This is beautiful. Just look at it and tell yourself why it works. Think about how crappy it would have been if all the cities had been crammed on to one graph. Stringing them out like this, one city per graph, tells the story of immigrants moving to the suburbs so elegantly. The density increases from left to right with time series adequately represented for each city.
These graphics accompanied a great article about water shortages in episode of The Economist which arrived last week. The article was well written and comprehensive, handily summing up the way water resources are related to the growth of urban centers, climate change, the rising affluence of the world’s poorest people (and their conversion from vegetarianism to omnivorousness) and the question of whether or not fresh water is a global or a local problem. I highly recommend reading it. Unfortunately, I think you would do almost as well reading it without the accompanying graphics as with them.
The first one is so confusing I still don’t know what I am seeing here. Table data usually has the attribute that the longer you look at it, the more you get, with an occasionally painfully long initialization period in which you can’t make out any pattern whatsoever. I spent a good bit of time on this one and I still don’t know how to make sense of it. The article rightly points out that fresh water is unevenly distributed across the globe–some places have a lot, some places hardly have any. No big surprise. Also not surprising: some continents use more fresh water than others based on overall population size and agricultural production practices. So when I looked at this graphic, I was kind of hoping to get a sense of both how efficient each continent was with their resources and how dire their straits were. The graphic sort of does that. Sort of. We’ve got a measure of total renewable water resources but it doesn’t take into account total land area. It does take into account population, sort of, and maybe population is more relevant than total land area in this case.
Ratio of Water Use to GDP
The second graphic does not stand well on it’s own. I can see here that it appears that these selected countries seem to have been becoming more efficient with their water use. Since 1995, all of these countries have lowered the number of cubic metres of water used per dollar (or dollar equivalent) of GDP. This graphic does nothing on its own to help me understand why that might be true. Have these countries moved out of water intensive agricultural production? Have they made their agricultural production more efficient? If so, is it technological change leading to increased efficiency or did they just shift to more efficient crops? Or maybe the change is in the GDP variable, not the water variable. The graphic really just doesn’t clear any of these things up.
What Works
Water Used to Grow the Same Crop in Different Countries
I like the third graphic. It’s clear and adds to the text in the article. This isn’t the first time I have read about water shortages and one of the biggest and possibly easiest changes we could make to prevent the water shortage from becoming any more of a problem than it already is, would be to introduce drip irrigation in places that do not already have it. Yes, it costs some money. But it is far more cost effective than many of the other strategies introduced to combat climate change. Drip irrigation technology is not overly complex nor does it require extensive training or equipment to install. Tubing perforated along its length with small holes, buried under the surface of the earth, delivers water directly to plant roots. Much less water is lost to evaporation or seepage into non-crop areas. Control over water resources is better – during rains cisterns collect and store water for later distribution through the drip tubing during dry periods.
Your sense of who’s single and when they’re single will grow immensely in three or four minutes of playing around with this interactive map of single-ness in the United States, by age and gender. Men get married later and die younger. This means that at young ages, there are more single men than single women because some men who will eventually get married won’t marry until later, on average, than the women they end up marrying. This is just a complicated way of saying that men often marry younger women. In old age, there are more single women than men (the imbalance is because the men start dying younger). During the decade of the twenties and then after about age 65 you’ll find the largest proportions of single-ness. People in the middle decades, from 30-60 or so, are more likely to be coupled. But don’t take my word for it, click through and play around. This data actually understates the number of people who are functionally single because single is measured here as never married. So the folks who have been divorced or widowed and haven’t remarried do not count as single for the purposes of this graphic.
The writer of the text accompanying the graphic is interested in the geographical distribution of single women and single men so there’s more on that if you click through.
What Needs Work
I like this one a whole lot so I don’t have much to say except that I wish the designer wouldn’t have gone with the red/blue, female/male color scheme. How about purple and green? Or orange and teal?
I also think I would have counted people who are divorced/widowed and NOT remarried as single.
The graphic designer is careful to note that since homosexual couples cannot get married, they will erroneously be counted as single, even if they are partnered. That’s a problem with the underlying data collected by the census, not the graphic design.
Visualizing a Billion Dollars - Those are palletsThat's a Trillion Dollars - See the guy still in the lower left corner?
What Works
This is a blatant repost of content – all these blogs are – but this one is particularly blatant. [For full effect, click through.] The folks over at pagetour.com went to the trouble to visualize just how much money a billion and a trillion dollars actually is. I have heard on NPR that it’s hard for people to make decisions about monetary volumes once the order of magnitude goes above 7 or 8, that humans unconsciously shift to logarithmic scale thinking which leaves 100 million dollars being only slightly less than 1 billion dollars. That’s like thinking that 100 dollars is only slightly less than 1000 dollars. Have a look. Think about it.
A simple overlay of graphs goes a long way to telling the story that as cigarette taxes increase, rates of smoking decrease. At the deli where I buy snacks, the cost of a pack is $12. Ouch. The reason raising taxes works so well in this situation is that it tends to prevent teens from starting to smoke in the first place because they are relatively poor and cannot afford to support an addiction causing habit. If they don’t start as teens, they are far less likely to start later in life when they might have more disposable income. Increasing tax rates works when it comes to causing a decline in smoking rates, but it might not work in causing other sorts of macro-behavioral changes, at least not over the long run.
Clearly, because we are looking at tax rates and prevalence rates, the two graphs could not share scales. I probably would have gone for either all line graphs or all bar graphs – I don’t like to mix it up for no good reason.
What Needs Work
Not sure I like the use of burning cigarettes as bars, or at least not these ones. Too cartoon-y for a serious subject.
Relevant Resources
Good Magazine (2008) Up in Smoke Interactive Graphic.
Books are still great, no matter what happens on these here internets. They make much better references because the information stays where it is and is accompanied by indices and tables of contents. No link rot, no head scratching while trying to navigate a tag cloud, no wondering if the review was posted by the publisher or an actual reader (ie not the author’s mom, either). They do cost more than free-media (aka e-media?), unless you can find them at your library.
Here’s a start to a list of books you might consider buying if you’re interested in graphic design. Most social scientists might like to be better informed about graphics, but have a bit of trouble getting into it. Below are books that are compelling and beautiful. They won’t tell you how to run the software you might use or write clean html/css or insert hacks for working around the stupidity of IE6. I can post some of those another day, if you like. Our contexts IT guru Jon will be invited to guest blog on the topic of how to do online publishing, as well.
Tufte is widely regarded as a key figure in graphic design, especially the visual display of quantitative information (which is the title of one of his books). He also has a brilliant essay on powerpoint, of which he is not a fan.
Envisioning Information - Edward TufteVisual Explanations - Edward TufteThe Visual Display of Quantitative Information - Edward TufteBeautiful Evidence - Edward Tufte
The Elements of Typographic Style by Robert Bringhurst
Typography is a critical component of graphic design. Font size is often the least flexible component and thus serves as a starting point for the rest of the design, assuming the design includes text. With respect to accessibility, small font sizes are terrible since many people have trouble with their eyes. (Even if only a few people had trouble with their eyes, designers should still make inclusive designs.) With respect to design, it is often better to have small fonts so that text blocks read as blocks rather than stripes. Reconciling those two and adhering to branding and identity strategies relies on a firm command of typography. Bringhurst’s book is recommended wide and far so I’m not saying anything new here.
Hoefler and Frere-Jones
Tobias Frere-Jones and his partner Jonathan Hoefler are current giants in the field of typography and have resources on their website – Hoefler & Frere-Jones. They have created typography for the White House, Gucci, the United Nations, Saatchi and Saatchi, Wieden + Kennedy, J. Walter Thompson, all the Martha Stewart magazines, Wired, The New York Times, The Washington Post, Apple, IBM, Sony, and on and on. They represent a sizable portion of the world of font development.
Thinking With Type - Ellen Lupton
Ellen Lupton of Maryland Institute College of Art says/writes much worth hearing/reading, including typography. Her Thinking With Type has it’s own website with additional information. Cheaper than buying the book, but I still recommend owning books if you can afford them.
3. The Grid
Grid Systems in Graphic Design - Josef Muller-Bronfmann
Grid Systems in Graphic Design (Josef Muller-Brockmann)
Muller-Brockmann’s book is a solid introduction to the grid as a design framework. It’s good to read if you know nothing about design and good to read if you need a refresher in the basics, before you slide down what can be a slippery Flash-driven rabbit hole into a world where nothing makes sense.
Universal Principles of Design - Lidwell, Holden and Butler
This book is not just about graphic design, but is more, well, universal, as the title suggests. It’s organized alphabetically, with one page of explanation and one page of examples for each of the 100 principles they’ve chosen. They admit that there are more than 100 principles, but they had to cut it off somewhere. Included are the 80/20 rule (or pareto’s principle), the Fibonacci sequence, the golden ratio, the baby face principle, and so on. I was so fascinated by it that I couldn’t put it down.
Michael Beirut is a towering figure in design, as is the company he works for, Pentagram. His little collection of essays includes some gems, though not all are equally worthy of recommendation. The book has been widely recommended by others; maybe they were more evenly impressed by the collection than I was. Good one for checking out of the library because all the essays go pretty quickly so it would be nice to read on the train.
This last one is a blog you might want to follow that features design related books, articles, entries, videos and so on. I am new to following it, but I like it so far.
Contextualizing the story about diabetes in New York by including data at the national and global level is quite smart. Sticking with maps to tell the whole story lends consistency.
What Needs Work
Comparison maps like this are clearest when their scales are the same. I see no reason that they should be different or why the colors need to be different. In the sense that the scales, in fact, are different, I appreciate the choice to use different colors. At least there’s some visual indication that direct comparison between the maps is not a good idea.
With respect to the graphs, it appears that they are all the same, just different populations, but that is not the case. The city and national data shows prevalence rates but the global data shows mortality, not morbidity. Close readers can figure it out.
First, the numbers 1-5 need to clearly relate to something. I was looking for them to relate to particular areas but then I realized they were more like time stamps. If that is the case, then it would be nice to have the actual time stamps or some kind of approximation. More importantly, I had to ask myself if this graphic helped me understand the “sequence of events” at all. And it didn’t. But maybe others see some value here?
This is an animation based on twitter data from Obama’s inauguration day in the US – Inauguration was at noon. In case you weren’t a twitter user at the time, it is worthwhile to point out that twitter had partnered with Facebook for the day to increase usage. Both twitter and facebook were encouraging users to point their comments towards the topic of the inauguration.
I like it because its like watching fireworks from above and gives a tangible sense of the excitement amongst Obama fans that day. Best thought of as an emotional animation of political temperature than as any kind of quantitative data. I wouldn’t even call it an information graphic/animation. I would call it popcorn, animated.
What Needs Work
I have the same problem with this animation that I have with twitter which is that I really don’t know what good they do, even though I’m intrigued. I’ve been trying to figure twitter out by using it and I still don’t see the appeal. Thus, it is quite alright to think this animation is pretty, but dumb.
This is one amazing piece of advanced pie chart. The trouble with mapping browser market share is that the number of people online keeps growing so absolute numbers don’t mean anything for more than a minute – most figures with respect to market share are giving no more than a cross section, a snapshot in time. This goes way beyond that and breaks out of the cartesian coordinates, too.
This works by starting at T=1, a red dot in the middle of the graph when the internet was in its infancy. At that point Mosaic was king which got clobbered by Netscape. Then Internet Explorer grew and then took off when it started to be bundled with all Windows installations. Remember those lawsuits? Who can forget. Netscape became Mozilla, which is now known as Firefox to most of us. Safari and Opera have some share, but it’s negligible. The game now is between IE and FF with enough representation by the smaller browsers that we cannot ignore them.
The graphic is great as it shows how many total users are online over time and what proportion of those users log-on with IE, FF, Safari, Opera, and others. So smart. Even managing to capture the changing names and ownerships of the browsers without cluttering things up with text box descriptions.
What Needs Work
I’m so impressed by this that I can’t think what needs work. Here’s where readers come in. What is wrong with this graphic? Anything? It was just a class project, so it’s hard to fault him for anything, even little things, knowing that he wasn’t aiming for a professional audience.
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…
