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.

Best sociology books of all time | W. W. Norton

What works

Anthony Giddens, Mitch Duneier, Richard Appelbaum, and Deborah Carr put together this list of keyworks in sociology starting way back in 1837. W. W. Norton illustrated each book with a simple diagram that helps illustrate what that book’s main argument is – some are kind of humorous if you happen to be a sociologist – and then laid the whole thing out in a snake stack.

Here’s what I like:

+ the book list includes Harriet Martineau who is often overlooked
+ the book list is short enough to fit on a poster – ask most sociologists about keyworks and they are likely to still be going on about it a week later.
+ the book list uses graphic depictions of the content – spare but intriguing – rather than an annotated bibliography of short summaries. This version is so much more interesting for having said less. Want to know?: read the book.

What needs work

I would have included Simmel, Toqueville, and Mary Douglas. I might have tried to find a way to represent multiple works by the same author (like Marx, Durkheim, and Weber who appear more than once here) in the same grid slot so that more other authors could be included. In order to accomodate that change, I think it would have been possible to arrange this not in a rigid timeline, but in a partitioned grid with early, middle, and contemporary works (or something like that). Even categories like: >100 years ago, between 50 and 100 years ago, in the last 50 years.

One more quibble – the background color has too much green in it to read as a soft brown. I would have liked a soft brown better. Somehow with the green, it ends up with a repellant quality. HOWEVER, I bet this is one of those situations where the poster was designed to be printed, looks fantastic coming off whatever printer it was calibrated for, and looks slightly more pukey on the screen. Trade offs, trade offs.

Recommendation

I have been mightily enjoying W.W. Norton’s tumblr which is where I found this poster. The archive is the best way to get introduced to what they’ve been doing. It certainly is neither primarily about information graphics nor primarily about sociology, but it is wholly intellectual in the most fun kind of way.

If you are more of a twitter person and/or you would like sociology information more than the potpourri of information on the tumblr, W. W. Norton has a twitter feed, too.

References

Giddens, Anthony; Duneier, Mitch; Appelbaum, Richard; and Carr, Deborah. (16 August 2011) Keyworks in Sociology. [information graphic] New York: W. W. Norton.

Loneliness and Fat Consumption among middle-aged adults | Cacioppo and Williams

What works

Written by a social neuroscientist, the book Loneliness contained this heartfelt graph on page 100. Yes, even I feel the phrase ‘heartfelt graph’ is an oxymoron. But the way that the graphic artist worked over the details here – the way the edges of the butter columns are rounded, the way that the paper is folded back, even the way that the grid lines are rendered makes this two bar graph captivating. I am also intrigued by the mix of digital and hand-rendered – most everything was hand-drawn except the axial numerals and the text labels. I like the mix. I probably would have liked it better if the lettering had also been hand-rendered but I think that’s just me being a bit too precious about hand-rendered images.

The book describes the way that loneliness is a neurological event, one that overlaps with social and psychological parameters to produce a more or less predictable set of occurrences. In this graph, authors Cacioppo and Williams are discussing recent findings that indicate lonely middle-aged adults tend to get more of their calories from fats than non-lonely middle-aged adults. For younger adults, loneliness does not seem to have an effect on either food consumption patterns or exercise patterns.

Socially contented older adults were thirty-seven percent more likely than lonely older adults to have engaged in some type of vigorous physical activity in the previous two weeks. On average they exercised ten minutes more per day than their lonelier counterparts. The same pattern held for diet. Among the young, eating habits did not differ substantially between the lonely and the nonlonely. However, among the older adults, loneliness was associated with the higher percentage of daily calories from fat that we noted earlier (and that is illustrated in Figure 6).

Perhaps because this book is about empathy-inducing loneliness, it is especially nice to see a tenderly hand-drawn graph rather than something far less engaging, the standard excel-produced item. The same numerical information would have been conveyed – and in fact that information was conveyed fairly well in the text itself – but the hand drawn element indicates that the topic is worthy of more than quantitative concern alone.

I am about halfway through this book and so far, I recommend it. Even if you are not interested in loneliness, the book does a good job of demonstrating how diverse research fields can be woven together to examine a topic common to all. The book draws from psychology, sociology, evolutionary biology, and neuroscience to help explain why some people are lonelier than others and what the impact of loneliness can be on the short-term and long-term health and social outcomes for individuals.

What needs work

For the record: I cannot draw or render or do anything good with a pencil besides finding a way to hold my hair out of my face. I tend to be overly appreciative of drawings and people who can draw. My critique here is of myself and others like me who swoon over the hand drawn.

I also wish there might have been a way to get the exercise information included, if not on the same graph, than on a companion graph right next to the butter sticks.

In a public announcement sort of way: folks lonely and nonlonely seem to take much solace in eating. That’s a large amount of fat consumption.

References

Cacioppo, John T. and William Patrick. (2009 [2008]) Loneliness: Human nature and the Need for Social Connection. New York: W.W. Norton.

Reading Suggestion

In case you missed it over the weekend, the New York Times ran a story about information graphics and the people who use them to communicate with the public. Unsurprisingly, Hans Rosling of Gapminder in Sweden – one of the new heroic figures in infographics – was the man in the picture and the first to be quoted. Rosling deserves the attention – gapminder had fairly humble origins and has grown because it draws from sound data, it is free to use, and it does a predictably good job of providing a visual overview of country level comparisons over time. Natasha Singer, the journalist who wrote the article, also interviewed Professor Ben Schneiderman of the Human-Computer Interaction Lab at the University of Maryland and Jim Bartoo of the Hive Group. And that’s where the article obliquely addressed the growing divide between infographics that are meant to be serious, complex, and complete and those that are meant to be beautiful and compelling, but user-directed. This second sort of infographic is the sort of thing that gets accused of being ‘info-porn’ and often covers information that is of dubious social value. Do we really care about celebrity’s twitter usage patterns? Is that as important as the work Hans Rosling does? What can the academic side of information graphics makers learn from the commercial side?

The article has a slightly different take on these questions,

The fact that serious software companies are now tree mapping the pop charts is a sign that data visualization is no longer just a useful tool for researchers and corporations. It’s also an entertainment and marketing vehicle.

but it’s clear that there are some divisions within the world of infographics that are worth considering more seriously. Nobody ever claimed that all writing is of the same species or that everything on TV is trying to do the same thing. Documentaries are not like sit coms which are not like dramas which are not like soap operas…but then again, they can all be found on TV and thus have some common elements. It’s no surprise that there is a wide variety of infographics out there with distinct goals.

Figuring out just how each type fits into the information ecology and changes the expectations about the entire range of infographics is worthwhile. When graphic designers started to take infographics seriously, it raised the bar for social scientists who were trying to communicate with information graphics. No longer was a chunky bar graph going to look sophisticated. It might look so generic and grade-school that it would reflect poorly on the overall quality of the argument.

References

Singer, Natasha. (2 April 2011) When the data struts its stuff. New York Times, Business Day Section: Slipstream.

Hans Rosling. Gapminder.org Hans Rosling is also a frequent TED Talks presenter.

Jim Bartoo. Hive Group.

Ben Schneiderman. Human Computer Interaction Lab at the University of Maryland.

Hillman, Dan [Director and Producer] | Rosling, Hans [Presenter] (7 December 2010 was first broadcast date) The Joy of Stats BBC. [Documentary] 60 minutes.
In the US you can stream The Joy of Stats from Hans Rosling’s gapminder.org website. Perhaps this works in other countries as well, but I haven’t had a chance to test it.

http://www.datapointed.net/

Data Pointed

I experienced the vastness of the internet today, stumbling across Data Pointed which is a not-new blog featuring original data visualizations. Why haven’t I come across it before? I wish I knew the answer to that as well as to the related question: how many other interesting data visualizations sites are out there that I do not know about?

What you see above is the most recent post at Data Pointed by Stephen Von Worley. He produces sophisticated graphics across a wide array of subject areas. Just so happens that this one is about the inter-relationship of the income distribution and the tax distribution which is of keen interest to social scientists, and policy people in particular. I find this visualization to be beautiful looking but a little hard to read. Each year is represented by a line, that line is drawn through all of the income brackets you see along the x-axis. As the line passes through these income brackets it changes both color and thickness. Thick red lines indicate areas in which people are paying more than their share of taxes; thin blue lines are areas in which people are paying less than their share of taxes. Von Worley had this to say:

“A modified Reagan-era tax system lingers to this day. To his credit, Dubya did reduce taxes on very low earners, so they’re no longer getting hammered. But, the people at our economy’s core – the full-time workers earning between $20,000 and $150,000 a year – still pay at up to double the rate of the ultra-wealthy, relative to what history suggests they should.”

Personally, I had a hard time drawing that message out of the graphic, despite the fact that it is so beautiful and elegant that I was compelled to stare at it and read the explanation until I could figure out how it worked.

McDonald’s Distances in the US

Von Worley himself notes that Data Visualization was not the popular success he had hoped, at least not at first. [Note: Graphic Sociology isn't exactly a success in terms of page traffic, but it has a core of steady followers generating a four-digit count of unique page views per week.] Data Visualization got popular after Von Worley created the map graphic below that uses blobbiness to indicate distances between points on the US map and the nearest McDonald’s. The farthest you can get from a McDonald’s in the US is 107 miles and you would be in South Dakota.

Distance to nearest McDonald's in the US | Stephen Von Worley

Does the map work?

I am not entirely sure the map is working – again, it is beautiful. Beautiful is compelling and being compelled, I wanted to spend time looking at it. I also love that it kind of looks like fat globules. How appropriate and subtly political. We also end up with a very good proxy for American population density. Not bad. But what would have been even more awesome is if we could tell this was a distance map without having to read the caption. I want to know that there’s a McD’s at the center of each blob and that what I’m supposed to notice is the distance I need to go to get from the darkness to the light. (In my version, I might have had the centers of the blobs be dark and the peripheries be light but I’m guessing it wouldn’t read as well visually no matter how well it fits with my understanding of McD’s as a morally shady place.)

References

Stephen Von Worley (15 March 2011) Shifting Burdens: U.S. Taxes By Income Level Over The Years at Data Pointed Blog.

Hi all. I put together the following biblio after some of the folks in the audience during my presentation at The Image conference (UCLA). They were wondering how to create better information graphics.

This list of resources is good if you are sick of using excel and killing yourself trying to use Word to make graphics or wondering what you should be aiming for within a categorical type (what’s a good bar graph, anyways?). Admittedly, none of the resources are perfect, most do not tell you how to use which software programs. But there are good pieces of advice and instructions in just about all of them.

The list is available for download here. And in html below.

Infographics Biblio – emphasis on how-to

Cleveland, William. (1994) Elements of Graphing Data. Summit, NJ: Hobart Press.
+ Table of Contents and Chapter 1: http://hobart.com/Elements.PDF

— (1993) Visualizing Data. Summit, NJ: Hobart Press.
+ Table of Contents and Chapter 1: http://hobart.com/Visualizing.PDF

Few, Stephen. (2004) Show Me the Numbers: Designing Tables and Graphs to Enlighten. Oakland, CA: Analytics Press.
+ Perceptual Edge blog

Graff, Gerald and Catherine Birkenstein. (2009) “They Say/I Say”: The moves that matter in academic writing. New York: W. W. Norton.
+ This book is not about graphics. I find that it offers a useful framework for figuring out
which contextual information needs to be included in a graphic in order to provide
enough context for a useful discussions. If academics creating infographics include some
history of an argument or predict what critics might say, they will create a stronger, clearer
graphic just the way writers create stronger, clearer arguments if they situate their
argument within a field and address predicted criticism before they arise.

IBM Research: Many Eyes Visualization Tool.

Roam, Dan. (2009) Unfolding the Napkin: The hands-on method for solving complex problems with simple pictures. New York: Portfolio Trade, a division of Penguin.

Rosling, Hans. (2005-present) GapMinder Visualizations and tools to make your own visualizations.

Seagram, Toby and Jeff Hammerbacher. (2009) Beautiful Data: The stories behind elegant data
solutions. Sebastopol, CA: O’Reilly Media.
+ Table of contents

Steele, Julie and Noah Iliinsky. (2010) Beautiful Visualization: Looking at data through the eyes of experts. Sebastopol, CA: O’Reilly Media.

Tufte, Edward. (2006) Beautiful Evidence. New Haven, CT: The Graphics Press.
— (2001) The Visual Display of Quantitative Information, 2nd ed. New Haven, CT: The Graphics Press.
— (1990) Envisioning Information. New Haven, CT: The Graphics Press.
— (1997) Visual Explanations: Images and Quantities, Evidence and Narrative. New Haven, CT: The Graphics Press.

Ware, Colin. (2004) Information Visualization: Perception for Design, 2nd ed. Morgan Kaufmann.

Wong, Dona M. (2010) The Wall Street Journal Guide to Information Graphics: The dos and don’ts of presenting data, facts, and figures. New York: W. W. Norton.
+ Table of contents and sample pages

Hans Rosling | MSNBC

What Works

Hans Rosling argues that by raising the living standards of the globe’s poor people we can avert a population growth disaster. He uses statistics and on-stage demonstrations to do it. Worth watching. Over at TEDtalks. Happy to see a kindred spirit having his day with TED.

I approve of the rockstar version of Hans Rosling’s portrait so I cribbed it from MSNBC. Thanks graphic designer out there somewhere, working to make statisticians more visually stimulating.

References

Rosling, Hans. (July 2010) Global Population Growth TED talks.

Gap Minder is Hans Rosling’s website. It features many more animations than just the one about population growth as well as tools to build your own animations. The emphasis is on country-level data.

What works

grain edit is a graphic design blog that will expand your mind’s collection of graphic design to remember. It is not restricted to information graphics – in fact, most of the work has nothing to do with information graphics – but it includes a wide variety of graphic design and illustration. I’m posting this reading suggestion on a Sunday because it is a bit off the core topic here which is supposed to be social science data presented via information graphics. Still, if you are at least mildly interested in graphic design, surely you will find something you like over at grain edit. Not only can you see what ‘the work’ looks like, you can also read interviews, and get photographic studio tours with some of the designers they feature. And if you are super keen on the whole concept, their blog roll will take you on a fantastic cyber-tour of thought provoking eye candy.

My favorite links:

• Penguin Books – Book Cover Design which is listed on grain edit but actually curated by thingsmagazine
• Lumadessa Studio Visit with Josh Brill which I like because of the space and Josh Brill’s flora and fauna series


Blue Jay by Josh Brill





Chickadee by Josh Brill





Dodo by Josh Brill

References

grain edit blog

things magazine

Lumadessa

Readers, dear readers, I know my regulars are sick of hearing about how much I hate pie charts. But I came across Stephen Few’s latest newsletter – Mr. Few is a man who is a professional information graphics guru and he hates pie charts, too. Of course, he is a professional and he doesn’t use the word hate. When I saw his newsletter, a smug smile of satisfaction crossed my face and I thought to myself, “Self, maybe the readers are sick of hearing you complain about pie charts, but they might want to hear how someone else complains about them. Because: Look! He has illustrations!”.

Why pie charts are not the best choice

Think of this as graphic art for geeks, abridged from Stephen Few’s newsletter, “Save the Pies for Dessert” from his information graphics educational company The Perceptual Edge.

In the first pie chart, a person might be convinced that pie charts are a decent tool. Just look at how easy it is to see that the light green segment is 25%? Super easy. Without even thinking, it’s obvious, which is the mark of a good information graphic. As for the other segments…same problem as always. Most humans are not good at visually estimating rounded volumes.

See how easy it is to see the 25% segment here? | Stephen Few, Perceptual Edge

And what if we simply rotated the pie a little?

Pie chart rotated - much more difficult to estimate the size of the 25% segment

Now it is much more difficult to get a quick visual estimate of any of the segments, even the 25% piece and all Mr. Few did was rotate the pie. Mr. Few notes that when people use software to generate pies like this, they have little control over what piece of the pie ends up in which position. He explains that our eyes have very few visual metaphors for segmented circles, one of which is the clock: “In the earlier example, our ability to decode the green slice at 25% was assisted by the fact that the green slice began at the 6 o’clock position and extended neatly to the 9 o’clock position.”

Perhaps we solve the problem by just sticking the numerical value right near the slices of pie? He does that and then adds another layer saying, “Why stop here? ….We can solve this problem by directly labeling the slices with both the company names and the values…” which leads to this graphic:

Overly labeled pie charts | Stephen Few

But that leads him to a conclusion that I support which is that this information is much easier on the eye if it’s just in a simple table. The pie itself just confuses things.

Turning the pie into a table | Stephen Few

Much clearer in a table, no? I think so. And so does Mr. Few.

References

Few, Stephen. (July 2010) “Save the Pies for Dessert” newsletter for Perceptual Edge.