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Sunday, September 6, 2009

American Vice: Mapping The 7 Deadly Sins

This piece originally appeared in Wired. I have added my own comments and observations.

We're gluttons for infographics, and a team at Kansas State just served up a feast: maps of sin created by plotting per-capita stats on things like theft (envy) and STDs (lust). Christian clergy, likely noting the Bible Belt's status as Wrath Central, question the "science." Valid point—or maybe it's just the pride talking.

Number of fast-food restaurants per capita.
This map, frankly, surprises me. I guess what surprises me is that there is no shades of grey in it. Either you have a very low concentration of fast-food restaurants (most of the country) or you have a very high concentration in Texas and the DC area. And I have no idea about the patch of high concentration just to the west of the DC area. Part of that patch even seems to cover the Smoky Mountain National Park, so its location is a bit surprising.

Number of STD cases reported per capita.
This is a bit of a surprise too. I have a feeling it has more to with poverty than with lust, looking at the broad swath of the southeastern states that it covers. Most people in richer communities probably manage to keep it under wraps rather than having to report it, I guess. And I have no idea what those patches in South Dakota and New Mexico signify. Indian reservations, perhaps?

Average income compared with number of people living below the poverty line.
This is probably the least surprising of the maps. The major metropolitan areas are all well-represented in this map, which makes sense because that is where the income disparity is greatest. In fact, a map of population density would probably look identical to this map!

Expenditures on art, entertainment, and recreation compared with employment.
This one is a bit more difficult to come to terms with. First of all expenditure on art, to me, does not imply sloth. Moreover, it is easy to see how this map can be easily distorted by tourist spending rather than local spending: look at the big patch over Yellowstone National Park. Of course, entertainment spending is very high there compared to employment, but to equate that with sloth is quite meaningless. In fact, the same pattern repeats around the Denver area, parts of California (Sequoia, Yosemite, etc.) and probably a lot of other recreational areas. Overall, not a useful map at all.

Number of violent crimes (murder, assault, and rape) per capita.
This is a pretty insightful map. It does not jibe well at all with most people's idea of big cities as being dangerous in general (notice the lack of coloring around the NYC or Chicago areas, and very light coloring around LA). To me, this seems to paint more of a picture of the drug trade and/or poverty than anything else. That would explain the abundant coloring over Florida and the southeast. The coloring in New Mexico and Montana is harder to explain, but it might be Indian reservations once again, just like in the case of lust.

Total thefts (robbery, burglary, larceny, and grand theft auto) per capita.
Theft more nearly seems to follow population density than violent crime. But the lack of coloring around NYC is still fairly surprising. But the coloring is more similar to greed overall, which, as I already noted, seems to be pretty closely aligned with population density. And I don't see how this graph can be too different from that for greed because as a bank robber once responded to the question of why he robs banks, that's where the money is!

Aggregate of the other six offenses—because pride is the root of all sin.
This is a bit of a cop-out. I think they could have come up with a better definition for pride. Yes, pride is the root of the other sins, but pride has its own inherent symptoms also. Such as the amount of money per capita spent on cosmetics, diet plans and/or cosmetic surgery. It also brings up the question of how the other sins were aggregated into this map - what weightings were used (for instance, I don't consider sloth anywhere near as serious as crime, whether violent or non-violent), how the denominators were normalized for (some of the measures are per capita, while others use employment and the number of people living below the poverty line in the denominator).

Overall, it was probably an interesting exercise. Hopefully they will use the feedback from this exercise to improve these maps and produce newer maps that plot out other useful and interesting things.

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