,626,159 residents in Manhattan gives the Manhattan per capita value). However, per capita often suggests a comparison to a whole (perhaps the entire city), espcially when the entire city is shown on the map.  In this case, we would take 42,048 complaints and divide among 8,405,837 city residents, and get a different value.

Borough Borough-
Manhattan 26 5
Brooklyn 10.5 3.3
Queens 6.3 1.7
Bronx 7.5 1.3
Staten Island 4.9 0.3

As seen in the table above, the choice of calculation can alter the "facts". Borough-centrically, the Bronx has more complaints per capita than Queens. But city-centrically, it is reversed. This data, once mapped, becomes the story.

Furthermore, we can simplify the story for the sake of a concise headline and say (in one case) that "the Bronx is noisier than Queens" without ever giving thought to what a noise complaint means. Does it mean that the Bronx is actually louder than Queens, or might it mean that Bronx residents are more inclined to complain, regardless of noise level?

This Symap is incredibly simple, but through analyzing the Lie and the Story we can see that there is an unbelievable amount of room for opinion, persuasion, and plain old error. Taking on larger and more complex mapping projects will require me to always question my methodology, even before the data makes it onto my map.

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