Graphic Sociology

Living Alone by Gender, Age Cohort in the US since 1850

What works

This post is an update to an earlier post about the increasing rate of Americans living alone. The first graph does an excellent job of visualizing the change in Americans’ tendencies to live alone, by age and gender. It’s clear that living alone is on the rise, especially for Americans over 45. It’s interesting that there seems to be a collective slow down in this trend in the decade between 35 and 45 when I suppose some of the late-to-marry people finally settle down and before the marital dissolution rate starts to fire up.

The graphics in this post accompanied an article by Eric Klinenberg in the New York Times Sunday Review that laid out the basic findings in his latest book, “Going Solo” that was based on 300 interviews with people living alone. He finds that while for some, living alone is an unwanted, unpleasant experience, most people who live alone are satisfied with their personal lives more often than not. In fact, they are more social, at least in some ways, than are their counter-parts who live with others. Singletons (his word, not mine. I prefer ‘solos’ in part because it’s an anagram), go to restaurants and other social spaces more often than do those who live with others.

Living alone in Minneapolis

In a number of cities, including Minneapolis, more than 40% of households are single-people households. The article included an interactive map down to the census tract level that shows what percentage of households in that tract were single-person households in 2010. I took a look at Minneapolis and St. Paul and found that the map supported Klinenberg’s qualitative findings. The highest concentration of solos is in the center city areas where opportunities to get out and be social in the community are the highest. The suburbs and rural areas have fewer solos.

I encourage others to use the map and see if their local cities replicate this pattern, that more solos live in ‘happening’ areas than in quieter areas. Of course, this could be caused by a third variable, the presence of households that are affordable for single-earner households…but there isn’t enough analytical power in the map tool to be able to sort out the dependencies.

What needs work

The information about who lives alone by age, marital status, and race that is displayed in the following long skinny stack of datapoints is the right kind of detailed information to use as an entrance into a deeper discussion about living alone, now that we’ve gotten a sense of the view from 30.000 feet. The problem is that this graphic is hard to read, too long for a single computer screen (but in order to make sense of it, one needs to see the whole thing at once), and too optimistic about what color differences are able to do than is reasonable.

The article does a better job of subtly navigating the movement from historical and international context into a detailed, robust analysis. By awkwardly pinning all the data points onto the stalk at once, viewers lose the ability to see patterns within data subsets. Here’s a test. Look at the following data and try to explain to yourself how race and living alone go together. Or how age and living alone go together. The graphic designer was hoping color would be able to do more than it has been able to accomplish here. The color is supposed to tunnel your vision down to a particular color-coded subset so that you can start to understand well just what it is about race or age or marital status that produces particular patterns in living alone. But I had a lot of trouble with the color frame because, quite literally, I had to keep shifting the frame around this graphic – it didn’t fit on my laptop screen. [Graphic designers often work on nice, roomy screens where they end up seeing more at once than their eventual audience who is probably peering at this thing from a web browser on a laptop or occupying half of a monitor somewhere.]

All the clustering around the mean is another problem that could have been avoided had the graphic been organized differently. As it is, all sorts of groups lump on top of one another down around 14%.

I also kind of hate that I can’t add categories together in any meaningful way here. I can tell that being a widow would put someone at high risk for living alone, but that’s kind of a no-brainer, isn’t it? I would have gotten more mileage out of visualizing the absolute numbers of people living alone by marital status, age, and race. Maybe over half of all widows live alone, but I haven’t the faintest idea how many widows there are in America so I don’t know if half of all widows is half a million people? Or 3 million people? Or whether it’s more or less than the 38% of separated people who are living alone. 19% of never married’s live alone, but because these people are likely to be young, maybe that is actually a larger absolute group than the 58% of widows living alone.

Final verdict: There was both a data fail and a graphic design fail.

Who lives alone? A demographic breakdown

References

Going Solo Cover

Klinenberg, Eric. (2012) One’s a Crowd. New York Times Sunday Review.

Weber, Susan and Beveridge, Andrew. (2012) [infographics]
Solo in America graphic Line graph looking of the changing percentage of singleton households in America, 1850-2000
More on their own here…and even more abroad American and International singleton households.
Mapping the US Census: Percentage of Households with only one occupant Interactive graphic of US singleton households by census tract.

2008 Political Party Identification in the US | David Sparks

2008 family income | David Sparks

What works

I like these maps because they use a smoothing technique currently being developed by David Sparks, a doctoral candidate in political science at Duke University. He uses data with the same kind of granularity – county or census-tract – but then smooths over the harsh (and probably unrealistic) edges that can occur where one county or census block abuts another with a different value for the variable of interest.

Here’s an example of a typical, non-smoothed map visualization using a map made by sociology students at Queens College that I posted about last week:

Percent change in hispanic population in the Dakotas, Minnesota, Iowa, Nebraska, Kansas, Oklahoma

As you can see in this map, each county boundary is stark and it appears that there are cases in which counties with no growth in the Hispanic population are right next to counties with sizable increases in Hispanic people. While this is technically true, there are many cases in which it is more useful to give viewers a clearer impressionistic image that depicts where population concentrations are the highest overall backed up by the granular data without displaying all of the granularity itself.

When it is important to portray an impressionistic point – there are more Democrats on the coasts than in the middle of the country – a smoothed map is a much more effective tool.

Sparks was able not only to achieve a better impressionistic glance by smoothing, he also varied the transparency based on the population density. For instance, because the population density in Montana is much lower than the population density in New York, he made Montana a much more ‘transparent’ state so that it would be easy to get an impressionistic sense of the cumulative spread of the variable. When looking at the purple map of Hispanic population increase in the middle states, no consideration was made for the population densities of cities versus rural areas. This visualization style tips the impressionistic balance away from the more densely populated areas.

What needs work

Since I am generally a fan of the smoothed maps for a clear visual depiction of a data story that is meant to be digested from the 30,000-foot view rather than the microscopic examination of differences between counties or even residential blocks, there is not much to dislike in Sparks’ new smoothed maps. However, I would not recommend the use of this kind of smoothed data for looking at micro-level trends. What Sparks offers is a great way to see patterns from 30,000 feet, one that improves on existing common practices in visualizing map data.

My one issue with the distribution of people’s political persuasion in 2008 is that the colors on the ends of the spectrum – blue and red – blend to form the color in the middle of the spectrum – purple. Therefore, places in which there are lots of independents look purplish. So do places where people living close together are evenly split between Republicans and Democrats. Color choice is essential. The color mix made by the colors at the ends of the spectrum should not mix to produce the color chosen to represent a third position. Small quibble and one that Sparks would have had a hard time satisfying. The colors associated with Republicans and Democrats have already been established.

References

Sparks, David B. (2011) Isarithmic maps of public opinion data [blog post and map graphics] dsparks.wordpress.com

Increase and decrease in Hispanic population in the Dakotas, Minnesota, Iowa, Nebraska, Kansas, and Oklahoma | 2010 Census

What works

This is a quiet story, the kind of thing that may or may not be picked up by a major national newspaper like the New York Times. Rural America is often used as a political flag to wave by politicians, but there is not often too much coverage of day-to-day life. The 2010 Census clearly shows,

The Hispanic population in the seven Great Plains states shown below has increased 75 percent, while the overall population has increased just 7 percent.

What is equally odd is that this story is running two graphics – the set of maps above and the one below – that more or less depict the same thing. I salivate over things like this because it gives me a chance to compare two different graphical interpretations of the same dataset.

The two maps above includes a depiction of the change in the white population as a piece of contextual information to help explain where populations are growing or shrinking overall. These two maps show that 1) in many cases, cities/towns that have experienced a growth in their hispanic populations also received increases in their white populations (hence, there was overall population growth) but that 2) there are some smaller areas that are experiencing growth in the Hispanic populations and declines in the white populations.

The second map shows only the growth in the Hispanic population without providing context about which cities are also experiencing growth in the white population. Looking at the purple map below, it’s hard to tell where cities are growing overall and where they are only seeing increases in the Hispanic population which is a fairly important piece of information.

Percent change in hispanic population in the Dakotas, Minnesota, Iowa, Nebraska, Kansas, Oklahoma

What needs work

For the side-by-side maps, the empty and colored circles work well in the rural areas but get confusing in the metropolitan areas. For instance, look at Minneapolis/St. Paul. Are the two central city counties – Hennepin and Ramsey – losing white populations to the suburbs? That is kind of what it looks like but the graphic is not clear enough to show that level of detail. But at least the two orange maps allow me to ask this question. The purple map is too general to even open up that line of critical analysis.

This next point is not a critique of the graphics, but a direction for new research. The graphics suggest, and the accompanying article affirms, that Hispanic newcomers are more likely to move into rural areas than are white people. Why is that? Is it easier to create a sense of community in a smaller area, something that newcomers to the area appreciate? If that is part of the reason new people might choose smaller communities over larger ones, for how many years can we expect the newcomers to stay in rural America? Will they start to move into metro areas over time for the same reason that their white colleagues do?

Are there any other minority groups moving into (or staying in) rural America? Here I am thinking about American black populations in southern states like Alabama, Mississippi, and Arkansas. Are those groups more likely to stay in rural places than their white neighbors? For that matter, what about white populations living in rural Appalachia. Are they staying put or are they moving into cities like Memphis, Nashville, and Lexington?

How do things like educational attainment and income levels work their way into the geographies of urban migration?

References

Queens College Department of Sociology. (13 November 2011) Changing Face of the Rural Plains [information map graphics] Queens College: New York.

Sulzberger, A.G. (13 November 2011) Hispanics Reviving Faded Towns on the Plains New York Times: New York.

The internet crosses the ocean

What works

I like the colors in the graphic above, however, the version I found does not come with a key but if you click through you can see one. The internet does not always deliver material the way it was originally designed or in the way that we would prefer it.

So I went looking for the original, the one that would probably have had a key attached to it, and found this map of the same information instead.

The internet's undersea world | The Guardian

I realize it is hard to see the tiny thumbnail of a graphic so you can either click through to the full version at the Guardian or look through the images I’ve distilled from the original below.

The internet undersea world | Thumbnail from the Guardian

Besides the map above, which shows where all of the cables are laid out and is very similar to the colored version at the top of this post, the Guardian cartographers/infographic designers included useful contextual graphics. Often, there is much more to maps than just the map, and to fully understand why and how the geography matters, it is critical to understand characteristics of the relationship that are not available through the map alone. For instance, in the case of undersea internet cables, the paths and linkages indicate that connections between, say, New York and London are probably quicker than connections between Minneapolis and Leeds. But it is also useful to know how fat the cables are because this is a good proxy for their bandwidth. If the traffic between two points in this network approaches the carrying capacity of the cable, connections might slow down, there would be reasons to build more cables, and so forth.

Undersea internet cable width | The Guardian

The Guardian carried on with this sort of critical analysis by showing how submarine operations sell capacity to other carriers, who mostly buy it as back-up. On the busy trans-Atlantic route, 80% of the capacity is purchased but only 29% of it is being used. This kind of arrangement is in place for times when communication bandwidth needs spike far, far higher than normal and when cables are cut.

World cable capacity, inset | The Guardian

Discussion

I was turned on to ferreting out these maps by a book I’m reading by Michael Likosky called “Obama’s Bank: Financing a Durable New Deal.” In the book, Likosky points out that one strand of the global internet infrastructure was privately financed, though still heavily reliant on governmental cooperation.

He writes:

In 1995, the US West finalized an agreement fo the construction of the Fiber Optic Link Around the Globe (FLAG). This $1.5 billion project would run a fiber-optic cable from the United Kingdom to Japan. In the process, it would link up twenty-five political jurisdictions. It contributed to a series of interlacing global information infrastructure project. Although underwater telegraphic cables had been laid at the close of the previous century, this project represented the first ever privately initiated and financed transnational communications link of this size and scale. FLAG was only as strong as the public guarantees of the twenty-five licensing authorities involved in legitimizing the project. In other words, it was a transnational public-private partnership.”

I was left wondering who financed the other strands of this aquatic internet infrastructure, realizing that it was probably more reliant on the public sector than the private sector, which is why FLAG is so unique. One of the reasons this matters is that global communications connectivity makes the current trans-national spoke and hub pattern of US business development possible. Without high speed communications connectivity, it would not be feasible for multi-national corporations to situate call centers and other communications-heavy activities far from the hub of commercial activities they are supporting.

If the US Federal government was indeed responsible for some of the early undersea internet bandwidth, I wonder if they had an inkling of how that might impact the development of off-shoring. It has been argued, though maybe not recently, that off-shoring is a good thing because it puts environmentally and socially negative jobs outside of America. Then we can reap all the rewards of growth up the management chain by locating the better jobs here. Clearly, it is irresponsible to locate environmentally detrimental projects in places were regulations are lax for the sake of increasing profits here. The same argument holds with respect to social ills like poor safety standards for workers, child labor, inhumane hours, and other negative working conditions. Increasing the ability to communicate instantly with far flung places makes the spoke-and-hub pattern more possible.

What needs work

Neither of the maps show who paid for the cables or who generates what kind of revenue from their use. I really want to know. I was hoping the color-coded one might do that, but without the key it’s impossible to tell.

References

Likosky, Michael. (2010) Obama’s Bank: Financing a durable New Deal. New York: Cambridge University Press.

Johnson, Bobbie. (2008, 1 February) How one clumsy ship cut off the web for 75 million people. The Guardian, Technology Section. Map graphic by telegeography.com.

American Agricultural Value map | Bill Rankin, Radical Cartography

American Cropland map | Bill Rankin, Radical Cartography

American livestock map | Bill Rankin, Radical Cartography

What works

Produced by Bill Rankin, Assistant Professor of History of Science at Yale University and editor/graphic designer at Radical Cartography, these three maps work together to show how American agriculture is organized both spatially and economically. [Click through to Radical Cartography to see much bigger versions. Since that site is in Flash, I can't embed links that take you directly to the big versions. Once you get to Radical Cartography click: Projects -> The United States -> Animal/Vegetable.] The top map here is the dollar value combination of the cropland and livestock areas in the US. For activist types, what’s even more exciting is the small black and white inset map that takes into account federal agriculture subsidies. The next two maps were combined to produce the top map – one shows how cropland is distributed, the other displays the distribution of livestock.

Bill Rankin is a rigorous researcher with a background in history and the thing he does best here is context. In order to understand the top map – which is what I believe Prof. Rankin wants viewers to store in their memory banks as the critical take-away – he first shows us how cropland and livestock land are distributed and then layers them over one another to show us how they are differentially valued. This type of data is sensitive to geography and location in two ways: 1. crops are sensitive to elements of geography like climate and available water supplies – there are no crops growing in the dessert of the American southwest 2. because the US hands out a variety of agricultural subsidies, the political boundaries of states have to be seen in conjunction with the crop distribution in order to understand how the political levers lead to the current subsidy scenario.

What needs work

The approach he takes is to color each county based on the percentage of area covered by a particular crop. This means that counties with multiple crops will end up with blended color values. For instance, cotton is coded blue and ‘fruits, nuts, and vegetables’ are coded maroon. This means that in some southern counties growing roughly equal amounts of cotton and ‘fruits, nuts, and vegetables’ the counties are neither blue nor maroon but purple. But wait. The blue of cotton might have combined not with the maroon of ‘fruits, nuts, and vegetables’ but with the brighter red of soybeans to produce that purple color. Confused? I am. I don’t know if those southern counties are a mix of peanut and cotton farms (likely) or a mix of soybean and cotton farms (also likely).

Another problem with the additive colors is that the choice of each color has a major impact on the impressionistic take-away of the maps overall. Corn is the most prevalent crop in the US covering over 144,000 square miles. The next most prevalent crop is soybeans which covers about 100,000 square miles. Soy beans and corn are often grown in the same counties (unlike, say, wheat which is a hardier crop and therefore ends up as a monoculture in northern counties where growing corn and soy are riskier endeavors). This means that soy and corn are going to have layering colors the same way that we saw crops layering with cotton along the Mississippi River in the south. Since the bright red color for soy is more aggressive than the somewhat subdued dusty orange chosen for corn, the impression we take away from the map is that soy is more prevalent than corn where the opposite is true. If the color values had been switched so that corn was coded in bright red and soy was coded in the dusty orange, the middle section of the country would end up looking like a corn field, not a soy bean field. Either way, the trouble with blending colors is that our eyes are not very good at looking at a color and saying – “Gee, that looks like it’s about 50% blue and 50% red.” We just say, “Gee, that looks like purple”. Or, in this case, “Gee, all those reddish colors either look like soy beans or maybe an 80% coverage of the ‘fruit, nut, vegetable’ category.”

A solution (that I am too lazy to put together)

In summary, the inclination to display crop and livestock coverage using maps was a solid inclination. I often criticize the inappropriate use of maps. In this case, I still think it could have gone either way. A clever Venn-diagram that used circles based on the total coverage of each crop which then overlapped with other crops in places where they are grown together could have been more illustrative. It would have been easier to see that corn is king, for instance, and that cotton and wheat are never grown together because cotton needs heat and wheat is cold-tolerant. The same sort of Venn-diagram could have been constructed for livestock. A final Venn diagram where the size of the circles is keyed to the dollar-per-square mile value of these crops could have then displayed how agriculture functions economically.

References

Rankin, Bill. (2009) Food: Animal/Vegetable” [Information Graphic, Flash] RadicalCartography.net.

US Map Iconographic

Graphic Sociology is back this week

After a summer hiatus, Graphic Sociology is coming back this week.

This graphic map of the US is just one of the interesting graphics I tucked into my digital knapsack of visual concepts over the summer. Regular readers know that I think maps are overused. There are good times to put them into use, and I have some solid examples coming up this week. But for now…

Graphic Map

The reason I posted this map is to spark a debate about the use of maps more generally. The graphic above is something I adapted from the blog Fuck Yeah Cartography (hey, I don’t name these blogs). The frame up there is something I basically “traced” and then messed around with so feel free to borrow my version if you like.

I thought about maps for communicating about sociological data and realized that I don’t generally see a reason to be cartographically accurate. An iconographic depiction of the US map does pretty much the same work as a depiction that had all the nooks and crannies and bends in the rivers carefully plotted out.

See subway maps:

Boston/Cambridge area T map

Massimo Vignelli 1972 Map of New York City's Subways

Subway maps are often better without anything more than the names of the stops and maybe a river (Boston’s map does not show the Charles River; New York’s map does show bodies of water).

Readers out in the blogosphere, do you agree that when maps are used to represent social science data they don’t usually have to be much more than iconographic? Do they have to have exact political boundaries? Does using an iconographic version help make the often-arbitrary quality of political boundaries more obvious?

For sure using an iconographic version is cleaner and therefore a bit easier to swallow all in one visual gulp. Are there other graphical advantages? Pedagogical advantages?

Coming Soon

I’ll post examples from around the interwebs the day after tomorrow, but for today, please ruminate on whether or not exact topography and political boundaries and all sorts of other things that could be represented well on maps have a place on maps that are used to show social science data.

What works

It’s a fun Friday post. Pull the slider to the right and watch the center of the US population move to the left (west) and south. What I like best about this is the interactivity. If it were just a static connect-the-dots it wouldn’t stay in your mind the way it does when you are the one doing the pulling of the slider. Getting those muscles involved, however minor their involvement might be, works more of your brain than if only your eyes were doing the work. My second favorite thing is more brain than brawn – the Census people found a way to remind us that in the beginning of our nation, we had fewer states. We added them as we went – almost always adding states to the west – so that can help explain why the center of the population originally started sliding leftwards. It has continued to slide leftwards (and towards the south) because those newer states have some lovely living conditions to offer. Not everyone loves the snow and ice of New England winters or the hurricanes of the southeastern seaboard.

What needs work

The center of population is a composite ‘score’ which tells us virtually nothing about why people are moving or where they are moving to…clearly, Missouri is not now a hotspot for internal migration. But if you were a school kid trying to grok the concept of the center of population, you might easily conclude that Missouri is a populous state.

I might have added some indicator of population sizes across US regions (state by state would be too confusing, but lumping by region would be fine).

References

2010 US Census. Center of Population

Drug Cartel Areas in Mexico | Christian Science Monitor

Drug cartels cause social ills

Unless you’ve had your head in a bucket since 2007, you are at least vaguely aware that Mexican drug cartels trafficking their goods into the US have caused significant social illness in Mexico, especially in areas close to the US border. Social illness here can be measured in cartel-driven murders, but that captures only the most gruesome, sensational branches of the drug virus. Besides the deaths are fear, anxiety, mistrustfulness as well as poverty, corruption, and vast inequality.

Is mapping the right way to understand Mexico’s drug trafficking problem?

The graphics here try to pack all of the complexity and destruction of those social ills into maps. Maps are rational. They allow us to feel we have a handle on the components that make up a problem. In this case, I am sure they are not explaining the whole story. I’m also not sure they are trying to explain the whole story.

What I like about the first map is that the map makers lay out the obvious: which cartels are where. Then they go one step further and highlight the contested territory. In case the colors aren’t coming through clearly, the white areas are the disputed areas. There are a lot of white areas.

And yet…

One would expect most of the violence in a situation like this to be in the disputed areas. But that isn’t the case. Most of the violence is near the US border. The border is another kind of contested territory, one that is much more important than white areas as far as violence prevention is concerned. In fact, those areas aren’t governed by one cartel or another because those areas are not critically important to drug trafficking. None of the cartels much care.

So let’s take a look at another map because I’m thinking the first one implies that we should find violence in the middle of the country.

Drugs and deaths in Mexico

Drug-related crimes south of the US border | The National Post

This graphic shows not only traffic patterns – where do the drugs go? – but also maps of where the deaths have been. It quickly becomes clear that the drug-related deaths are up near the US border, not in the ‘disputed areas’ highlighted in the previous map. In this map, (thanks unnamed National Post graphic designer) that undisputed area is left unclaimed and unlabeled. That’s a more accurate way to understand those regions and the inset series of maps below the main map do a good job of visually locating cartel-related violence.

The other thing I love about this map is that it specifies *which* drugs are being trafficked. Call me crazy, but I have found it odd that there is a great deal of talk about ‘drugs’ in Mexico as if there is no good reason to talk about which drugs are being moved where. Why is it useful to know which drugs are going where? First, it’s nice to know which drugs because different drugs have different price points per volume and weight. Economics matter. If one drug has a higher profit margin than another because it retails for more per ounce but doesn’t cost much more to produce/transport, one could assume that it will become more popular. Then again, demand matters, too. Even if pot is easy to produce, doesn’t mean you can convince cocaine users to try weed. They probably already tried it and moved on.

Another reason it matters which drugs we’re talking about is that detection and apprehension vary from drug to drug. An easy example: a pot sniffing dog probably won’t lead authorities to a stash of ephedra. What’s more, being able to tell where things are coming from and going to means that it is easier for authorities to target weak points in the routes. We know from news stories (I recommend looking at the LATimes, see references below), we know that drug runners pour much energy into protecting the drug routes right at the US border. But they aren’t digging tunnels under all of Mexico. There are points in the chain of drug traffic that are more vulnerable. Some of those points are deep within Mexico where it might be difficult to get well-trained, cooperative authorities with the necessary tools and manpower to perform raids.

My main gripe about these graphics is that they display this problem as a Mexican problem. This is not a Mexican problem. It is a Mexico-US problem. The demand in the US is pulling all those drugs up from south of the border. Looking at it this way helps introduce conversations about economic imbalances. I imagine that one of the reasons drugs come from Mexico is the same reason that many large companies choose not to have large labor forces in the US: labor is cheaper in Mexico. Various instantiations of poverty also tend to encourage corruption; encouraging local police to fight the cartels is hard when they are out-gunned and out-manned by cartels who can afford to pay off whoever they want including witnesses, other cops, border agents, and whoever else is likely to become cooperative after the application of a bit of grease.

Conclusion

The drug-related social illness in Mexico is an unfolding problem, one that has been discussed with more complexity elsewhere. I hope to illustrate that while the rationality of mapping patterns is appealing, it also tends to obscure complexity. It’s easier to misinform than inform with a map. They are deceivingly neat, these maps.

References

LATimes “Mexico Under Siege” special section, US News.

Llana, Sara Miller. (13 January 2011) “Mexico drug war death toll up 60 percent in 2010. Why?” in The Christian Science Monitor, World/Americas section.

National Post Staff. (31 December 2010) Graphic: Drug Terror Just South of the Mexico Border in The National Post. [Graphic].

Excellent use of a map

I spend a lot of time explaining which uses of maps are bad. In this case, the use of a map is spot-on. Nothing could better display this information than a map. So here’s what you are seeing. Due to the mechanism that determines flight pricing, some non-stop flights from City A to City B are cheaper than multi-leg flights that take passengers from City A to City C with a layover in City B. Figuring out where these curiously expensive cities are and then booking tickets through them (instead of to them) is called hidden-city ticketing. It’s technically forbidden by the airlines because it messes up their profit-making abilities, more on that later.

There are some markets – Atlanta, Cleveland, Salt Lake City, Charlotte, Detroit, Cincinnati or Chicago O’Hare – where prices are too high compared to the rest of the airfare market. If you want the longer version of why this is true, there is an excellent, lengthy, FiveThirtyEight/Nate Silver blog post, Which Airports Have the Most Unfair Fares?, on the vagaries of airfare pricing. Suffice it to say, if you happen to need to fly into one of these expensive cities, especially if you do it often, you are interested in figuring out how to avoid feeling like you are getting ripped off.

As a visual representation of this simple-but-hard-to-explain Point A to Point C via Point B scenario, a map is the best way to clarify the concept. Just look at how the visual works. A person starts in Fargo and wants to get to Chicago. If they crank that request through kayak, they end up with a direct flight to Chicago for $586 [ouch]. But if, instead, they tell kayak that they want to go from Fargo to New York with a layover in Chicago they end up paying only $213. Kayak let’s you tell it where you’d like to have a layover. (Detroit’s airport is surprisingly nice, for instance, and if I have to layover in the summer, I’ll go through Detroit.)

How can airlines charge less to fly a person a greater distance? Not all airline pricing is driven by fuel, snacks, and human capital costs. A good bit of it is driven by demand and supply – the classic economics story from your undergrad days. Some markets are not well served creating mini-monopolies for service in and out of those airports. Other markets, like New York, have a great deal of service provision forcing airlines to pull their prices into a lower, more competitive range.

Is it legal?

Perhaps you have read somewhere in your ticket’s fine print that the airline prohibits you from bailing out of your scheduled travel halfway through the trip. The New York Times asked a lawyer whether or not it’s even legal for the airlines to penalize people this way and how far they can go to punish someone caught doing this. It turns out, there are penalties the airlines can impose, but most of them can be side-stepped by savvy travelers. The Times presented recommendations, summarized here:

Making a habit of this certainly won’t endear you to the airlines. Most of them — the major exception being free-spirited Southwest Airlines — expressly forbid it in their ticketing rules. But those rules don’t carry the force of law, and most travel lawyers say that their recourse is limited. They could probably preclude you from flying with them in the future, but their case for demanding penalties is weak, and the risk of detection is low if you don’t book these kinds of routes more often than a couple of times per carrier per year.

Also, do not end up checking bags. They will end up at your final destination. Get to the gate early enough to ensure yourself space in the overhead bins.

Book your itinerary as two one-way flights. This should be logically obvious. If you are going from Fargo to Chicago but you book your ticket through to New York, you clearly won’t be wanting a return flight from New York because you never intended to actually see the Big Apple in the first place. The other kicker is that if you fail to report for part of your ticket, the airline will probably cancel whatever remains on the ticket. So book one-ways.

Don’t lie if the airlines catch you; lying increases your likelihood of being found guilty of fraud. Honesty is the best policy.

References

Silver, Nate. (6 April 2011) Which Airports Have the Most Unfair Fares? [blog post] The New York Times, FiveThirtyEight blog.

Silver, Nate. (4 May 2011) How to beat high airfares. Sunday Times Magazine. [Graphic The Art of Hidden-City Ticketing]