Sociological Images

Yesterday I stumbled upon a really great interactive graphic posted by the Guardian that summarizes the degree to which a number of rights and benefits are available to gays and lesbians in the U.S., by state. Each state is represented as a segment radiating out from the center of the circle; each colored ring represents a particular right, benefit, or protection:

• Light blue = whether state has a law addressing discrimination or bullying in the school system
• Purple = state-level hate-crime laws
• Pink = protection against housing discrimination
• Green = protection against employment discrimination
• Blue = right to adopt (lighter shade indicates individuals are allowed; darker shade means gay and lesbian couples are allowed to jointly adopt)
• Yellow = right to visit partner in the hospital
• Red = marriage

The different shades indicate differences in the scope of coverage (say, full marriage rights vs. domestic partnership — and it has been updated to reflect yesterday’s passage of the bill outlawing same-sex marriage in North Carolina — or whether a law bans discrimination based on sexual orientation but not gender identity); the Guardian website explains each issue. Their post also allows you to hover over a state and get a more detailed summary. Here’s the info for Nevada, for instance:

The graphic also lets you scale states by population if you want to get a better sense of the proportion of the U.S. population living in areas that do or do not provide these protections.

A couple of years ago, Lisa posted about the ubiquity of McDonald’s in the U.S., highlighting a map that showed the distance from the nearest McDonald’s. As a follow-up to that, Data Pointed posted a map that illustrates the unevenness of its market dominance across the country. If we plot the markets dominated by the top 8 hamburger-based chains (in terms of sales), we see that though McDonald’s is the single largest burger chain in most of the U.S. (all the black territory), other chains outsell McDonald’s in many markets, with the Sonic-dominated Southern Plains the most obvious:

In fact, there are relatively few places where McDonald’s has an outright majority of the market share; in most areas, the combined sales of its 7 largest competitors are more than McDonald’s:

This illustrates the importance of the ubiquity shown in the map Lisa originally posted. McDonald’s might like to truly dominate every market; it ideally would probably like to have a monopoly on them. But it doesn’t have to in order to successful and to exert incredible market power. It doesn’t need to control every individual market in order to exert enormous influence on the fast food industry, from setting the standard for labor practices to influencing which varieties of potatoes farmers grow for the french fry market. The “be everywhere” model allows it to win the larger burger chain war, even if it loses some regional market battles.

For the last week of December, we’re re-posting some of our favorite posts from 2011.

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Kristina Killgrove, anthropology professor at University of North Carolina, Chapel Hill, asked a great question about a set of maps posted at Move On.  The maps compare the states that allow gay marriage with the states that allow cousin marriage.  Most Americans find cousin marriage to be disturbing and testimonials from married cousins about their deteriorated family relations and social stigma attest to it.  The idea behind these maps, then, is that cousin marriage is genuinely weird or gross, and yet many states grant cousins the right to marry, but not gays and lesbians.

In fact,  emerging evidence that cousin marriages do not significantly increase the risk of birth defects suggest that the stigma and laws against cousin marriage are unwarranted.  A doctor cited in the study suggests that disallowing their marriages or rights to have children are tantamount to “eugenics or forced sterilization.”  Even if there were significantly increased risks of genetic disorders, Dr. Bittles argues, “People with severe disorders like Huntington’s disease, who have a 50 percent chance of passing it on to their offspring, are not barred from marrying because of the risk of genetic defects… so cousins should not be, either.”

In any case, U.S. aversion to cousin marriage is culturally and historically contingent.  That is, it is related to our particular time and place.  Worldwide, more than 10 percent of marriages occur between first cousins and cousin marriage, historically, has been quite desirable.

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Lisa Wade is a professor of sociology at Occidental College. You can follow her on Twitter and Facebook.

Billions of dollars are spent on christmas trees — real and fake — each year.  The data for 2009:

Most of the companies benefiting from this spending are small businesses:

Fake trees appear to be growing in popularity, but the sales of real trees do not appear to be slowing:

The states that benefit most from Christmas tree sales include Oregon, Michigan, North Carolina, New York, New Jersey, Ohio, Pennsylvania, and Washington, and Wisconsin:

Found at Intuit.

Dolores R. sent a link to a map created by Derek Watkins to show how the names given to geographic features reflect cultural patterns. Using a database of names officially accepted by the U.S. Board of Geographic Names, Watkins mapped generic terms used in the names of streams (excluding “creek” and “river,” which are commonly used throughout the U.S and were plotted in a gray that fades into the background):

The generic terms reflect historical immigration patterns. “Kill” appears in areas of New York originally settled by the Dutch; “cañada,” “arroyo,” and “río” indicate areas of Spanish exploration and settlement in the Southwest; of course, Louisiana and the surrounding area still reflects its French heritage through the term “bayou.”

The map reflects internal migration and cultural diffusion within the U.S., as well. For instance, Watkins suggests that the patch of red in southwest Wisconsin, indicating the use of “branch,” may be due to the lead mining boom in the early 1800s. Lead mining attracted Appalachian miners to the area, and they may have influenced local naming practices, bringing along terms common in Appalachia.

For more on the interconnections between geographic names or terms and larger cultural patterns, Watkins suggests reading Names on the Land: A Historical Account of Place-Naming in the United States, by George Stewart (2008). Another excellent source is Wisdom Sits in Places: Landscape and Language among the Western Apache, by Keith Basso (1996).

If I ran the Federal scary anti-smoking image warning program, I might show smokers the list of health-related terms that show up most in the states with the highest cigarette smoking rates.

If you take the smoking rates by state, and throw them into the Google Correlate hopper, you can see the 100 search terms that are most highly correlated with that reported smoking behavior. That is, the terms that are most likely to be used in high-smoking states and least likely to be used in the low-smoking states.

Is the result just a lot of noise? Maybe, but I don’t think so. Here are the smoking-related terms in the top 100:

• camel no 9
• cigarette coupon
• cigarette coupons
• marlboro coupons
• my time to quit
• safe cigarettes
• stopping smoking
• time to quit
• fire safe cigarettes
• ways to stop smoking

So that’s good for face validity — a list of random search terms isn’t likely to have all those smoking terms on it.

But after the smoking terms, the thing that jumps out is the health-related terms. We know from the Google flu tracker that people search for their symptoms. So these caught my eye.

Here is a screen shot of the first page of results:

I selected “stages of copd” as the term to map. The map on the left is the smoking rates; the one on the right is the relative frequency of searches for “stages of copd.” That is, chronic obstructive pulmonary disease, a nasty disease the most common cause of which is smoking.

Here is the complete list of health-related terms among the top-100 correlates with smoking rates:

• stages of copd
• abnormal mammogram [the association between smoking and breast cancer remains controversial]
• aneurysm symptoms [more common among smokers]
• hardening of the arteries [exacerbated by smoking]
• jaw swelling
• red rash on legs [could be psoriasis, which smoking increases the risk of]
• swelling in neck
• swollen lymph node in neck
• pilonidal cyst pictures

Lymph node swelling, which is implicated in the jaw and neck searches, most often reflects infection — which smoking causes.

How strong are the connections? They’re not the strongest I’ve seen on Google Correlate. The “stages of copd” search is correlated with smoking rates at .77 on a scale of 0 to 1. It’s not uncommon to find correlations of .93 (which is the relationship between “quiche” and “volvo v70 xc”).

But considering the smoking rates come from a sample survey (the National Survey on Drug Use and Health) which includes random error, and states are somewhat arbitrary geographic units, that correlation seems pretty high to me. Here’s the scatterplot:

What is the correlation causality story here? I can’t say. But the simplest explanation is that these are the terms smokers (and maybe those who know or care for them) are most likely to Google relative to non-smokers — not that they are the most common searches smokers do, of course, but the searches that differentiate them from non-smokers. The simplest explanation is the best place to start.

I like this list of conditions because in my experience smokers sometimes have the attitude of “you have to die of something.” But it’s not just the chance of dying that smoking increases — it’s a lot of possible forms of suffering along the way.

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The Google Correlate tool is showing the great potential for using Internet search activity to investigate layers of behavior and meaning behind other observable social phenomena, such as race/ethnic composition, health behavior, and family patterns.

Dmitriy T.M. sent us a link to some images at the Brookings Institution, based on analysis by William Frey, illustrating the very uneven changes in average of of the population by state in the U.S. Overall, the U.S. population is aging, with rapid growth in the population over age 55 and individuals over age 45 surpassing those aged 18-44, according to the 2010 Census:

But this varies by region of the country. Here’s a map showing growth in the +45 population, illustrating the rapid growth in the Southwest and much of the South:

Nevada had the single highest growth in the 45+ population, with this group increasing by 50% between 2000 and 2010. West Virginia growth comes in last among this group (excluding Washington, D.C.), increasing by 15%. Of course, growth doesn’t tell you anything about the underlying numbers.

Many of the same states that had rapid growth in the 45+ population also saw significant gains in the under-45 range. But unlike with the 45+ population, where every state’s population was stable or growing, a significant number of states actually experienced a loss of the under-45 group:

Again, Nevada’s #1, with 28% growth. Michigan, on the other hand, had an 11% loss.

These patterns have significant implications for individual states — everything from estimating how many elementary schools they’ll need to build in the future, to how many health care workers they’ll need to educate or attract, to a state’s or region’s ability to attract different types of employers, and so on. And states will be grappling with these issues under very different circumstances. It’s one thing to, for instance, address the potential health-care needs of the elderly in a state where every age group is increasing; it’s another if your working-age population is fleeing.

Brookings has a much more detailed interactive map that includes information on aging; you can look at the dependency ratio (population under 18 or over 65 per person of working age) and look at age changes by major metro areas in addition to states.