Archive: Feb 2009

What Works

I couldn’t end the agriculture week without including a bit about the state of the oceans as a source of food. To be clear, agriculture is often related to farming the land and raising land-based livestock. Aquaculture is the term used to talk about fish farming. Catching fish out of the open water is not considered agriculture. I ought to have used the broader theme of food production.

I like this graphic because it’s got multiple levels of information – aquaculture vs. open water catches by volume, fish catch by country, and the status of the stocks by oceanic region. It can be difficult to figure out how to represent global level data when it isn’t possible to fall back on national boundaries. It’s a little odd to see the oceans chunked into squares, though.

What Needs Work

This graphic has a sort of not-quite-done look to it overall. The treatment of the aquaculture vs. open water catch could have been more elegantly integrated – superimposing the red and blue blocks on one another makes it look a little bit like the kids left their wooden blocks laying around on top of a map. I might have preferred pie charts with two pie pieces – one for the aquaculture bit and one for the open catch bit to communicate relative share. The size of the pies would be determined by absolute value of the total catch + aquaculture.

What I really would have liked to see would have been a more logical representation of the catch volume by oceanic region. The colors chosen don’t indicate much of anything, except perhaps the static areas which are just blue, standard ocean color. What would have been great would have been to indicate the relationship between areas that have decreased yields because they have been overfished and areas that are currently being overfished which will soon have decreased yields even though their current yields are high. This is complicated because the largest increase and the largest decrease are far more closely related to one another than they are to steady areas or areas with only a slight increase. It would be great if the ocean regions could be depicted by the replacement rate with an extra classification for areas that have been severely over-farmed to the point that the concept of replacement rate no longer has the same meaning.

Relevant Resources

United Nations Environment Programme – GRIDA (2009) State of the World’s Fisheries and Aquaculture

Public Service Announcement: The amount of mercury and arsenic found in predator fish is high enough that people who eat these fish recently can suffer the effects of heavy metal poisoning. To figure out what is safe to eat and what should be avoided, check out the Monterrey Aquarium, one of the best, if not *the* best source for guidelines about what to eat when what you’re eating lived in the water. They even have an iPhone app for easy reference at the grocery store and your favorite restaurants.

Monterrey Aquarium Seafood Watch

What Works

The first map was produced by the USDAs Economic Research Service in 2004 to show the change in milk production by US region from 1980 – 2003. The accompanying text is surprisingly brief, “Since 1980, milk production in the U.S. has increased almost 33 percent. Regional production growth has been most pronounced in the Pacific and Mountain regions, the result of development of low-cost systems of milk production in the Pacific region and some Mountain States. Growth has been much slower in the Northeast and Southern Plains, and the other six regions have seen essentially flat or declining production.”

The graphic is a fairly straightforward way to combine a map with a bar graph. I like it better than if it were just a bar graph with regional labels, but I would like it even more if it were better integrated so that the data from the graphs were embedded in the map, maybe by showing the change in production by color or by applying concavity/convexity to the map.

What Needs Work

There is a serious drawback to the map + graph combination. One of the problem with images is that they tend to appear as sealed, complete narratives that are telling the whole story. It’s hard to interrogate an image, harder than interrogating a text. We’re taught not to believe everything we read, but those strategies don’t translate directly into the world of images. The important missing information here is that the population in the US is shifting to the south and west out of the north east. The image doesn’t suggest causal links; but the text does. However, it leaves out the no-brainer that since milk is a localized commodity, population growth is generally going to result in increased milk production in that area.

Bonus Image

I found this image depicting population density and population change in the US. Cool colors indicate a loss of population; warm ones suggest growth. The z-axis represents human volume. A solid graphic. I have looked and looked and been unable to find the original source which just goes to show that once information hits the digital domain it really does have a life of its own. Hackers were right about that, information wants to be free.

Relevant Resources

Blayney, Donald. (2004) Milk production shifts West USDA Economic Research Center.

Dupuis, E. Melanie. (2002) Nature’s Perfect Food: How Milk Became America’s Drink. New York: NYU Press.

Mendelson, Anne. (2008) Milk: The Surprising Story of Milk Through the Ages. Knopf.

What Works

This is a simple concept, displayed beautifully. The demand for food will outstrip our current ability to produce it.

What Needs Work

Since the timeline is already marching along the x-axis I want to see some context. Tell me when Nobel Peace Prize winner Norman Borlaug revolutionized agriculture and increased yields by orders of magnitude. Show me how global population maps onto the demand for food. Tell me if I should be concerned about the growing Chinese and Indian middle classes who will probably demand more meat (and hence more land per kcalorie). This data is out there and adding it to this graphic would have elevated it from simply beautiful, to being both beautiful and smart.

Relevant Resources

Doyle, Stephen and Zavislak, Zack. (November 2008) “The Future of Food.” in Wired Magazine. 188-205 [So sorry, but I cannot find this article online. If you want to see more, let me know, because the food info porn went on for pages and I scanned them all. There was a centerfold on cows.]

Nobelprize.org Norman Borlaug Biography

Naim, Moises. (March/April 2008) Can the World Afford a Middle Class? in Foreign Policy.

Watts, Jonathan. (20 May 2008) More wealth, more meat. How China’s rise spells trouble in The Guardian.

Why This?

Continuing what I have decided will be an agriculture theme for the week, I went looking for data related to energy efficiency of diets. This concept first became news in the 1970’s during the energy crisis, championed in the book “Diet for a Small Planet” by Frances Moore Lappé which has recently been released as a 20th anniversary edition. I was interested in getting to the bottom of the planetary (rather than the personal) part of her argument which is that to produce unit weight of protein in the form of beef/veal, the animal is going to need an input equivalent to 21 units of protein and we’d be globally better off if we just ate the plant sources ourselves in terms of energy consumption and intelligent stewardship of the planet. I didn’t quite find what I was looking for to back up that data (yet) but I did find the contemporary twist on that argument which relates dietary choice to greenhouse gas emissions.

What Works

The first graphic doesn’t work all that well and probably makes no sense to you so we’ll come back to that. The second graphic, from the EPA, is not particularly pretty, but it has strength in simplicity and it makes intelligent use of the x-axis to represent greenhouse gas sinks. (Note: The visual representation does a great job of communicating that are emissions dwarf our sinks better than reading a number on a page would do.) Whereas the first graphic fails miserably to represent the difference in the energy efficiency of diets, the second graphic at least conveys the conclusion of the report that went along with the first graphic which is that the difference between eating the standard American diet and a vegan diet is, “far from trivial, …[it] amounts to over 6% of the total U.S. greenhouse gas emissions.”

The details of the report accompanying the first graphic are worth perusing and I only wish they would have spent more time trying to represent them graphically. The authors, Eshel and Martin, compute the comparative impacts of transit choice vs. dietary choice and find that, “while for personal transportation the average American uses 1.7 × 10^7–6.8 × 10⁷ BTU yr⁻¹, for food the average American uses roughly 4 × 10⁷ BTU yr⁻¹.” That would make an excellent graphic in about ten different ways and catapult them past the problem that many readers are going to get tripped up looking at the orders of magnitude and units and miss the point.

What Needs Work

The first graphic is supposed to show the composition of the hypothetical diets considered. The mean American diet as reported by FAOSTAT has a little break-out component that provides more detail about the constituents of the animal products category but it took me a long hard look to figure that out. The break out part should have been constructed so it wasn’t exactly the same scale as the rest of the graphic (which it isn’t, by the way) otherwise it just reads like another column with viewers liable to assume that they can follow the scale on the y-axis. But the y-axis doesn’t relate to the break-out part at all – only the percentages listed alongside it are salient.

My bigger problem with the first graphic are the next five bars. Just to help you navigate α represents the proportion of the standard 3744 kcal diet that comes from animal sources. See α, think animal. A key would have been nice. Now that you know that, looking at the graph, it appears that each of the diets gets the same amount of kcals from plant sources because the green segments are all the same size. However, this is not actually what the authors are trying to convey. You have to read through the text quite carefully to pick out what proportion of each diet comes from animal sources overall. Once having done that, this graphic can help you further breakdown how those animal sources are apportioned. For example, the ovo-lacto group gets none of their animal protein from animal flesh – only from dairy (.85 of animal protein total) and eggs (.15 of animal protein total). But it took a good ten minutes of going between text and graphic to figure out what they have charted here. In all honesty, I’m still a little confused about whether the last three diets just switch out fish for meat for poultry and keep the same total number of kcalories in the animal flesh category relative to dairy+eggs. And I certainly can’t tell if any of those hypothetical diets have a greater or lesser proportion of kcals coming from plants by looking at this graphic.

In summary, the two graphics here were not trying to make the same point. The first one was trying to explain how the authors modeled their hypothetical diets in order to convince you that, in the end, and in conjunction with some other writing and graphic representation, if Americans moved to vegan diets the national greenhouse gas emission rate would drop by 6%, on par with what would happen if everyone started driving a Prius. The second graphic does this much better using aggregate data (and thus a totally different approach than Eshel and Martin).

Relevant Resources

Eshel, Gidon and Martin, Pamela. (May 2005) Diet, Energy and Global Warming. Submitted to Earth Interactions.

United States Environmental Protection Agency. (April 2008) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2006

Moore Lappé, Frances. (1971, 1991) Diet for a Small Planet Ballantine Books.

What Works

The clean color palette gives this graphic the aseptic look of ‘pure information’ while the inclusion of the numerical data (in tons of manure) backs up the intention of the graphical representation with checkable facts. I like the three different versions of the same concept; it increases my confidence in the basic point that raising livestock produces vast quantities of waste. It’s mostly a pigs and cows problem but it isn’t restricted to Iowa.

Measuring animal waste per capita is a brilliant way to remind readers that they play a role in this system. These animals are a special class of animals called livestock which means that they only exist to provide food for humans. Measuring waste per capita is a subtle, but incontrovertible way to remind us that all animal eaters are contributing to the pile-up of animal waste.

What Needs Work

The animal cut-outs overlaying the outline of the nation in the first panel and Iowa in the third panel seem like a first draft idea, not a final draft idea. The relative size of the animals seem to relate to their ability to fit within the outline behind them more than to relative proportions of waste.

I have no idea why animal waste should be related to the weight of a Prius. This is an unnecessary politicization of the data. There is no logical reason to measure animal waste in Priuses. I can only think of political reasons to do so. Tons is just fine. The Prius portion of this graphic could be lopped off and no information would be lost.

It makes sense to me to move sequentially through levels of analysis. In that case, I would have put Iowa in the middle so that our thoughts would move from largest level of analysis to smallest. This is especially true in this case where the Iowa level data and the national level data measure animal waste per capita while the cow’s waste is measured annually, not per capita. I would have translated the cow’s waste into per capita data, too, just to make the narrative of the graphic more cohesive.

Bittman writes, “Americans are downing close to 200 pounds of meat, poultry and fish per capita per year (dairy and eggs are separate, and hardly insignificant), an increase of 50 pounds per person from 50 years ago. We each consume something like 110 grams of protein a day, about twice the federal government’s recommended allowance; of that, about 75 grams come from animal protein. (The recommended level is itself considered by many dietary experts to be higher than it needs to be.) It’s likely that most of us would do just fine on around 30 grams of protein a day, virtually all of it from plant sources.” And that’s something I’d like to see represented in the graphic too – the fact that Americans don’t need meat to meet their nutritional needs. That isn’t to say we could easily do without meat – much of eating is cultural and from that perspective many Americans would be set adrift, at least from a culinary perspective, without meat.

Bonus: I would like to see more about the waste lagoons – something that talks about what happens to the waste over time would be incredibly useful because this graphic begs the “where does it all go?” question.

Relevant Resources

Bittman, Mark. (27 January 2008) Rethinking the Meat Guzzler in The New York Times, The World.

Flora, Jan; Chen, Qiaoli; Bastian, Stacy; Hartmann, Rick. (October 2007) Hog CAFOs: The Impact on Local Development and Water Quality in Iowa Report from the Iowa Policy Project.

Book Recommendation

Donald Norman’s “The Design of Everyday Things” is currently my commuting book which I’ve had ample opportunity to read because the F train here in NYC is the most capricious multi-ton object I’ve ever encountered. This is a good book if you want a straightforward introduction to basic design principles. Norman is an engineer by training which means he comes from a different tradition than, say, an architect. He goes through all sorts of examples of commonly encountered objects – keyboards, sinks, ovens, telephones – to help demonstrate that good design would benefit from prototyping and user-testing because, in the end, humans are fairly adept at taking clues about how to use an object from their first glance. When things aren’t obvious, it’s the fault of the design, not the fault of the person who has trouble figuring out how to put someone on hold or transfer a call with a phone system that can likely ONLY be used by a robot or an algorithm. He opposes beautiful design for the sake of beauty – glass doors stripped of plates and hand bars in service to the sleek glossiness of glass’s amazing material properties are no good if people end up pushing on the hinged side of the door when they should be pulling on its swinging side.

Norman offers users a set of criteria by which everyday design can be critiqued as well as some rules of thumb for figuring out particularly obtuse design challenges. He absolves humans their occasional mechanical buffoonery, “Humans, I discovered, do not always behave clumsily. Humans do not always err. But they do when the things they use are badly conceived and designed.” Norman doesn’t go so far as to suggest that the objects themselves possess agency, it’s not the fault of the things, but of the people who designed them, “When you have trouble with things…it’s not your fault. Don’t blame yourself; blame the designer. It’s the fault of the technology, or more precisely, of the design.” This part of his theory is the weakest. It’s rather simplistic to blame the designers without interrogating why they produce shoddy designs. He hints that designing for the sake of beauty is part of the problem and that user testing happens in the market place where negative reactions are likely to kill the product line altogether rather than resulting in intelligent, sensitive redesign. Luckily, other books (Harvey Molotch’s “Where Stuff Comes From” for example) do a better job of revealing the motivations and constraints on designers.

Where Norman is at his best is in the many detailed examples of everyday objects gone screwy with clear, diagramatic prescriptions for improvement. Norman never rants about bad design just to sharpen his teeth. His examples are accompanied by constructive suggestions that are so clearly spelled out that readers are capable of critiquing his suggestions, a sure sign that the book succeeds as a teaching tool. Furthermore, Norman illustrates his discussion with photos, sketches and diagrams throughout which enriches the legibility of the project and subtly introduces readers to the practice of learning through drawing that is common in design practice, but not all that common outside of it.

Relevant Resources

Norman, Donald A. (1998) “The Design of Everday Things” Cambridge, MA: MIT Press.

Molotch, Harvey. (2003) Where Stuff Comes From: How Toasters, Toilets, Cars, Computers and Many Other Things Come to Be As They Are. New York: Routledge.

What Works

Wordles are generated by inserting a block of text into an algorithm that filters out typically common words like ‘the’ and ‘they’ and then picks out frequently used but relatively uncommon words like ‘Mexico’ and ‘resistance’. These images get used occasionally in academia on websites where people want to use images to represent something like a talk or paper where they don’t actually have images that go with that talk or paper, but they do have an abstract, transcript, or full text of the talk or paper. In the sense, that a wordle is a relevant image to stick in the hole reserved for images. In another sense, I encourage you to go out and find public use images that are available, relevant to the talk/paper, and intellectually provocative rather than creating a Wordle.

What Needs Work

Wordles appear to tell you something you didn’t know by revealing patterns. In fact, Wordles are bizarre artifacts that sit somewhere between images and text. As images, they are fairly ugly agglomerations. As text, they don’t make much sense. In fact, I think Wordles are excellent when referred to as icons of the dystopic side of instantly available, decontextualized factoids that anchor the downside of the internet era. With but a click a block of carefully crafted (well, maybe it was carefully crafted) writing is blown apart and reconstructed as a brightly colored lettered blob that is somehow supposed to indicate the essential components of the piece of writing. A bit insulting to the person who wrote the text, if nothing else. A good abstract or even list of works cited says more than a Wordle in a clearer fashion.

Relevant Resources

Jonathan Feinberg of IBM Research created the Wordle Generator.

What Works

This is a map of a website.

Let’s reflect on that seemingly straightforward sentence for a moment. This is a map of something that does not exist in space. Baudrillard comes to mind here – “Abstraction today is no longer that of the map, the double, the mirror or the concept. Simulation is no longer that of a territory, a referential being or a substance. It is the generation by models of a real without origin or reality: a hyperreal. (from Simulacra and Simulations, Baudrillard)” I’ll let you decide whether or not you want to accept the notion that there is such a thing as the hyperreal without any further digression down that rabbit hole.

The visual elegance here cannot be overstated. It’s a simple non-cartesian network map with absolutely no frills, labels, anything besides a hint of color. As a graphic, what works here is that, if you happen to have a basic understanding of how websites are built, you can quickly see what kind of site you’re looking at. Lots of blue means lots of links, lots of green means the designer is using a lot of css, lots of red (tables) is kind of old-school (not in a good way), and so on. But it does require some knowledge of how websites are put together to decode this representation. That being said, it’s a brilliant way to reveal the skeleton supporting the visual skin of the websites you visit. See the links at the bottom to be taken to the applet that will allow you to map out the structure of any site you like.

Though this may not at first glance appear to have anything to do with my post earlier this week about John Snow, both Snow and the Aharef web-map generator represent tools for the examination of patterns. Pattern recognition is an undersung analytic tool in the social sciences.

What Needs Work

I wouldn’t mind a little more color in order to break out the grey “other” category a little more. I would also love a color that indicated use of javascripting and flash, but I understand that would be a different technical hurdle altogether. If this kind of map could be combined with page traffic information, we’d really have an amazing graphic. Just imagine that the traffic following each link could be mapped, say by making the node larger or smaller based on flow (or we could stick with the color thing, and lighter hues would indicate less traffic while darker ones indicate more traffic). It would also be nice to get some meaning related to the length between nodes. Right now that distance seems fairly arbitrary, constrained by the size of the viewing window.

Relevant Links

Generate your own webmap for any site

Original post about this applet tool by it’s creator Aharef on Aharef

Baudrillard, Jean. (1998) Simulacra and Simulations from Jean Baudrillard, Selected Writings, ed. Mark Poster.

USA Today Flash Animated Graphic accompanying the headline “Deaths Down on America’s Roads”

What Works

Nothing is working here and I’m not just saying that because it’s flash and I can’t repost it. Please link through for a hot minute and look at it anyways.

What Needs Work

My problem with this graphic is that it is ONLY a map of the US, except for the few seconds when you roll your mouse over it. Even then, you don’t end up seeing a pattern, you just see little pop up windows with some numbers in them. Information graphics need to artfully, intelligently, dare I say cleverly weave the information into the graphic so that the two become greater than the sum of their parts. None of that happens here.

The map of the US is still just a map of the US. No shading, no numbers, no way to tell that we’re talking about traffic deaths. Even just mapping out the interstate highway system would have given a hint of a visual clue to tell us what we’re talking about. In the previous post, Snow stacked bars to indicate dead bodies. Maybe it’s a little over the top, but if we are addressing the notion of a change in body count, I would like to see some visual representation either of bodies or of change (change is more abstract and probably more appropriate for USA Today than a visual representation of a body count). Furthermore, I want to know if there really is a relationship between gas prices and body counts which *could* be explored looking across states. States tax gas at different rates resulting in variations from one state to the next. Sensitively factor in income and unemployment and we might be able to get a sense of how much gas prices impact mortality on the roads. Even more interesting would be whether it’s the fact that people aren’t on the road at all that prevents them from dying out there (no gas = no go) or if it is somewhat more subtle – perhaps people drive slower to be more fuel efficient rather than staying home and it is the slow down, not the no-go that keeps people alive. The more likely scenario would also point out that cars continue to get safer and that seatbelt laws work. If we could look at the data over time, we’d have a better idea how more quickly traffic fatalities dropped in 2008 than in other recent years, which would help factor in the cars-are-safer-now + more-states-have-seatbelt-laws effects.

This graphic falls woefully short of even hinting at any of these questions. I wish they had left it out altogether, forcing everyone to read the article in full.

What Works

This is a combination of a map and a chart whose creation helped epidemiologists understand that cholera was not caused by a ‘miasma’ carried by the fog from the river, but rather was a germ carried in the water. It’s one of my personal favorite early examples of information graphics as a tool not of publication, but of analysis and discovery. Snow mapped the area around the Broad Street pump and then represented deaths with bars (not dots as some later cartographers have done when re-presenting Snow’s maps). The bars end up looking like stacked bodies, reinserting the gravity of the situation into the fairly sterile context of the map as info graphic.

The pattern is imperfect, but clear. Proximity to this well is directly proportional to mortality risk. The point of this entry it to encourage the use of information graphics not only in the publication stage of the research process, but also in the analysis stage. Granted, epidemiology isn’t a social science, but this is a classic example that sets the scene for contemporary examples of graphics as tools of analysis.

What Needs Work

There are other more comprehensive maps of the whole neighborhood that show the patterns even more clearly. What I have here is just a close up, probably a mistake on my part. The full version is here as a pdf. The romantic in me wanted to restrict this post to the original grainy, scanned map* drawn by Snow himself.

The realist in me notes that even though I believe the creation of information graphics can be used as analytic tools, the story in the John Snow case isn’t a perfect fit. An article by Brody et al in The Lancet points out that, “Snow developed and tested his hypothesis will before he drew his map. The map did not give rise to the insight, but rather it tended to confirm theories already held by the various investigators.” So Snow didn’t get his brilliant insight just by examining the map but he did use the map as an analytical tool later in the process to help confirm his hypothetical hunches. It wasn’t like he just threw the map/chart together to present at a conference or while he was writing up an article which is how I feel many social scientists end up using info graphics.

*This version is actually the second version though it’s main difference from the very first map is that the pump has moved just slightly off from the exact corner of Broad Street closer to the house of 18 deaths.

Relevant Resources

John Snow website at UCLA School of Public Health where I found many maps.

Brody, H., M. R. Pip, et al. <2000) “Map-making and myth-making in Broad street: The London Cholera epidemic, 1854.” The Lancet 356, (9223): p64-68.