I reviewed Susan Schulten’s new book, Mapping the Nation: History and cartography in 19th Century America, for publicbooks.org but there were so many images (90%) that did not make it into that review I decided to write a post here, too. This blog tends to focus on contemporary graphics, but information graphics are not new and the historical context of infographic forms is fascinating, especially in light of research that examines the status of information graphics as the output of inscription devices (Latour and Woolgar, 1979). How did we end up with the selection of graphic forms we now have? In what way were these images originally used and by whom?
The images in Schulten’s book – and on her superb companion website – are mostly maps, but there are also a surprising number of information graphics. As Schulten writes, maps and mapping were both made possible because America became a country (and thus had a government that could be petitioned to support the expense of creating maps and provide a centralized repository in which maps could be collectively held and made available) and they made America an imaginable possibility. In short, the establishment of American government made mapping possible and the existence of national maps made America an imaginable possibility. Without being able to see not only the colonies, but also the rest of the North American continent, it would have been far more difficult to imagine and pursue westward expansion, for instance. The first chapters of the book provide a nice companion to Benedict Anderson’s “Imagined Communities” that focused on the role of newspapers and novels in creating a national imagination. Schulten is also interested in printed matter, but for her the big deal is mapping.
Maps as propaganda
If mapping in the immediate post-colonial and early frontier eras was exciting – and it was – it got even more exciting during the contentious lead-up to the Civil War. One of the maps I’m including here is propaganda for the abolition of slavery. I have included the whole map as well as a close-up, but I encourage you to click through to Schulten’s companion website where you will find high quality scans of all the maps that will give you far more detail than I am able to show here.
Propaganda is typically not something maps are used for now, at least not in the blatant fashion of the pre-Civil War years, but it is true that maps are depictions of political boundaries and, as such, are ripe for the delivery of political messages. [For a more recent example of US maps used in politically charged ways see modern artist Jasper Johns.]
What I found more intriguing were the maps that displayed their political messages almost invisibly using choropleth techniques. The choropleth technique is still extremely common today and relies on shading assigned to political divisions like state or county lines. Census tract boundaries can also be used. It’s debatable whether or not census tracts are political boundaries but they certainly are not boundaries based on natural features like streams or mountain ranges. Some of the first choropleths were developed to show more precise locations and densities of slave labor in an effort to discredit Southern claims that slavery covered the South like a blanket without which Southern economies would freeze.
Another attempt at a similar political message – to display variation in slave holdings in order to prove that other economic models were viable and operant in the South during the 1850s – failed as a map but introduced an interesting graphical form. This Missouri map shows county boundaries within each of which there is a small graphic with the overall intent of providing:
A view of the numerical relation of slaves to agricultural wealth in Missouri, Showing in each county the number of slaves to every ten thousand dollars worth of farms and farming implements according to the US Census of 1850.
To interpret the map, then, keep in mind that counties with more dots rely more heavily on slave labor rather than mechanical labor. Of course, counties with few dots could either be utilizing human labor more efficiently, and thus have lower slave-to-machine ratios, or they could have had very little agricultural practice of any kind, slave or free. Because the graphic elements represent such an obscure, unfamiliar measure (slaves-to-machines), the map ought not to be considered a great success. But it is an excellent example of maps depicting thematic data without resorting to choropleths. We could use more of this boundary pushing map-graphic hybridity now
Disease mapping in America
With some chagrin, I admit Schulten’s book corrected an inaccurate belief of mine with respect to the use of maps in the detection of disease. I had erroneously thought that John Snow was the first person to use maps as a tool to detect the cause of disease when he pinpointed the cause of London’s cholera epidemic to a public water pump. He was not the first to use maps to discover disease. Americans in Baltimore, Boston, and New Orleans were mapping all sorts of potential causes of diseases like cholera including weather patterns, train routes, proximity to open water, and the eventual culprit, proximity to public water. Snow was the first to hone in on the cause, but he was not the first to use maps. Further, he was likely aware of American public health mapping efforts.
I am including one more image – not a map – to show just how fresh 19th century graphics were. This is a graphic that uses states as categories but breaks them out of the map form in order to present them as squares. It is easier to divide squares into percentages, which is just what Francis A. Walker did to show the types of church denominations present from one state to the next. It is easy to see why he avoided using a map – it would be difficult to divide the irregular shapes of states into precise percentages. Further, even if he could divide the irregular areas properly, if he then filled the areas with particular denominations, it would have appeared that the denominations were geographically tied to particular places within the states. His choice of squares as representations of the states is logical. From this graphic solution to his problem we end up with a visual technique for representing all sorts of information that is bound to related categories.
Latour and Woolgar. (1979) Laboratory Life: The Social Construction of Scientific Facts. Beverly Hills: Sage.
Cairo, Alberto. (2013) The Functional Art: An introduction to information graphics and visualization. Berkeley: New Riders, a division of Pearson.
A functional art is a book in divided into four parts, but really it is easier to understand as only two parts. The first part is a sustained and convincingly argument that information graphics and data visualizations are technologies, not art, and that there are good reasons to follow certain guiding principles when reading and designing them. It is written by Alberto Cairo, a professor of journalism at the University of Miami an information graphics journalist who has had the not always pleasant experience of trying to apply functional rules in organizational structures that occasionally prefer formal rules.
The second part of the book is a series of interviews with journalists, designers, and artists about graphics and the work required to make good ones. This part of the book is as much about the organizational culture of art and design and specifically of graphics desks in newsrooms as it is about graphic design processes. The process drawings are fantastic. I’ve included two of them here. The first by John Grimwade is multi-layered, full of color and dynamic vitality. These qualities were carried through into the final graphic but are often very difficult to build into computer-generated images. I wondered if the graphic would have been as dynamic if it had come from a less well-developed hand sketch (or no sketch at all).
The second is a set of photographs taken of a clay model by Juan Velasco and Fernando Baptista of National Geographic that was used to recreate an ancient dwelling place call Gobekli Tepe that was in what is now Turkey. Both of these examples lead me to the iceberg hypothesis of graphic design – the more the design that shows up in the newspaper or magazine is just the tip of an iceberg of research, development, and creative work, the more accurate and engaging it is likely to be.
As a sociologist I am accustomed to reading interviews and am fascinated by the convergence and divergence in the opinions represented. In this case, I especially appreciated that Cairo’s interview questions touched on the organizational structures and working arrangements, as did his own anecdotes throughout the book, to provide an understanding of the opportunities and constraints journalists and information graphic designers face. Their work is massively collaborative and the book works to reveal the bureaucratic structures that come to promote and impinge upon design processes and products.
There is a fifth part to the book, too, a DVD of Cairo presenting the material covered in the first three chapters of the book. I admit, I have rarely been a large fan of DVD inclusions. They are easy to lose, scratch and/or break. But assuming the DVD is intact and accessible, I never know when I ought to stop reading and start watching. And even if the book has annotations indicating that an obedient reader should stop reading and start watching the DVD, this assumes the reader is willing and able to put down the book and fire up the computer. The only time I can imagine using the DVD is as a teaching aid in class to give the students a break from having to listen to me all the time. Unfortunately, that is prohibited by Pearson.
Still, it is worth watching because Cairo has a great voice and he is able to discuss interactive content/design in a way that is not easy in the pages of the book. While some of the discussion repeats themes from the first part of the book, there are new examples from additional designers, including some who have been Cairo’s students, which might be of interest to people thinking of signing up for his online course.
What does this book do well?
The book does a great job of explaining the decision making behind graphic design. The sketches, process drawings, and recounts of the conversations that went on in editorial meetings gave important depth of context. The organizational culture and day-to-day expectations of the newsroom tend to encourage the use of templates and discourage exuberant creativity. Cairo explained that this Brazilian prison graphic that eventually won the Malofiel design award also won him a reprimand from his boss who proclaimed it to be “ugly”. In practice, conceptual distinctions between art and technologies for comprehension are made rigid by bureaucratic structures in which, “the infographics director is subordinate to the art director, who is usually a graphic designer,” and that this arrangement, “can lead to damaging misunderstandings.”
The more prominent argument follows from these peeks into the backstage of journalism. Infographics and visualizations are technologies, not illustrations. Cairo writes that:
The first and main goal of any graphic and visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach….The form of a technological object must depend on the tasks it should help with….the form should be constrained by the functions of your presentation….the better defined the goals of an artifact, the narrower the variety of forms it can adopt.
One of the writing techniques that Cairo uses is summarizing his take-away points from previous paragraphs in quick lists of pointers or key questions. Cairo incorporated these quick lists gracefully into the writing style and I never felt like I was reading a textbook. Still, the quick lists make it easy to use the book as a reference. The index, bibliography and detailed table of contents add strength to the book as a reference source, too. Note to the publisher: I found it frustrating that the book did not include a list of figures, especially given the subject matter.
One of the greatest strengths of this book is the diversity of sources from which Cairo draws his material. Yes, he uses graphics he has developed in many cases which is hugely valuable because he is able to provide insights into the development processes. However, he also draws from graphics old and new [see an old one he pulled out of an archive at the University of Reading about weaving in the industrial revolution], from magazines, newspapers, and the internet, made by freelancers, in-house designers, and students, and in languages other than English (some of which are translated, some of which impressively need little translation). My favorite graphic in the book was one I never would have come across that uses pieces of fruit to describe the surgical procedures used to achieve sexual reassignment.
This diversity serves as an example of the breadth of Cairo’s experience in the world of journalistic information graphics. It is also a testament to his real joy in the subject. Many authors of design books are happy to fill the pages with their own work. Cairo is surely talented enough to have done. Instead, he chose to showcase an incredible range of designers and styles. This diversity, combined with the accessibility of the writing, are cause enough to recommend this book for anyone who is curious about graphics and journalism, especially journalism students.
What doesn’t this book do well?
The most curious shortcoming – given the incredible diversity of designers, styles, countries, and publication types represented – is the scarcity of women designers. There are thirteen designers profiled in part IV of the book; only two are women. There were forty-seven graphics reprinted; five were designed by women. With respect to the reprints, Cairo is completely justified in reprinting his own work more often than the work of others because he knows how the design process unfolded in those cases. Since he is a man, this inflates the masculine contribution to the reprinted graphics category. Still, many of the graphics he worked on were collaborative efforts and his collaborators could have been women in a more ideal world. But mostly, they were men.
Because the information graphics world is relatively interdisciplinary and (so far as I know) has no specific professional organization whose membership includes a representative sample of practicing information graphics and data visualization professionals, it is hard to tell if the gendered pattern in Cairo’s book is due to some oversight on his part or the underlying gendered make-up of the industry or a combination of both. Even if the industry is dominated by men, it is important for people who write and edit textbooks to ensure that women are represented or they run the risk of sending the message that women may not be welcome or well-rewarded if they choose to pursue data visualization. That is unacceptable. The graphics world will lose out on half its talent pool and women might avoid careers that could have been satisfying and rewarding for them. Notably, the kinds of graphic design that require coding – like data visualization and interactive design – are better compensated than illustration and static design so it’s possible that women are being subtly nudged into the less well-compensated areas of graphic design along the line. It would have been nice if this textbook that is so diverse in so many other ways could have pushed the gender boundary and included more women.
The book also over-promises in the cognition section. The first chapter on cognition was too basic. The second and third chapters in this section had more that was directly applicable to design. All three chapters could have been condensed into one. It is certainly true that perception and cognition ought to be included and there were some useful applications derived from the three chapters, but there was too much review and too few clear applications of the basic principles of cognition and perception to graphic design.
Here are the pointers I did find useful, if you happen to want to buy the book and skip those chapters:
+ If you want viewers to estimate changes by visually comparing elements, you will have the best luck if those changes are depicted using elements of the smallest number of dimensions possible. For instance, viewers will have an easier time coming up with an accurate estimate of the difference in size between two lines (1D) than between two circles or squares (2D). It’s best to avoid 3D comparisons altogether. I would also add that regular objects like circles and squares are cognitively easier to think with than irregular objects like polygons other than squares.
+ The less frequently a color appears in nature, the more likely it is to draw the eye. Reserve the use of colors like red, pink, purple, orange, teal, and yellow for elements that are meant to draw attention.
+ Humans cannot focus on multiple elements at the same time. Design graphics that have one focal point or clear hierarchies of focal points. Do this by eliminating unnecessary use of bright color, chart junk like grid lines that aren’t absolutely necessary, and by establishing a logical information hierarchy in the page layout.
+ Landscapes have horizon lines. Humans are used to encountering the world this way. This is one reason why it is easier to make comparisons using bar graphs (where all the elements start from a common horizon line) rather than pie charts (where there is no shared horizon).
+ Eyes are good at detecting motion and they will focus attention on moving objects. Try not to ask viewers to read text and simultaneously watch a moving element in interactive graphics.
+ Human brains are good at picking out patterns. Often, fairly small changes to a graphic layout that strengthen the appearance of grouping or other types of patterns will add to the ability of the graphic to deliver an instant impression or overview of the message being communicated. For instance, changing the spacing of the bars in a bar graph so that every fourth bar has twice as much space after it as all the rest will make the graph appear to have groups of 4-bar units.
+ Interposition – placing one object in front of another so they overlap – is a good way to add depth. If objects never overlap, the opportunity for the illusion of depth is lost.
Overall, the book was well-written, included valuable insight into the process underlying the creation of strong, successful information graphics and visualizations, and would be a solid textbook for use in journalism departments. The representation of women designers was disappointingly low and the segment on cognition could be condensed or otherwise improved. Cairo is clearly a talented designer and teacher. This book meaningfully combines both of those strengths and is an important contribution to undergraduate and graduate education in the emerging sub-discipline of information visualization and design.
I am sending you out with one of the graphics I was most impressed by, in part because the graphic is good, but mostly because Cairo helped me to see why a rather average looking graphic is in fact rather brilliant. It is by Hannah Fairfield of the New York Times graphic desk and it shows that the driving behavior of Americans is sensitive to changes in the economy. During the 2005 recession when gas prices were high but the economy was struggling overall, Americans drove fewer miles. This pattern had only one historical precedent – the 1970s. The graphic depicts this by having a timeline that appears to walk backwards during those two periods in history, a broken pattern your pattern-loving mind is likely to fixate on once you realize this is not your average line graph. Smart.
There are two ideal types of infographics books. One ideal type is the how-to manual, a guide that explains which tools to use and what to do with them (for more on ideal types, see Max Weber). The other ideal type is the critical analysis of information graphics as a particular type of visual communications device that relies on a shared, though often tacit, set of encoding and decoding devices. The book reviews I proposed to write for Graphic Sociology include some of each kind of book, though they lean more towards the how-to manuals simply because more of that type have come out lately. As with all ideal types, none of the books will wholly how-to or wholly critical analysis.
I meant to review two of Edward Tufte’s books first so that we would start off with a good grounding in the analytical tools that would help us figure out which parts of the how-to manuals were likely to lead to graphics that do not commit various information visualization sins. However, I have spent the past six weeks at a field site (a graphic design studio nonetheless) and it rapidly became completely impractical to lug the two oversized, hard cover Tufte books around with me. I found Nathan Yau’s paperback “Visualize This” to be much more portable so it skipped to the head of the line and will be the first review in the series.
Visualize This is a how-to data visualization manual written by statistician Nathan Yau who is also the author of the popular data visualization blog flowingdata.com. The book does not repeat the blog’s greatest hits or otherwise revisit much familiar territory. Rather, this was Yau’s first attempt to offer his readers (and others) a process for building a toolkit for visualizing data. The field of data visualization is not centralized in any kind of way that I have been able to discern and Yau’s book is a great way to build fundamental skills in visualization that use tools spanning a range of fields.
The three primary tools that Yau introduces in the book are two programming languages – R and python – and the Adobe Illustrator design software. Both R and Python are free and supported by a bevy of programmers in the open source world. R is a programming package developed for statistics. Python has a much broader appeal. Both of them can produce data visualizations. Adobe Illustrator is neither free nor open source but it is worth the investment if you are planning to do just about any kind of graphic design whatsoever, including data visualizations. Yau mentions free alternatives, and there are some, but none have all of the features Illustrator has.
Much of the book starts readers off building the basic bones of a visualization in R or python, based on a comma-separated value data file that has already been compiled for us by Yau. He notes that getting the data structured properly often takes up more than half the time he spends on a graphic, but the book does not dwell much on the tedium of cleaning up messy data sources. Fine by me. One of the first examples in the book is a graphic built and explored in R, then tidied up and annotated in Illustrator using data from Nathan’s Hot Dog Eating contest.
This process is repeated throughout:
1. start visualizing data with programming;
2. try to find patterns with programming;
3. tidy up and annotate output from program in Illustrator.
The panel below shows you what R can do with just a few lines of code. Hopefully, it also becomes clear why it is necessary to take the output from R into Illustrator before making it public.
There are hints and tips sprinkled throughout the book covering everything from where to find the best datasets to how to convert them into something manageable to how to resize circles to get them to accurately represent scale changes. This last tip is one of my favorites. When we visualize data and use circles of varying sizes to represent the size of populations (or some other numerical value) what we are looking at is the area of the circle. When we want to represent a population that is twice as big as the size of some other population, we need to resize the circle so its area is twice as big, not its circumference.
More great tips:
1. First, love the data. Next, visualize the data.*
2. Always cite your data sources. Go ahead and give yourself some credit, too.
3. Label your axes and include a legend.
4. Annotate your graphics with a sentence or two to frame and/or bolster the narrative.
*Love the data means take an interest in the stories the data can tell, get comfortable with the relationships in the data, and clean up any goofs in the dataset.
Pastry graphics: Pie and donut charts
Yau’s advice about pie charts diverges from mine. I say: use them only when you have four or fewer wedges because human eyes really have trouble comparing the area of one wedge to another wedge, especially when they do not share a common axis. Yau acknowledges my stubborn avoidance of pie charts but advises a slightly different attitude:
Pie charts have developed a stigma for not being as accurate as bar charts or position-based visuals, so some think you should avoid them completely. It’s easier to judge length than it is to judge areas and angles. That doesn’t mean you have to completely avoid them though. You can use the pie chart without any problems just as long as you know its limitations. It’s simple. Keep your data organized, and don’t put too many wedges in one pie.
The Yau explains how to visualize the responses to a survey he distributed to his own readers at FlowingData to see what they’d say they were most interested in reading about. He showed the readers of the book a table with the blog readers’ responses which I’ve recreated below [Option A]. I think the data is easier to read in the table than in either the pie chart or the closely related donut chart [Option(s) B]. In life as in visualization, a steady stream of pies and donuts is fun but dumb. Use sparingly.
What needs work
The overarching problem I had with Visualize This is that it spent relatively little time generating different types of graphics using the same data. We saw a little bit of that above when Yau used both a pie chart and a donut chart to visualize the same survey responses, but since donut charts are just variations on pie charts, it was not the best example in the book. The best example came when Yau visualized the age structure of the American population from 1860 – 2005 (I updated the end date to 2010 since I had access to 2010 census data).
First, Yau shows readers how to make this lovely stacked area graph in Illustrator. That’s right. No R. No Python. Just Illustrator.
Then Yau admits that the stacked area chart has some general limitations:
One of the drawbacks to using stacked area charts is that they become hard to read and practically useless when you have a lot of categories and data points. The chart type worked for age breakdowns because there were only five categories. Start adding more, and the layers start to look like thin strips. Likewise, if you have one category that has relatively small counts, it can easily get dwarfed by the more prominent categories.
I tend to disagree that the stacked area chart ‘worked’ for displaying the age structure of the US population, but not because there were too many categories. I’ll get to why I don’t think the stacked area graph worked shortly, but first, let’s have a look at the same data represented in a line graph. This was Yau’s idea, and it was a good one. What we can see by looking at the data in a line graph rather than a stacked graph is the size ordering of these age slices. Yeah, I can kind of see that the 20-44 group was the biggest group in the stacked graph. But I had to think about it. In the line graph, I don’t wonder for a second which group was biggest. The 20-44 group is on top. The axes in line graphs just make more sense. I admit that the line graph is not an aesthetic marvel the way the area graph was. But, you know, you can figure out your own priorities. If you want pretty, go with the area graph and get smart about colors (with the wrong color scheme, any graphic can look awful. See also: what Excel generates automatically). If you want a graphic for thinking with, avoid stacked area graphs.
Coming back to what I think about visualizing the age structure of the American population. Call me old-fashioned, say that I adore my elders too much, I’ll just tell you we all stand on the backs of geniuses. I like the age pyramids for visualizing the age structure of a population. Here’s one I plucked from the Census website.
The pyramid has these advantages:
1. It shows gender differences. Males are on the left. Females are on the right.
2. This graphic does a better job of showing the structure of the population because the older people appear to balance on the younger people. This is useful because the older people actually do kind of balance on the younger people when it comes to things like Social Security. The structure of the population does not come through in the area graph or the line graph. Both of those show us that there are more old people now than there were before but displaying more is a less sophisticated visual message than showing us just how many older people and how much older and how these things have changed over time. See all those and’s in the previous sentence? Yeah. That’s how much better the pyramid is.
3. It is possible to see both the forest and the trees in this age pyramid. What do I mean? Well, the stacked area graph and the line graph had to lump rather large (and disproportionately sized) groups of ages together. In the age pyramid, the slices are even at every five years and if you happen to want to figure out just how the 20-24 year olds are changing over time, you can. But this granularity does not make it difficult to understand the overall structure of the pyramid.
To summarize my larger disappointment, I wish that Yau had gone through a number of examples of displaying the same data with different graphics in order to teach readers how to choose the best graphic. To his credit, he did visualize crime data with a bunch of different graphics, but I didn’t like any of the graphic types. I’m including the one I liked most, but it’s mostly for historical reasons. This type of weird fanned out pie wedges is called a Nightingale chart and was developed in part by Florence Nightingale way back when information graphics didn’t exist. He visualized this same crime data with Chernoff faces and with star graphics, neither of which were interpretable, in my opinion.
Unlike Chernoff faces, star charts, and Nightingale charts which I think are totally useless, heatmaps have promise as data visualizations. This is a good example of how I wished Yau would have started working hard to get the data to lash up better with the visualization. This is his final version of the heatmap of a whole bunch of different basketball game statistics with the players who were responsible for scoring, assisting, and rebounding (among many other things). I am a basketball fan. I went linsane last season. But I just do not get excited when I look at this heatmap because the visualization does not reveal any patterns. Ask yourself: would I rather have this information in a table? If the answer is yes, well, then you know there’s at least one other kind of representation besides this one that you would prefer if this is the data you are trying to display.
So what would I do? Well, I’d do a couple things. First, I would probably try restricting this heatmap to the top ten players or even to my favorite players. Throwing in 50 players and about 20 statistics per player without condensing anything means we are looking at 1000 data points. Ooof. So…if not cutting down the number of players, maybe put the scoring statistics in a different heatmap than all the other statistics (playtime, games played, rebounds, steals, blocks, turnovers, and so on). Maybe strip out the “attempts” and just leave the completed free throws, field goals, and three-pointers. I do not know if these things would have revealed patterns, I just know that the current graphic is still looking like a data soup to me.
Overall, this was a great how-to for data visualization and I want to end on an appropriately high note. One of the biggest wins in the book was Chapter 8 in which Yau walks us through the most meticulous and involved demo in the book. The payoff is big. He shows us how to use google maps and FIPS codes to make choropleths (these are large maps in which colors mated with numerical values fill in small, politically bounded units, usually counties but sometimes census tracts). He does not use ArcGIS which is one of the reigning mapping tools on the market. But ArcGIS is expensive. And Yau shows us how to generate maps without spending a dime. You will have to spend some time. If you are a cartography geek or you follow the unemployment rate, you’ve probably already seen this graphic because it was widely circulated, for good reason.
Graphic Sociology is one of a growing number of blogs that feature and critique information graphics and I’m glad to be part of this group. I’m glad that since there are so many of us, each one can specialize a bit. With this back-to-school season, Graphic Sociology is going to graduate and move into a more analytical, less repetitive direction with fewer reposts, more original content, and more macro-level analysis rather than micro-level critiques of particular graphics. If you love the old format, go ahead and look through past posts. Or better, browse through the list of links in my blogroll. There are plenty of other blogs, often updated more frequently than Graphic Sociology, where you can gaze upon graphics for hours and hours.
So what are the changes?
1) graphics will be tilt towards original work by me (or others – nominate your own work!) with fewer reposts of graphics found around the interwebs,
2) reposts will show modifications rather than just tell about opportunities for modifications and describe how and why graphics come to be as they are,
3) I will tweet and pin graphics I like (@digital_flaneusat pinterest) for those who enjoy having a stream of graphics wash over their visual cortex,
4) each month I will review an information graphics how-to or theory-based book (see below for the initial list of books),
5) new textbooks and related online content in the social sciences will occasionally be reviewed with an eye towards assessing their information graphics content.
Every one of these changes is a change that will require more time and commitment on my part. Because I haven’t suddenly found more hours in the day, this means there will be fewer posts on the blog, but the posts that appear will be deeper, more engaging and thought-provoking. My twitter and pinterest infographics board will serve to stream interesting graphics for those who want more volume.
Which books will be reviewed?
These are all books that offer thoughtful perspectives on how to create or how to understand information graphics.
Graphic Sociology will focus more intently on the intersection of information graphic design and social sciences. It will have more original graphical content. It will also develop an ambition to become a resource for teachers looking to choose textbooks with high quality information graphics and for social scientists who want to be able to quickly understand which books are worth buying if they want to get into creating information graphics in their own research.
Follow me on twitter and/or pinterest if you want to see a volume of infographics. I have found I can share many more graphics I like that way.
If there are other changes folks would like to see or changes that already rub the wrong way, please leave me a comment. That’s the beauty of web 2.0. Readers can talk back.
Click on the image above or here to go to the actual graphic. What you see above is just a screen grab. If you like the screen grab, you will love the active graphic in which you can see what it would look like the visualize the wind blowing across the US right now. Yes, whenever you are reading this, you can download recent data to populate the graphic.
This is a great use of a map to display information. Think, for instance, what the same data would look like in a table.
State Speed Direction
Bismarck, SD 16 mph S
Columbus, OH 5 mph W
Fargo, ND 8 mph N
Minneapolis, MN 2 mph SW
New York, NY 6 mph E
In a table, cities would probably be arranged alphabetically which is fine if you want to know exactly what is happening in a given city but terrible if you are trying to discern if there’s any geographical pattern to wind flows. Looking at the map, it is easy to detect geographical patterns. In fact, it would be nearly impossible to avoid detecting geographical patterns. Huge win for the map as a graphic with respect to wind data.
The fact that the wind appears to blow is a programming achievement.
The creators of the graphic at hint.fm offer this disclaimer, “We’ve done our best to make this as accurate as possible, but can’t make any guarantees about the correctness of the data or our software. Please do not use the map or its data to fly a plane, sail a boat, or fight wildfires”. That being said, I think the graphic could be useful for those sorts of purposes. I also think it could be used to perform site selection for windfarms or at least as an educational tool to explain to people why the Dakotas make excellent states to harvest wind while the neighboring state Minnesota is a poor choice.
What needs work
I wish there were an easier way to find graphics like this. I stumbled upon this one via Albert Cairo’s twitter feed, but there must be other awesome graphic work out there just waiting to be discovered.
On that note, if you happen to enjoy stumbling upon information graphics, I highly recommend visiting visualizing.org and visual.ly, two websites that aggregate information graphics by allowing people to upload their own work. Both sites have relatively high collective standards for design and are trying to maintain the same high standards for data quality.
Then there’s Nathan Yau’s blog, flowingdata.com, which has long been on my list of must-reads. I assume many of my readers know about flowingdata but it is worth mentioning because it’s a great blog.
For a more strictly aesthetic experience, behance.net is a giant collection of graphic artists’ portfolios. Looking through it is the digital equivalent of walking around in a flea market – great stuff, unique stuff, and lots that is instantly forgotten even though its presence adds to the atmosphere. Most graphic artists are not information graphic designers so much of what is on behance is not information oriented.
I sometimes find things on pinterest, too, which is more like the digital equivalent of a mash-up between jcrew and a flea market. Oddities organized. It’s much harder to find good information graphics there because, for reasons I do not understand, pinterest is dominated by the long vertical graphics that require lots of scrolling. I’m not a huge fan of those. They encourage laziness – nothing needs to be integrated when you have an infinite length of scroll to just layer unaffiliated fact upon unaffiliated fact and hope that with a picture or two thrown in, a narrative will emerge.
Besides newspapers and magazines, where else do you find information graphics?
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.
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.
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.
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.
Giddens, Anthony; Duneier, Mitch; Appelbaum, Richard; and Carr, Deborah. (16 August 2011) Keyworks in Sociology. [information graphic] New York: W. W. Norton.
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.
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.
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.
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.
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.)
Analyzing the visual presentation of social data. Each post, Laura Norén takes a chart, table, interactive graphic or other display of sociologically relevant data and evaluates the success of the graphic. Read more…