Tag Archives: charts

Visualizing email traffic

Editing process in graphic design

The editing process in graphic design is somewhat different than the editing process in writing. Writers tend to start with a skeleton, make sure the bones are all in the right places, and then slowly add and sculpt musculature and skin through iterative processes. Graphic designers start with a whole bunch of skeletons, subtract a few, add musculature to the rest, subtract a few of those, add skin to the remaining ones, and then only late in the process will a single design go through a final polishing process.

One of the ways social scientists teach students to become skeptical about the things they read is by teaching them how to edit their own work and the work of others. Students start to see how pieces of written work represent a series of choices. They see that what they’ve read could have gone in other conceptual directions, used different evidence, been shortened, lengthened, stripped of jargon, or otherwise constructed and styled in new ways that could have changed the meanings taken away by the readers. Learning to construct, critique, and polish writing is a major part of how readers develop the tools they need to understand and analyze the works they read.

There is far less educational time spent teaching students how to create visual work, especially visual work outside of the realm of personal expression (I feel like most arts programs emphasize personal expression which is different than creating visual work with the intent of displaying data or even political messaging). It is not surprising that we end up with a bunch of people who struggle to apply an analytic lens to information graphics. This leads to a communications power imbalance that privileges certain kinds of visual devices, including information graphics, over writing inasmuch as information graphics are more likely to be accepted without too much scrutiny since most folks do not have a good idea where to begin to scrutinize them. Information graphics combine the moral authority of numbers with the cognitive inertia of sight that lies behind the cliche that ‘seeing is believing’.

In the service of pulling back the curtain on graphic design, I thought it might be useful to save an entire series of drafts in the development process of a graphic that describes the email traffic in a small design work group. The purpose is to break the seal around the image and reveal it is a series of decisions that might easily have been otherwise.

First Draft

First, I thought a stem and leaf diagram might work.

Stem and Leaf diagrams of office email traffic

Stem and Leaf diagrams of office email traffic

But these graphics failed because there was no way to keep strings of receiving or sending visually united. If the people in the office happened to be sending (or receiving) a series of email that spanned between one ten-minute period and the next ten-minute period, that run would be visually broken. I also wasn’t thrilled with the way the sent email matched up with the received email. It was hard to see that when one person in the office sent an email, it would often land in the inbox of someone else in the office.

Still, I liked the version where I turned the numbers into balls and that idea came back in a different form later in the development process.

Second Draft

I decided to abandon the stem and leaf for a timeline. I initially imagined triangles as markers for the email because I thought the shape would indicate the directionality of an email going out into the internet.

email traffic timeline, version 1

This version has an entire day on one page, morning sits above afternoon.

And I tried some different color schemes.

Email traffic timeline, version 1.1

Email traffic timeline, version 1.1 stretching the day across two pages.

Email traffic timeline, version 1.2

Email traffic timeline, version 1.2

The triangles did not work and some of the color schemes created a sense of vibration. A trained graphic designer might have tried the triangles (and rejected them, of course), but they would not have made the mistakes with color that I did.

Third draft

I replotted the graphic with circles, not triangles, and added up all the emails that were received in 5-minute periods instead of plotting each individually. This lost a bit of granularity, but it made it easier to see where traffic was greatest because it allowed the height of the circles start to draw the eye.

Email timeline, version 1.3

Email timeline, version 1.3
There is another page to the right of this one but viewing the image at this scale displays more detail.

This version is much closer to the final but something was missing.

Fourth draft

I started to realize that the timelines were difficult to analyze so I went back to the data and pulled out some summary statistics about the average number of emails each person sent and received. I also thought it would be interesting to see how much of the officewide traffic each person generated. While I was looking for new ways to help people understand what they were looking at, I also showed them the range of reality in the same timeline format by pulling out the lines for the highest traffic person-day and the lowest traffic person-day. I also remembered one of the lessons I learned from reading Nathan Yau’s Visualize This and added some descriptive text. [A full review of that book is here.]

Office email traffic

Office email traffic

This is as far as I have gotten. But if I get good suggestions in the comments, I’ll keep improving.

What can writers learn from graphic designers

Getting through this many drafts alone was hard. It is very hard to see the same thing with new eyes. I got some help from two different people and even though neither of them said much, their opinions made a huge difference in the process. I encourage writers to find a way to share their work with others earlier in the process. It is humbling. If the comparison to graphic design is apt, earlier sharing either of the whole draft or of smaller sections will also likely lead to a stronger piece that gets written faster.

What a man wants, then (1939) and now (2008)

Update on references

As you can see in the comments, Christie Boxer, the lead author of the journal article behind the Coontz Opinionator piece has contacted me to let us all know that the article is currently in revise and resubmit phase but will be published in Journal of Family Issues shortly.

What works

The graphic is more legible than the chart from which the data originated. I’m guessing the Journal of Family Issues would not allow such a “fancy” series of graphics in the final published piece so I don’t mean this as a critique of the article’s authors. Just pointing out that there is good reason for journals and other publishers to reconsider their policies about how data can most usefully be presented.
I happen to have created a few graphics in this style myself and tend to favor it over the chart (e.g. this one about agricultural subsidies) in the past and think they work well for displaying changes in attitudes over time.

What needs work

Illustrations to Accompany "The M.R.S. and the PhD" by Stephanie Coontz, New York Times

Illustrations to Accompany "The M.R.S. and the PhD" by Stephanie Coontz, New York Times

The article from which this news story is drawn clearly provides information on both what women want and what men want in greater detail than what’s seen here. Why did the news story choose to run with less than half the data?
The chart clearly contains information on what men want in a mate AS WELL AS what women want in a mate. I see no reason for going (less than) halfway on this story. In fact, what I find most interesting is the convergence on some things – nobody cares much about chastity in a mate any more – and divergence on other traits – women rank men’s desire for home and children much higher than men rates women’s desire for home and children. That’s a puzzler worthy of thought in a way that a story that reflects only what men want is…well…just not all that interesting. Pair bonding takes two, as I’m sure Coontz knows because she’s been researching marriage for years. It’s unclear if the Times pressured her to come up with a more attention grabbing headline “The M.R.S. and the PhD” or if she chose that on her own or if it was a combination of factors.

I’m glad to see that, at least as far as I can tell from what is available to scholars other than Coontz (who might have an early full-length, unreleased draft of the Boxer, Noonan, Whelan paper), the scholars whose data led to the graphic were not so singly concerned with what men want in a mate. They were looking at how mate selection characteristics have been adjusted over time for both men and women and I hope that their article looks at the consonance and dissonance between the two genders’ mate selection ideals.

I would have preferred more attention paid to the graphic – like, say, the inclusion of what women want or an integrated graphic that displayed the overlaps and distances between what men and women want – and less time put into the accompanying illustrations which I have included to the left. I welcome regular readers of Sociological Images (and others) to comment on the messages coming out of the illustrations.

References

Coontz, Stephanie. (2012) “The M. R. S. and the PhD”. The New York Times, Sunday Review, Opinionator. [Information graphic by Bill Marsh/The New York Times]

Boxer, Christie; Noonan, Mary; and Whelan, Christine. (forthcoming) “Measuring Mate Preferences: A Replication and Extension” Journal of Family Issues. [Table drawn from Christine Whelan's research webpage]

Time and Newsweek Circulation Figures

Time and Newsweek Circulation Figures | Graphic by Laura Norén

Time and Newsweek Circulation Figures | Graphic by Laura Norén

Newsweek and Time Circulation Figures | Graphic by Yolanda Cuomo

Newsweek and Time Circulation Figures | Graphic by Yolanda Cuomo

Which one works?

These two graphics portray some of the same information – household income, median age, audience and circulation – though the first one does not break down information between genders. Though it probably goes without saying, I like the one I designed best. The second one has some tantalizing shapes – I applaud the visual appeal – but it does nothing to aid people’s eyes as they try to compare relative sizes between the salient categories. I also happen to think it is easier to understand the complexity of the difference between audience and circulation with the textual explanation provided in the first one. I find the white-font-on-dark-background of the Time and Newsweek labels hard to read (it’s also a known graphic design no-no, especially with a small font size like this. It is easier for the human eye to grok the contrast with dark text on a light background than with light text on a dark background).

From a sociological perspective, comparing the readership of Time and Newsweek not only to each other but also to national averages provides a much deeper sense of context. The second graphic was built from the first though I never had a chance to meet with any of the writing or design team to understand why the national averages were removed.

There are other elements I dislike in the second one. I dislike, for instance, the need to repeat certain elements of text over and over again: “readers per copy” and “Total adult population” and even the “Time” and “Newsweek” headings. One of my closest friends and colleagues spends a lot of his time writing code. The best lesson I have learned from him is that where elements or actions have to be repeated over and over, there is inefficiency in the system. A better design is possible.

I would love to hear from my readers on this comparison. Am I suffering from too much ego investment in the graphic I made? Is the second graphic an improvement on the first? If so, how?

References

Norén, Laura. (2010) “Appendix: Data and Methods” in first draft of Dill, Nandi and Telesca, Jen Imagining Emergencies. [Information graphic].

Cuomo, Yolanda. (2011) “Readership Data Time and Newsweek 2008″ in final draft of Dill, Nandi and Telesca, Jen Imagining Emergencies. [Information graphic].

US agricultural commodity subsidies by state, 2010 | New graphic

Beans

Beans

Overview

On Tuesday I read “When One Farm Subsidy Ends, Another May Rise to Replace it” OR “Farmers Facing Loss of Subsidy May Get New One” by William Neuman [aside: why does the NY Times frequently have two titles for the same article? One appears in the title tags in the html and in the URL, the other appears at the top of the article as it is read]. The upshot of the article is that the subsidies appear to be curtailed as cost-saving measures but come right back under new names:

It seems a rare act of civic sacrifice: in the name of deficit reduction, lawmakers from both parties are calling for the end of a longstanding agricultural subsidy that puts about $5 billion a year in the pockets of their farmer constituents. Even major farm groups are accepting the move, saying that with farmers poised to reap bumper profits, they must do their part.

But in the same breath, the lawmakers and their farm lobby allies are seeking to send most of that money — under a new name — straight back to the same farmers, with most of the benefits going to large farms that grow commodity crops like corn, soybeans, wheat and cotton. In essence, lawmakers would replace one subsidy with a new one.

Neuman also interviewed Vincent H. Smith, a professor of farm economics at Montana State University who, “called the maneuver a bait and switch” saying,

“There’s a persistent story that farming is on the edge of catastrophe in America and that’s why they need safety nets that other people don’t get. And the reality is that it’s really a very healthy industry.”

My curiousity was piqued, to say the least. Farm subsidies have long been an emotionally charged issue – Professor Smith is right to point out that the family farmer is an icon in the American zeitgeist whose ideal type gets trotted out as a narrative to support subsidies that often go to large-scale corporate agriculture. Before mounting my own angry response to what appears to be both hypocritical and a well-orchestrated marketing schmooze (ie the public proclamation by various farm lobbies that they are willing to take fewer subsidies as they band with the rest of the beleaguered American public in a collective belt-tightening process while simultaneously opening up other routes to receive the same amount of funding through different mechanisms), I decided to go in search of some hard data to see what is going on with agricultural subsidies.

Agricultural data

I found two great sources of data. First, the USDA runs the National Agricultural Statistics Service which publishes copious amounts of tables full of information about how much farmland there is in the US, what is grown on it, what the yields are, what commodity prices are, what farm expenditures are doing, and all sorts of rich information. Linked from the article was another source of data – the Environmental Working Group – which has been tracking farm subsidies for years. The Environmental Working Group also relies on the National Agricultural Statistics Service, especially for farm subsidy information. Between those two sources, the US Census, and the 2012 US Statistical Abstracts (Table 825 especially), I had more than enough information to start putting together a graphic that could describe at least part of what is going on with agricultural subsidies.

Selecting the right data

Because farming is distributed unevenly around the country, I knew I needed to come up with a set of numbers that went beyond absolute dollar amounts per state. Probably it would have been nice to see where subsidies go per crop, but other people have already done that.

To look at agricultural subsidies overall, and to work with the state-by-state data that I had, I ended up considering three approaches.

  • 1. Absolute commodity subsidy amounts per state.
  • 2. Commodity subsidy amounts per capita.
  • 3. Commodity subsidy amounts per farmland acre.

It is obvious that the third option, looking at the amount of spending per acre within each state, is the best.

Hypothesis

I expected to find that states with small amounts of farmland would be relatively more expensive per acre than states with large amounts of farmland. I assumed there would be economies of scale and that states with very large amounts of farmland probably had a lot of that land dedicated to pasture, which is pretty cheap to maintain compared to something like an orchard.

Attempt Number 1

I decided that simply showing the costs per acre might not be as interesting as keeping the absolute amount of farmland in play and doing some kind of comparison.

Rank comparisons are extremely popular and I admit I was sucked into them, though now that I’ve tried to make them, I kind of hate them. These are the kinds of comparisons that you’ll hear on the news – Ohio ranks Yth in per capita income but Zth in educational spending per pupil – and see in graphics that often look like this:

My first attempt to do something similar looked like this.

US Agricultural Commodity Subsidies | Process Graphic 01

US Agricultural Commodity Subsidies | Process Graphic 01

Here are my problems with it:

  • There is no obvious pattern – it looks like a rat’s nest.
  • The states with bad ratios – the ones where we are paying more than $10/acre – have upward sloping lines connecting them from the left column to the right column. Psychologically, the ‘bad’ deals should have downward sloping lines. It just makes better visual sense.
  • Pink was supposed to be along the lines of red on accounting sheets but it looked too cheery to indicate being ‘in the red’.

Attempt Number 2

US Agricultural Commodity Subsidies - Process Graphic 2

US Agricultural Commodity Subsidies - Process Graphic 2

I got rid of the pink altogether and flipped the scale on the left so that the best deals – the lowest per acre subsidy costs – are at the top. This means that states that are taking less per acre end up having upward sloping lines more often than downward sloping lines.

Thinking through this brought up some larger concerns. Comparing by rank alone is ridiculous. The space between each listing in both columns is extremely critical in a graphic like this and needs to be scaled appropriately. For instance, look at Alabama ($6.06) and Oklahoma ($6.07) in the right hand column. They basically have the exact same amount of spending per acre and yet they are the same distance apart as Washington ($9.86) and Minnesota ($11.37). The same problem happens in the lefthand column – states with about the same amount of acreage dedicated to farmland have the same distance between them as states with large differences in the amount of acreage they have dedicated to farmland.

Attempt 3

US Agricultural Commodities by State, 2010

US Agricultural Commodities by State, 2010

Click here to see a pdf of the whole graphic.

I scaled both the right and left hand columns using a log scale for farmland acreage (though the number of acres is still given in absolute millions of acres – only the visual arrangement was logged). The pattern is still messy and hard to discern, though clearer than in previous versions. In order to bolster the pattern, I turned the ‘good deals’ in the lefthand column pink. The states with less acreage dedicated to farmland routinely receive less subsidy per acre than some of the bigger states. But the very biggest farming states – like Montana and Texas – are also pretty affordable on a per acre basis. It was states near the middle of the pack that were coming in at $18 and $19 per acre of commodity subsidy spending.

I thought maybe it was a weather event that led to some of the larger subsidies. But if that were the case, states that were geographically near one another would probably have had the same drought/hurricane/flood and should have received similar funding. There is work to be done on the weather question – looking at data over time would be a good step in the right direction there.

However, I don’t know that weather is going to be the best answer to this question. Look at Washington and Oregon. They are geographically right next to each other, grow some similar kinds of things, and have a similar amount of farmland acreage yet they have dramatically different amounts of subsidy spending per acre. Washington takes $9.86 per acre; Oregon gets $2.51 per acre. It’s still unclear why there is such a great disparity between these two states in 2010.

Falsified hypothesis

Through the construction of this information graphic, I falsified my own hypothesis. The states with the smallest amount of land dedicated to farmland received the least amount of commodity subsidies.

I have some thoughts about what is going on. They will require more data analysis and graphic development to suss out and represent completely.

    New Hypotheses

  • 1. It’s the weather. It could still be the weather. I did not do enough investigation into this variable, though this seems like a weak hypothesis.
  • 2. It’s corn. The states that grow a lot of corn seem to get more subsidies. This hypothesis could easily be expanded to be something more sophisticated such as: “Subsidies per acre are sensitive to the commodity grown.”
  • 3. It’s lobbying. The states that are known to be “big farm” states seem to have more funding than smaller farm states. Maybe they are better represented by the farm lobbies and therefore end up with more subsidy per acre than states without strong representation from the farm lobby. This hypothesis has an overlap with the “it’s corn” hypothesis.

Conclusion

There are two kinds of conclusions to be drawn. On the agricultural front, it is safe to conclude that Americans spend a good bit of money per acre of farmland; there is no free market on the farms. Bigger states do not offer economies of scale compared to states with less farmland acreage. No additional conclusions can be drawn from this limited data, though interesting hypotheses can be posed about the influence of local weather events, funding for specific commodities like corn, and the impact of lobbyists efforts on agricultural funding allocations.

As a graphic exercise, I hope I have proven that rank orderings do not offer much analytical value on their own. I hope I have also suggested that graphics can be used not only for representing findings at the end of the process but for discovering patterns. Graphics are not just for display, they are also for discovery.

References

Neuman, William. (2011, 17 October) “When One Farm Subsidy Ends, Another May Rise to Replace it” Business Section, nytimes.com.

Noren, Laura. (2011) US Agricultural Commodity Subsidies by State, 2010“US Agricultural Commodity Subsidies by State, 2010″ [Information graphic] and [Data Table - this is a combination of data and analysis originally published by the National Agricultural Statistics Service, a public-facing branch of the USDA].

Environmental Working Group a good source for information on agricultural subsidy spending.

United States Census Bureau, Statistical Abstract of the United States (2011) Agriculture.

United States Department of Agriculture, National Agriculture Statistics Service

Disease Mortality Rates

What works

This graph is clean and balanced. Generally, it’s not good to abandon axes, and I don’t know if a social scientist could have gotten away with stripping them, but in this case, all the viewer needs to see is included next to each circle. The name of the disease is probably the most important element and that is easier to see right next to the dot than in some sort of axial label elsewhere. I’d also rather have the percentage values spelled out than have the circles simply plotted on a percentage continuum that I then have to mentally map. So it’s working for me. I’d also point out that if the axis had been a percentage continuum the flow of the cascade would not have been so elegant. Swine flu, malaria, and seasonal flu would have been more or less on top of each other. The way they’re presented here we can actually understand them a bit better than if they had been strictly plotted. The overall review is that relying on the principles of graphic design rather than relying on the principles of graph paper serves this particular data quite well.

What needs work

These colors strike me as being a bit too cheery for such a serious topic as disease fatality. While I am not the kind of graphics person who goes on and on about color theory, I do think that after sufficient cultural training (ie living in Western culture for a sufficient period of time or growing up here) will lead to a subconscious attachment of sentiment to particular colors. AIDS (untreated) and AIDS (treated) get the color tones more or less correct since orange and red are often correlated with anger, danger or aggression. AIDS is certainly an aggressive disease – any illness is experienced as an assault, in my opinion. Tuberculosis is also an angry, aggressive disease with a high fatality rate and yet it’s green. Green is related with invigoration, health, and the positive features of the natural order (that’s why all those bath products are green. Marketers like color theory.). Purple and magenta, especially when they are right next to each other as they are here, kind of look like carefree fun, the kind of fun associated with bubble gum, bike streamers, and pop rocks. So red and orange are working for me but green is a misstep. The colors for the rest could have been either shades of red or shades of gray and black. Yes, the graphic overall would have been more depressing, but that is the point.

Some information isn’t inherently beautiful and dressing it up as beautiful information may dilute the message. I think yesterday’s CO2 graphic was ‘beautiful information’ and yet it was only red, black and white.

References

McCandless, David. (September 2009) Fatal Infection at Information is Beautiful

For more on fatality rates
World Health Organization, CDC.

For more on circular graphics, see this newsletter [pdf]. I still dislike pie charts.
Few, Stephen. Our Fascination with all things Circular Perceptual Edge.

Online dating sites – Graphing the field

An Overview of the Online Dating Field by Zosia Bielski and Tonia Cowan

An Overview of the Online Dating Field by Zosia Bielski and Tonia Cowan

What Works

Imagine this data as a bar graph that illustrates how many users each site has. Maybe there is even some sort of inception date from the site included, too. That would be a typical way to represent this sort of data because all the reporters had in terms of numbers, were user totals. But they weren’t interested in simply showing how big one site was with respect to another. They were interested in discussing hook-up culture. Now, so far as I know, there is no agreed upon quantitative measure of ‘hooking up’. These folks didn’t claim to invent one (which is nice). They just used a couple different qualitative axes to illustrate the distinctions they saw within the field of online dating when it comes to marriage vs. hooking up and raunchiness vs. wholesomeness.

I think Bourdieu would have recognized some of his own influence here. He had similar Cartesian field maps in Distinction. Granted, he may not have been thrilled to have his concept used to describe online dating – ‘raunchy’ is a word that may not have been part of his vocabulary. On the other hand, his axis of choice probably would have been class (high and low) and as far as I can tell, the desire for lasting vs. fleeting sex does not show a clear relationship to class. Feel free to debate that assertion in the comments.

What Needs Work

Not a fan of the colors. I also wonder how certain smaller sites made the list – seems a bit arbitrary considering how many sites were left off the list.

References

Bielski, Zosia. (2009, April 9) “One Click Stands” in The Globe and Mail. [Tonia Cowan also contributed to the production of the graphic.]

Bourdieu, Pierre. [tras. Richard Nice] (1987) Distinction: A Social Critique of the Judgement of Taste. Cambridge, MA: Harvard University Press.

Reinventing the Automobile | Infographics

Reinventing the Automobile* is a book that lays out a vision for a progressive evolution of urban mobility transition that offers a robust point-to-point on-demand mobility network of 2-passenger fully electric vehicles. These vehicles would take up less parking space because not only are they small, but one proposed design folds up when parked. And they’d be able to tell you where the nearest parking spot is as you’re approaching your destination. Being fully electric they require a plug….or do they? The authors suggest that after an initial period of individual owners plugging these babies into outlets in their garages overnight, folks in city planning departments or franchise owners would trust the technology and economics enough to start installing wireless charging devices available curbside or in the road bed itself. Stuck in a bottleneck at a bridge or tunnel entrance? At least charging pads in the roadway can ensure that your 2-seater won’t run out of juice before you get where you’re trying to go. You can sit there and it will charge itself with embedded charging device in the road surface while plodding through gridlock. Even farther down the timeline, the cars might be able to drive themselves. So you can sleep through the gridlock or make calls or surf the ‘net. Just don’t post facebook status updates about your traffic problems. Nobody cares.

What I like most about the book as an object of intellectual design is that even if readers decide to skip all the words and they only look at the images, charts, maps, and diagrams, they won’t miss much. This book is stuffed with great graphics. I haven’t included them all as that would constitute copyright infringement and be too long for a single post. What you see below is just a small sample from Chapter 9: Personal Mobility in an Urbanizing World.

Daily driving in Paris

Daily Trips in Paris - Reinventing the Automobile (Mitchell, Boroni-Bird, and Burns), Figure 9.6

Daily Trips in Paris - Reinventing the Automobile (Mitchell, Boroni-Bird, and Burns), Figure 9.6

What works

This graphic is both elegant and deep. (Or it would be elegant if I had a better scanner.) It’s a simple form – Paris as concentric circles – but the more you look at it the more you learn. Rewarding that way. What sometimes happens in elegant graphics is that the details become obscured in iconography or approximations. But this graphic includes percentages as well as absolute numbers of two different kinds of trips – public transit and trips by cars. We see that Central Paris is defined as Arrondissements 1-20, the first ring is Seine Saint-Denis, Val-de-Marne, and Hauts-de-Seine, and the second ring is the rest of the Île-de-France region. There’s a summary of all the trips over in the legend so that the graphic itself can just show you the break down of different kinds of trips.

What needs work

In terms of transit, things like rivers often represent real barriers. There are only so many bridges and tunnels which creates a bottleneck effect. Paris is a city on a river so the one thing the elegance of this graphic obscures is the impact of the natural geography on transit choices. Maybe it’s not important when it comes to the cars vs. transit question, but bottlenecks are critical factors when it comes to planning mobility and I’m curious about whether bottlenecks push more people to transit or cars. In Boston/Cambridge, MA only one bridge has a train running across it and I have always assumed that pushed more people into their cars because many of them would have to go out of their way if they took the train and could only go over that one bridge.

Parking in Albuquerque

Parking in Albuquerque - Reinventing the Automobile (Mitchell, Boroni-Bird, and Burns), Figure 9.13

Parking in Albuquerque - Reinventing the Automobile (Mitchell, Boroni-Bird, and Burns), Figure 9.13

What Works

What you are seeing here is a simplified map of downtown Albuquerque, New Mexico. The white areas are buildings. The teal areas are parking – darker teal represents multi-story parking structures while the lighter teal shows us where surface lots can be found. Lovely way to show this information. One could imagine the same sort of information as a percentage-of-land-use pie chart or some far less granular collection of numbers. This schematic doesn’t bother to calculate just how many square feet of land are dedicated to parking. Nope. This is the visual equivalent of the ‘show don’t tell’ rule that writing professors are always encouraging their students to adopt when constructing essays. A table with land use percentages would be telling. This graphic is showing.

Albuquerque is like a parking lot with some buildings in it.

What needs work

I have never been to Albuquerque but I’m guessing that if you lived in Albuquerque you might like to see some sort of orienting label. Even just a single recognizable street name thrown in their somewhere to help orient. Now, the point of Reinventing the Automobile is not to provide urban planning for Albuquerque so I know they aren’t all that concerned with just precisely which neighborhood in Albuquerque this schematic represents. Still. It’s almost too cleaned up to read as a city plan right away.

Vehicle-to-Vehicle Crashes

Vehicle-to-Vehicle Crashes - Reinventing the Automobile (Mitchell, Boroni-Bird, and Burns), Figure 9.16

Vehicle-to-Vehicle Crashes - Reinventing the Automobile (Mitchell, Boroni-Bird, and Burns), Figure 9.16

What works

This graph does a great job of providing us with granular data and indicating a couple different trends visual. Keep in mind that they have multiple layers collapsed into a single graphic. It looks easy once it’s done but when one is faced with a pile of related numbers along multi-dimensions it isn’t always clear how to relate them to one another visually.

This graph has three levels of accident severity – minor, serious, fatal. It also shows the probability of injury. It also factors in variation in speed (which it does by creating five speed ranges). And then there’s the belted vs. unbelted division. That is a total of four different dimensions all displayed on one graph with a single measure on the y-axis. Color is used well. Grid lines are all that separates minor from serious from fatal accidents which are more or less three different graphs lined up next to one another.

References

Mitchell, William; Boroni-Bird, Christopher; and Burns, Lawrence. (2010) Reinventing the Automobile: Personal Urban Mobility for the 21st Century Cambridge, MA: MIT Press.

* The book specifically credits Ryan Chin, Chih-Chao Chuang, William Lark, Jr., Dimitris Papanikolaou, and Ruifeng Tian with “Illustration Production”.

Illustration by Christopher Niemann – reflections on a pie chart

by Christopher Niemann

by Christopher Niemann

What works

I never thought of a fuzzy halo-like hairstyle as an exploded pie chart before. Mostly, I just saw this and it made me smile. Recall from my earlier post Translating inspiration into better design that seeing something which you believe to be beautiful or clever can be a springboard for improving your own ability to design elegant information graphics.

What needs work

Clearly, it would be great if this had anything at all to do with social science or research. But it doesn’t. And I’m sure some of my readers are going to be upset that it had anything to do with Mr. Gladwell, but it is unfair to let personal opinions about Malcolm alter the reception of Christopher Niemann’s graphic.

References

Pinker, Stephen. (2009, 15 November) “Malcolm Gladwell, Eclectic Detective” [book review for "What the dog saw"] New York Times, Sunday Book Review.

Niemann, Christopher. (2009, 15 November) Illustration for Stephen Pinker’s review of Malcolm Gladwell’s book “What the Dog Saw”

Hey Jude the flowchart

Flowchart of Beatles song 'Hey Jude' created by dannygarcia inspired by jeannr

Flowchart of Beatles song 'Hey Jude' created by dannygarcia inspired by jeannr

What Works

I love it when I find evidence that someone has taken something not at all visual or even all that hierarchical and turned it into an information graphic. It can be difficult to convince people (and here I mostly mean academic sociologists) that developing information graphics is a critical part of communicating research findings or teaching concepts. Coming across examples like this helps – then again, it’s pretty easy to dismiss this as a silly exercise unrelated to the important work sociologists are doing.

I love the loop on ‘na’ at the end.

Good use of gray scale, too.

What needs work

I am now curious about developing a way to understand how to choose a path. When should Jude ‘make it better’ vs. ‘let her into your heart’?

References

dannygarcia at the blog Danny Garcia.