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.

Figures 1 and 2 from "Who Gives a Tweet?" by André, Bernstein, and Luther CSCW paper

What works

A new study will be presented in a couple weeks at CSCW by researchers in Human-Computer Interaction and Social Computing that used 43,000 ratings of tweets to explain what content twitter readers find useful.

In short, worthwhile tweets:
1. Are informative NOT boring
2. Are funny
3. Are concise (even shorter than 140 characters!)
4. Are hyper-timely
5. Avoid whining and navel gazing (Tweets about meals past, present, or future are ‘boring’)
6. Avoid using too much twitter mark-up like @ replies, hashtags, multiple links)

The graphics do a good job of providing a visual overview of the study’s findings. With my brief textual synopsis and the two graphics here I bet many of you reading this will feel like there is no need to go read the study itself. Just in case that’s true, you should know that in the author’s discussion section, they note that their raters were volunteers who were not randomly chosen and skewed towards the tech crowd. Perhaps there’s reason to believe that tech people would be more likely to appreciate informative tweets? Not sure. But I can say from my own research that there is a noticeable portion of the twitterverse that appreciates food-related tweets. Even within that sub-group, people tend to appreciate tweets about recipes or with pictures over tweets that just say, “I had a great #sandwich at lunch! Fresh mozzarella rocks.” A recipe is informative. A recounting of lunch or a whiny tweet about missing lunch is boring at best and annoying at worst.

The thing I like best about this piece is that many of the findings apply to communication in general, not just tweets. Folks, it’s probably true that whether you are tweeting or talking, nobody wants to know what you had for lunch unless they want to have what you’re having. And if they do, they’ll probably ask. No need to volunteer. Also: brevity is the soul of wit; and wit is wonderful.

As an aesthetic point, I think they got the colors about right. Red represents the not-worthy or bad votes that ought to stop; blue represents the neutral position; and green represents the good tweets tweeps should go for.

What needs work

This graphic came without a title and I added “Which tweets are worth reading?” because it was really hard to interpret the graphs at first glance without a title. There is enough information for interpretation in the caption, but I think a caption should not stand in for a title.

The title is the first thing we see.
The graph is the second thing we see.
The caption is the third thing we see.
In order to understand the graph, then, it’s logical to have a title first so that readers’ don’t get frustrated that they have no idea what these colorful bars represent (the axes only get us halfway there in this case).

The title follows their own recommendations: questions work well as tweets. I figured I would try it here as a title, see what happens.

References

P. André, M. Bernstein, and K. Luther. (In press). “Who Gives A Tweet: Evaluating Microblog Content Value.” To appear in CSCW ’12: Proceedings of the 2012 ACM Conference on Computer Supported Cooperative Work. (Best Paper Award honorable mention; top 5% of submissions)

Spring Counter | John Maeda

What works

John Maeda (now head honcho at RISD, formerly of MIT’s Media Lab) designed this simple interactive graphic in 2006 while contemplating the cyclical nature of life during the still grey days of a New England winter. His visualization shows the number of springs men can expect to have if they live an average life span for men in their country. Users input their age and select their country. The flowers in color are those in the user’s future; the ones in grey represent the past. Simple. Elegant. An infographic haiku.

What needs work

I have a slightly sunnier view of the past than does Maeda, perhaps. I think I would have colored both the past and present flowers, just used different schemes. Maybe it’s the social scientist in me, but I believe our past and future both provide the context for our present. Perhaps some past years have been grey, but the territory of the past is not generally a cemetery.

References

Maeda, John. (2006) Life Counter. Interactive web-based graphic.

See also:
Maeda, John. (2006) The Laws of Simplicity. Cambridge, MA: MIT Press.

Crony Capitalism | Original by Stephanie Herman posted to lewrockwell.com/blog

What works

There is a lot of information here, that’s one of the best things about these Venn diagrams. People often stick a single word or a phrase in one circle, another in the next, and that’s it. But this graphic proves Venn diagrams can help organize much more detailed, drilled-down information fairly well.

What needs work

For the sake of legibility and small font sizes, I probably would have made one of the circles white instead of black, then left the colored one a color, and had the middle oval shape have a much lighter background. That might have helped make some of the text easier to read. In particular, I think it’s important to read the names themselves, so I would have worked to make sure they stood out.

I might have snugged the titles up to the curve. Their spacing is a little haphazard. Clearly, in a circular format, one cannot use a vertical margin line, but then that leaves a question about whether to mirror the shape of the circles on the outside or the ovaloid shape on the inside. I would have tried it both ways and then picked one. Not sure what happened here.

References

Herman, Stephanie. (2011) Venn diagram of Corporate Cronyism in America on geke.us

SOPA Opera in Congress

Graphic Sociology opposes PIPA/SOPA

The proposed PIPA and SOPA legislation has been roundly criticized with solid, logical arguments here by danah boyd, here by Tarleton Gillespie who links to 16 other authors who have argued against SOPA/PIPA, and here in a letter Harvard Law Professor Laurence Tribe sent to members of Congress.

As someone who is a passionate scholar of collaboration (both in its cooperative and competitive forms), I worry about the legal and economic repercussions of SOPA/PIPA that have been brought up by the authors mentioned above as well as the negative impact the threat of discretionary censorship would have on the kinds of sharing and borrowing that have made the internet and digital files such a rich source of remixing, incremental improving, and all around innovation. There is no way I could put a dollar figure or other empirical metric on what might happen to remix culture (what the cool kids call it) or innovation (what the business schools call it) under a legal regime in which just about anyone can censor just about anyone else. I can say that the internet as we know it would cease to exist. I post things here on Graphic Sociology that I have designed and created without even mentioning Creative Commons or standard copyright or anything else. If people take my work and get something out of it, that’s fantastic. I don’t even care if they give me credit, though many creative people do, and for good reason. I’m afraid if PIPA and SOPA were to pass, fewer people would re-mix my work and that’s the best kind of use, as far as I am concerned. Reposting is fine, remixing is divine.

I also post the work of others and critique them as an academic, something that is legal under the fair use doctrine. I’m not sure how SOPA and PIPA would mesh with the particular provision of the fair use doctrine that I am exercising. Presumably, they can co-exist, but I certainly don’t have the resources to hire a lawyer and defend myself against anyone who might claim that I’m violating SOPA/PIPA. And as just one of a family of bloggers, any infringement claim against any blog post on the society pages could darken the entire site. So if someone got upset with me, that would mean lights out for Sociological Images, Thick Culture, and all the rest of the blogs here.

The rest of this post is written by guest blogger Alec Campbell of Reed College in Oregon.

Guest post by Alec Campbell, Reed College

What works

This graphic clearly shows that something caused a change between January 18 and January 19. That something was almost certainly the focused attention on SOPA and PIPA resulting from shutdowns, blackouts and other actions taken or led by a number of popular Internet sites (wikipdedia, reddit, the Social Media Collective, and even here at thesocietypages there was a blackout of sorts).

What needs work

The most important flaw in this graph is that it excludes members of congress who are undecided or whose opinions are unknown. Looking at the graph it appears that the distribution of opinion moves from 72% in favor before the Internet shut down to 39% in favor after. In reality the distribution is 15% in favor, 6% opposed and 79% unknown/undecided before the shutdown and 12% in favor, 19% opposed and 69% unknown /undecided after the shutdown. The two graphs aren’t comparable because they don’t include the same total number of observations. When comparing populations of different sizes one has to compare percentages which this graphic does not do.

In fairness, the article accompanying this graphic has links to much more detailed data on SOPA
and PIPA
that does include a full accounting of all members of congress. However, those data don’t allow for comparison over time, which is the central point of this graphic.

What I can’t figure out

It’s clear that there are fewer supporters on January 19 but it isn’t clear if the people who no longer support PIPA/SOPA now oppose it or if they are now undecided. Did the Internet action make converts or agnostics? Arstechnica is keeping a running tally of Senators who are now opposing PIPA, including many of the former co-sponsors of the bill.

The graphic could have used arrows to show movement from one of the three camps (supporters, opposers, undecideds) to help illustrate where the movement happened.

Why it Matters

None of this matters much if our interest is in the fate of SOPA/PIPA. It matters a great deal if we are interested in the power of Internet protest. This graphic is about the power of protest because the prominently displayed time dimension is only relevant to this issue. This graphic overstates the power of Internet protests by omitting the unknown/undecided category making it appear that people changed their minds overnight. Clearly, some did. The number of supporters dropped in absolute terms. However, the larger effect is in convincing people to publicly state their opinion or to finally make up their minds. It is entirely possible that the major effect of the Internet protest was to get congresspersons that were leaning towards opposition to publicly state their opposition and force some supporters to claim an undecided status. That is certainly something but it isn’t the same thing as changing supporters into opponents, which is what the graphic implies.

References

propublic.org following House support of SOPA

propublic.org following Senators support of PIPA

Lee, Timothy. (19 January 2012) PIPA support collapses, with 13 new Senators opposed. [blog post] arstechnica

Wall Street shrinking graphic from Dealbook, NYTimes

What works

From the accompanying article New Normal on Wall Street: Smaller and Restrained, it is clear that Wall Street (at least as measured at Goldman and Morgan Stanley) did not have a great year in 2011 and certainly didn’t match their performance when financialization was at its height in 2006. The economic problems in the European Union are having an impact. The weak domestic economy is having an impact. Regulatory changes, especially the Volcker Rule, are having potentially long-lasting impacts by changing the rules that allowed banks to practice such widescale financialization in the first place.

The Volcker Rule, which is aimed at stopping banks from making financial bets for their own accounts, could permanently eat away at bond trading revenue. Efforts to strengthen the derivatives market — such as making sure that trades are properly backed with collateral — could deplete the profitability of this business.

Mr. Hintz estimates that a Wall Street bank currently makes a 35 percent profit margin on its derivatives businesses, but he thinks the new rules could shrink that to 20 percent.

The question remains: are these impacts part of a durable restructuring of financialized banking or are they something that the capitalist tendency towards profit-seeking can overcome?

The policy issue remains, too: keep your eye on the Volcker Rule – it may sound boring but it matters and any changes to it or reinterpretations of it are worthy of your attention.

What needs work

I’m not convinced that the graphic answers the major question: is this temporary or evidence of a lasting shrinkage of financialization? I’m not even convinced that the graphic does a great job of interpreting Wall Street’s current status as compared to 2006. At the very least, in this case I firmly believe the text is more compelling than that graphic.

• The graphic needs to do a better job emphasizing the asset to equity ratio*. In short, this is a reflection of way each institution is leveraging its assets. That ratio is often seen as one of the simpler ways to think about what financialization means. The greater the leveraging ratio (in visual terms: the greater the difference in the size of the equity box versus the assets box), the more heavily a firm is participating in financialization – generating money from money, rather than from, say manufacturing or productivity. One simple trick I would have tried: make the assets portion of the box even lighter to help suggest an ephemeral quality.
• I would love to see more than two years in this picture. If 2006 was the height of things, is 2011 like returning to 2005? Returning to 1990? We need more data points to fully understand this trend.

What I can’t figure out

Curiosity question: Why was Morgan Stanley more sensible about compensation in 2006 than in 2011? Wouldn’t we expect bankers to be taking less in compensation now, after the righteous indignation of the American public has rained down upon them, than they were before the crash? Yet in 2006, Morgan Stanley dedicated only 42% of its annual revenues to compensation while in 2011 it dedicated 50.6% to compensation. Is that good business sense? It surely doesn’t make for good PR if the banks are trying to suggest that they have to compensate their bankers for performance – their performance looks kind of crappy this year.

* I am a complete newbie when it comes to finance and financialization so please feel free to broaden, complicate, or wholly overturn my arguments here.

References

Eavis, Peter and Susanne Craig. (19 January 2012) New Normal on Wall Street: Smaller and Restrained Dealbook: NYtimes.com. Graphic: Shrinking Wall Street

ZKO Rollercoaster // GREAT EMOTIONS from virtual republic on Vimeo.

What works

This short video does a pretty good job of teaching someone how they might learn to experience the suspense and exhilaration of classical music. I won’t try to explain it. I just thought Graphic Sociology readers might like it.

It got me thinking about how our senses work separately and together. I don’t experience chamber music as a rollercoaster but I might have learned to think of the peaks and swells of the musical dynamics this way if I had seen a video like this at the outset of my classical music listening. In a way, it’s a little like seeing the characters in a book come to life on screen in a movie before you get a chance to imagine them into life in your head. Once you’ve seen the actors and all of their particularities onscreen, it’s hard to reimagine the character otherwise.

As a radical empiricist, I hesitate to speculate about things like imagination that cannot be measured. Thus, let me be clear that I am not suggesting this one minute Vimeo could forever alter a child’s experience of classical music. Rather, I’m curious about the impact of an initial vision of something in comparison to both the initial aural and the subsequent visualizations of an experience. Does an aural first impression have the same impact as a visual first impression? After hearing a voice for the first time, can you imagine someone’s voice otherwise? I certainly can imagine aural qualities otherwise – I hardly remember the specific qualities of voices after hearing them only once. And I don’t think second and third visual exposures are as meaningful as the first one. But I have no clever experimental research in my back pocket that I can pull out to support or refute my position.

Are there any newcomers to classical music out there? Did watching this video provide enough of a framework to classical music listening that you think you would be more willing to do it going forward? And have you tried thinking of classical music as, say, a series of ocean waves (which was how I used to think of it)? Or some other kind of visual metaphor? Are you stuck thinking of it as a rollercoaster or some other amusement park ride (maybe the songs you don’t like are imagined as merry-go-rounds, pumping away repetitively to the point of nausea)?

References

Virtual Republic. (January 28, 2012) Video Advertisement of Classical Music as a Rollercoaster. For Zurich Chamber Orchestra.

The detailed view (before) | whitehouse.gov

What needs work

The complete view of the bureaucracy in the federal government is totally confusing, even when it is color coded and arranged so as to be easily viewed from 30,000 feet (see above).

BusinessUSA initiative aims to make it easier for businesses to interact with the US Federal government

What works

The US Federal Government has copied a kind of 311-style approach to helping businesses navigate the portions of the federal bureaucracy relevant to them. One department, one number, one website.

What interests me the most is the choice of those in the White House to promote this program through information graphics. This reflects the visual skills of Obama’s administration which have been evident since the middle of his campaign where not only those like Shepard Fairey but also his official campaign team launched an extremely successful visual campaign.

Shepard Fairey - HOPE

Obama Campaign Logo, 2008

The White House choice to use graphics in order to explain and promote their simplification of a portion of the federal government is also evidence of a growing shift towards the use of infographic stylings in the service of persuasion. Infographics gain a great deal of traction from the notion that humans tend to believe what they see. They gain even more traction when they mobilize numerical data that many people feel uncomfortable processing on their own. This graphic manipulates that sense of visual numeracy by taking a network (nest?) of dizzying resources and simplifying it into three nodes, each of which will bring businesses to the same pool of resources. ‘From many, one’ is an extremely powerful message, made all the more powerful by the strength of this visualization – it is clean, the nest part is detailed, and the resolution in the ‘one’ is not represented as a single node (which wouldn’t work as well because it would appear hyperbolic and would efface the modern entry modes into the federal government – the phone and the internet).

Deconstructing a Ford plant | The Atlantic

What works

This diagram of the closure of a Ford plant identifies both physical and temporal processes that a marvel of modern manufacturing has to undergo in order to cease production in a rational way. Environmental damage has to be mitigated – the paint shop is especially toxic and it seems to take workers years to handle that. [Let me register my vote here for automotive paneling that can be modified without paint or other dreadfully toxic processes. Surely, there has to be a better way. Sandblasting?]

The diagram is very smart. It maintains the size of the Ford plant – the thing takes up most of the visual space. Clearly, it could have taken up less space and given over more space to various explanatory text blocks and additional-information diagrams in sidebars, but I think that approach would have diminished the gargantuan nature of both the plant itself and the processes of shutting it down.

Second, the integration of a timeline measured by number of workers employed is just the perfect layer of information to pull the rest of the text-boxes together as a narrative. The timeline makes the whole graphic complete.

Third, I don’t mind the length of the text in the text blocks. It seems about right to me.

What needs work

I could have used some additional information about the relative uniqueness or typical-ness of an automotive plant closure (or even various elements of the plant closure process). The New York Times article Developers Revive Closed Auto Plants notes that about half of the nation’s 263 closed auto plants have been revived one way or another. In one case, an old Ford transmission factory now houses a community college with a 4-year nursing program on one corner, an aluminum scrap processor on another, a mobile facilities manufacturer in a third location, but is still more than half vacant. I was curious while looking at this graphic: Would Ford have had to go through the same kind of process with a transmission factory (they don’t paint transmissions so it seems it should have been easier in that regard)? When a plant is going to be repurposed, does Ford still have to do all the same ‘closing time’ activities or do those become the responsibilities of the new owner? Is that a negotiable term?

While a graphic would have been hard-pressed to answer all of those questions, I was hoping it would be able to at least address the idea that plants are both closing altogether and being repurposed – two related but not synonymous occurrences. In some places where the plants are closing, municipalities demand that their former owners take them down to slabs under the assumption that a slab is more appealing to a new owner than a facility that may need to be torn down and rebuilt.

Overall, I think the graphic is successful but could be better with more contextual information. I know some of that was in the article, but I am only reviewing the graphic, which I think should be able to stand alone.

References

Peck, Don. (April 2009) Disassembly Line. The Atlantic.

Christie, Bryan. (2009) Disassembly Line [information graphic]. The Atlantic.