There are virtues to an illegible chart

21 April, 2010 (18:54) | charts, data visualization | By: jerome

It all started with an extreme pie chart

A few weeks back, there’s been a chart on aid who’s made the rounds of the internet:

All US ODA by recipient, 2004-2008, OECD data, taken from USAidWatchers.com

What this chart shows is that US aid is concentrated in a few countries. The article explains that this is a result of the 3D doctrine, which ties development with diplomacy and defense. This is why US gives so much to strategic countries like Afghanistan, Iraq, or Sudan, but relatively little to India – highlighted in the chart, which has “a huge chunk of the world’s poor”.

When I saw that chart I was planning to create a chart or a data visualization on the same subject for my work. The original chart was being heavily criticized for its form, because half of it is not legible. Chart purists don’t like pie charts for that very reason – they are difficult to read, especially if you add more items. But I found the chart interesting. It states in a very striking way that more than a hundred countries in the world get next to nothing from the USA.

An apology of  extreme charts

There are virtues to an illegible chart. In fact, I don’t believe that a chart should give equal prominence to each and every of its datapoints. In most cases, it’s here to support a story, so all it should do is bear a message. Tufte popularized the notion of data-ink ratio, which states that a chart designer should use the largest share of ink to represent data, not everything else. I feel this is taken too literally by many.

There is a tradition of extreme charts which purposely break presentation rules because of the very nature of the subject they are plotting. A famous example is Al Gore on his lift – if CO2 emissions hadn’t increased so much, he wouldn’t need that lift to show his chart.

 

Al Gore on his lift, the most memorable image of An Inconvenient Truth

Another one – from the NY Times, one of the charts that Matthew Ericson showed in his Infovis 2007 keynote speech:

In perspective: America's conflicts. NY Times

Click to see the full image - it is big. I really like this chart.

Again, if the number of US soldiers killed per month had not been so high in WWII, the 2nd group of bars wouldn’t overwrite the text above and sky-rocket to the top of the page. The logical thing to do would have been to scale the chart so that the maximum values would fit in a well-delimited space, and maybe use a logarithmic scale so that the values for other wars would remain legible. That’s how we would have done it if we had to plot that kind of series in an OECD book. The fact that the NYT designers chose, on the contrary, to let the data rise all the way to the top of  the page expresses in a very powerful way the extreme nature of the WWII casualties.

“A conventional chart couldn’t hold all that horror”, the chart seems to say. Likewise, if CO2 emissions had grown more steadily over the past couple of centuries, Al Gore wouldn’t have needed a lift. By the same token, if aid values to about 100 countries were more than negligible, they could be seen on that chart. So granted, there could be more academic ways to show that, like a giant bar chart with values too small to see for all but a handful of states. But all in all I think the original pie chart does a good job in communicating that in a nutshell,  ad absurdum if you will.

My take on the chart

I wanted to work on a specific subset of aid data, that which goes to fragile states, which are, simply put, the 43 countries in the greatest need of aid. Now official aid from developed countries, like US aid, is very concentrated, meaning that only 10 of these countries got more than $1b in 2008. Only 10 countries got more than $100 per capita in that year.

Another interesting aspect of the data is that for many of these countries, aid only mostly from one or two donors, so they are vulnerable to a policy change in that country. That’s what I wanted to show in the representation.

 

 


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Comments

Comment from Andrew Begin
Time April 21, 2010 at 7:18 pm

You bring up an interesting point on the pie chart; that the illegible labels can in fact be seen as a key part of the visualization, being used to prove the graphic’s point. While the pie is not useful for consuming the data, it does provide a pretty clear message.

The relationship between data, art, bias/motive and other factors is becoming a very hot topic in dataviz and a fun one to discuss. There’s quite a range of visualizations between Napoleon’s march (http://www.edwardtufte.com/tufte/posters) and the stock market mountains (http://infosthetics.com/archives/2010/03/high_altitude_the_stock_market_trends_as_realistic_mountain_ranges.html), and it’s interesting to see how they’re all received and interpreted.

great post and examples.

Comment from Robert Kosara
Time April 22, 2010 at 12:58 am

Interesting point, but I still think it’s a bad chart (the pie chart, that is). Having all the labels plot over each other does not look like a deliberate decision, but rather looks careless and lazy.

You’re right of course that not every data point has to be shown, especially when the goal is to make a point rather than to provide the data for the reader to explore. And in such a simple chart, it’s not really possible to show that much data, anyway.

To really make a point, they could have shown the three or four largest slices and then combined the rest into an “Other” slice labeled with the number of countries represented in there. Or maybe put the entire list of countries next to that slice. Each of these would have made the point, and would have been a clear decision rather than the trainwreck this chart is.

Comment from jerome
Time April 22, 2010 at 4:41 pm

well, the original pie sure could have been sleeker. I would never have made it like this, probably because I never would have used a pie chart to begin with. And if I was commissioned to make one, I would have done exactly as you say.

I appreciate that some people are not trying to inform with an objective, orthodox chart, but are rather trying to convince with charts which are formally questionable (yet error-free), but which are effective.
it reminds of the road to recovery chart from the Obama administration. (http://flowingdata.com/2010/02/17/road-to-recovery-is-the-recovery-act-working/). I can find a million reasons why this chart is flawed (well, at least a couple) but I think it’s effective as a communication tool.

All in all I think people will be able to make stronger points if they use data, and it’s great to see that some people are able to come up with different ways of doing it which we “chart people” would not even dare to consider.

Comment from Robert Kosara
Time April 22, 2010 at 6:27 pm

I’m trying to recreate the pie chart using the fragile states data you published, but I don’t anything close. Do you know a source that covers the same time span? It seems the fragile states data is only for 2008.

Comment from jerome
Time April 22, 2010 at 7:25 pm

yes, the problem is that technically the list of fragile states can change from one year to the next. but that’s where the data came from originally:

http://stats.oecd.org/Index.aspx?DataSetCode=TABLE2A
for aid type I used “Memo item, ODA Total excl. debt”

Comment from Robert Kosara
Time April 23, 2010 at 4:59 pm

The numbers still don’t add up. Egypt and Colombia are not fragile countries according to the data, and I wonder if especially these two countries were recently. And even if, I’m getting a much smaller fraction for Iraq and Afghanistan than in the original pie chart.

Comment from jerome
Time April 23, 2010 at 5:27 pm

I think the underlying data for the original chart must be something like
Recipient;% of specified
Iraq;0.330141247693019
Afghanistan;0.0903720278156409
Sudan;0.0437142862351714
Colombia;0.0329666269433484
Ethiopia;0.0320139965446183
Egypt;0.0288552938867211
Jordan;0.0218504584184366
Pakistan;0.0210514545601498
Nigeria;0.0204034033626468
Congo Dem. Rep.;0.019217902353561
Palestinian Adm. Areas;0.0173341541333622
Uganda;0.0170234979329087
Kenya;0.0169238351455958
South Africa;0.0120447340067054
Zambia;0.01133676359683
Haiti;0.0111915233153191
Georgia;0.00948959891197406
Serbia;0.00924797742273248
Bolivia;0.00920470918824052
Peru;0.00917165031244892
Tanzania;0.00908025224408389
Mozambique;0.00867248559599259
Liberia;0.00829340238424988
Indonesia;0.00827371290675635
Mexico;0.00652013400998694
Lebanon;0.006190031412009
Zimbabwe;0.00586418271348954
Honduras;0.00581982061913684
Somalia;0.00581629595958553
Micronesia Fed. States;0.00576245374643964
Philippines;0.00552605847653276
Nicaragua;0.00537243193608942
Kazakhstan;0.00498508400539881
Rwanda;0.00498301782566183
Ghana;0.00469508960231509
Armenia;0.00465923530687935
Malawi;0.00438017950240315
India;0.00436425776442999
Cambodia;0.00425341329854049
Madagascar;0.00424709321934503
Botswana;0.00421585744332135
Angola;0.00396925281471412
Ecuador;0.00394385095794777
Bangladesh;0.00378037967875767
Chad;0.00364376873614824
Nepal;0.00356829240575638
Mali;0.00351724561225464
Guatemala;0.00351639483236294
El Salvador;0.00349646227490035
Eritrea;0.00330527987923787
Korea Dem. Rep.;0.0031245499222448
Tajikistan;0.00310960050414786
Senegal;0.00310753432441088
Namibia;0.00308650790708754
Bosnia-Herzegovina;0.0030528413313733
Azerbaijan;0.00305247671141972
Kyrgyz Republic;0.00299134209920215
Marshall Islands;0.00298891129951159
Cote d’Ivoire;0.00267995665884152
Viet Nam;0.0026290314053243
Burundi;0.00260265722868174
Macedonia FYR;0.00257652613200822
Guinea;0.00250262982141522
Uzbekistan;0.00237853749721218
Albania;0.00237744363735143
Sri Lanka;0.00215162234609848
China;0.00213837448778493
Niger;0.00210835411160652
Moldova;0.00181908894842998
Thailand;0.00176330209553164
Yemen;0.00176220823567089
Benin;0.00169584740411863
Croatia;0.00167457790682623
Timor-Leste;0.00160542165562982
Sierra Leone;0.00141764237953413
Guyana;0.0013744956850267
Mongolia;0.00134338144898754
Myanmar;0.00133195669044191
Central African Rep.;0.00130400249400048
Burkina Faso;0.00129172695556316
Paraguay;0.00121746602501658
Palau;0.00121685832509394
Cameroon;0.00116471767173144
Mauritania;0.0010080526316749
Dominican Republic;0.0009371948206951
Cuba;0.000751117104382793
Panama;0.000721704428127027
Cape Verde;0.000719273628436467
Saudi Arabia;0.000695694871438043
Venezuela;0.000607335302686216
Djibouti;0.000544377590700733
Lesotho;0.000538543671443391
Congo Rep.;0.000440460903929327
Jamaica;0.000433046964873121
Vanuatu;0.000429036145383699
Gambia;0.000296557562248222
Laos;0.000255233967508716
Argentina;0.000252195467895517
Togo;0.00024247226913328
Syria;0.000240284549411777
Turkmenistan;0.000216462712444297
Swaziland;0.000211114953125067
Algeria;0.000206982593651116
Iran;0.000204065634022445
Guinea-Bissau;0.000176719137503654
Malaysia;0.000166752858772361
Grenada;0.000123241544311351
Gabon;9.61381277616164E-05
Belize;9.61381277616164E-05
Fiji;0.000082161029540901
Barbados;7.35316906394158E-05
Samoa;6.10130722330359E-05
Tonga;5.57868528983336E-05
Kiribati;5.34775931923024E-05
Trinidad and Tobago;4.08374348013946E-05
Papua New Guinea;3.76773952036676E-05
Sao Tome & Principe;2.50372368127598E-05
Mauritius;2.27279771067285E-05
Maldives;1.64078979112746E-05
Solomon Islands;4.8615993811184E-06
Equatorial Guinea;4.13235947395064E-06

Here I’ve listed all the developing countries with which USA had a positive aid flow over the period, removed the aid flow to unspecified countries, and used constant USD. I suspect the original chart might have used current ones, which is why Egypt, Colombia and Ethiopia are swapped in that table (but there’s a very small difference between those)

Comment from Robert Kosara
Time April 24, 2010 at 3:43 am

Awesome, thanks! That makes a lot more sense.

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