The New York Times has a clever feature by Carl Richards, The Sketchpad: Personal Finance on a Napkin, explaining finances with drawings on napkins. The image above may be my favorite. What’s yours?

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NYTimes Year in Review

The above is just a sample — click through to see the entire chart.

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US-Asia balance of trade (click to enlarge)

I like the concept but I think it still needs some work. The data I have is only from one month but I’d prefer to have an entire year. I also want to be able to show some change over time. I could obviously have two stacked charts, but I’m thinking of other ways to show the change.

Merry Christmas and Happy Holidays to all!

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The lines in the map above represent U.S. merchandise exports to Asia, by U.S. state and Asian country in 2007. The thinnest lines represent exports of greater than U.S.$2.5 billion; mid-thickness lines, greater than $5 billion; thickest lines, greater than $10 billion.

This image functions both as an infographic and as a introductory image to the Asia Matters project. It is the first image visible on the homepage, so I wanted to make it beautiful and iconic. At the same time, the entire website is dedicated to representing data in innovative, graphical ways. As such, the image needed to convey information — or at the very least, convey the concept of conveying information.

I think it succeeds. The lines clearly show clusters of high-density/high-value trade with Asian trade partners. Beyond the obvious Japan, China, Taiwan and South Korea, we also have Singapore, Hong Kong, Australia and India. An early draft of this visualization used straight lines between the U.S. origin and Asia destination. However, by curving the lines I was able to make each line distinct, even at this relatively low level of resolution. It was more work to custom curve each line, but well worth it in the end.

However, at the scale and resolution of a website image, I was unable to label the origins and destinations. This is unfortunate because there is some interesting data being displayed. The obvious exporters to Asia are well-represented: California and Washington State have bold lines running to Japan, China and others. Texas and New York are also clearly visible. Less visible are the two lines originating just east of Texas: they represent Louisiana’s trade with Japan and China, which is quite significant.

The beautiful background world image is courtesy of NASA’s Blue Marble.

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I’ve talked the last two weeks about a series of animated, interactive graphics used by Hans Rosling to illustrate the differences, and surprising similarities, between the global “haves” and “have nots.” The first week, I showed how a little animation can make great graphs. The second week, I analyzed Rosling’s progression from simple graphs — which give the audience a framework for understanding — to complex, nuanced analysis of reality. Rosling uses animation to build upon his previous graphs for a seamless interaction with the audience

In this final installment, I look at just the following slide, in which Rosling shows not only the child survival rate and GDP per capita for each of 200 countries in the world, but he also displays data by quintiles for five African countries.

The three countries displayed are, from the top, South Africa, Uganda and Niger.

What I like best about this slide is that it builds upon the sequence Rosling has already established. It goes one step further by introducing the spread of data. As Rosling says in the video, “averages can be deceiving.” By introducing this extra layer of data, we are able to deduce many things:

• Although on average South Africa is a “rich” country, the poorest there are still poorer than the very richest in Niger and Uganda.
• However, the poorest 20% in South Africa, who make about the same as the richest 20% in Uganda, have the same child survival rate. This is amazing news: somehow, the whole country has better health care, even for those who are not as rich.
• Even more amazing is the dramatic difference in health (child survival) between those in Niger and South Africa who earn about the same amount of money (the richest in Niger vs. the poorest in South Africa). The survival rate is nearly 10 percentage points higher in South Africa. Again, South Africa and to a lesser extent, Uganda, offer better health care no matter the income.
• In Niger, notice the slight uptick in child survival rates for the very poorest 20%. Perhaps these very poorest receive attention from international organizations, but they somehow show a survival rate almost equal to the very richest in their country.

And we could infer all of that simply by adding a few lines overlaid on the graph! The conclusion, then, is to not be afraid of adding data. By introducing it slowly, building it up one layer at a time, Rosling is able to inform the audience of some very complex data. he does not sacrifice data accuracy or readability. As a result, he gains a reputation reliable, credible source of information and analysis.

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Last week I began an analysis of the images Hans Rosling uses in his presentation on the relationship between the global “haves” and “have nots.” In particular, his focus is on how people’s conceptions of the global North and global South (different terminology for the same concept of haves/have nots) may have been an accurate reflection of reality at one point in history, but may not be accurate today.

To make his case, he relies on a few sets of time-series data which he presents to great effect in some animated graphics. One thing that I like about Rosling’s work is that he start simple to get his audience to understand what he is talking about. Then he builds in data to make the presentation more accurate and nuanced.

For example, he presents a graph here that compares child survival rates with per capita income. Circles toward the top show better chances for survival, while circles toward the right show wealthier countries. Rosling adds a very helpful label (“good” and “bad” for health) to explain the “survival” scale, which some readers may find cryptic:

Each circle represents a collection of counties in one region of the world. The upper-right turquoise circle represents the rich and healthy OECD countries (the “developed” world). The size of each circle is proportional to the population of each region. So already we have a lot of information packed into a simple graph.

He then introduces some complexity and nuance to the picture. He breaks the red, sub-Saharan circle into its constituent countries (in the video, there is a nice little animation of the “explosion”). He highlights the two extremes. Mauritius is relatively well-off and healthy, while Sierra Leone is truly awful at the very bottom:

This is an excellent technique: first he shows the overall picture, then he adds complexity. This also fits his thesis perfectly — when we think of Africa, we think of poor countries with little access to health. But this isn’t necessarily the case everywhere. Mauritius is doing pretty well, as is the South Africa (the unlabeled, larger dot directly below Mauritius). In the case of sending aid to Africa, then, organizations and individuals must have a nuanced view of the situation to effect the most good.

Here is the complete graph for all 200 countries, showing an immense amount of information. But it is very readable, in part because Rosling has built it up slowly.

Conclusions

• To show complex and nuanced data (and no data of use is ever simple), use elements that allow the audience to grasp the overarching narrative first. Rosling has the advantage of using video and animation to build up; but a static image should still strive (perhaps using different techniques) to guide the reader from simple to complex.
• When explaining data that may have confusing terms or unfamiliar measurement scales, do not be afraid to clearly label the implications.

Join me next week for: Animated graphs, part III

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I happened across this video on the TED.com network. In it, professor Hans Rosling demonstrates a number of good practices in conveying information to an audience in an understandable and innovative fashion.

It’s also quite enjoyable to watch him really interact with his data. The whole video is viewable here. It’s about 20 minutes long but quite worth it. What follows are a series of screenshots that explain some of the highlights of his techniques for information display.

1. This screen shows child survival as a function of GDP per capita. In other words, it answers the question: is there a link between how much money you make and health? The answer, viewable inthe graph below, is a clear yes, indicated by the linear progression of countries up the right hand side of the graph.

This is a clear graph, showing a lot of data in an organized fashion: mortality rates, GDP per capita and country population (the size of the bubble) are all visible. Notice all this data is for one year, 1962.

2. What makes this simulation great, however, is that it progresses from 1962 all the way to the present. It is particularly in the video. Here I show the end state at around year 2000, which shows that many countries have progressed up the wealth and health ladders. (There are also more countries in existence!)

3. Now for a separate graph. This one shows the relationship between life expectancy and fertility rates. Like the graph above, this one shows an amazing amount of data. He does not sacrifice accuracy in the name of trying to make his presentation understandable to the layperson. Instead, he has used good design and a judicious amount of explanation to make the data speak for itself.

Looking at the data, then, compare the situation in the graph from 1977…

4. … with the amazing change in 1999. Prof. Rosling gets very excited at this point, calling it “a completely different world” from 1977. He’s right: nearly every major country has increased it’s life expectancy and decreased it’s fertility rates.

5. Look at how involved Rosling is in his data! He’s interacting with the data, not merely talking about it. By displaying it in easy-to-understand terms, he is able to draw in his audience to interact with the data and think for themselves.

Conclusions?

• Animated graphs can help show data over multiple time periods — it makes intuitive sense for events that unfold in time to be animated.
• Do not sacrifice data accuracy or quality in order to make analysis understandable — no matter if you are talking to experts or laypeople. Do not dumb down; instead, lift all up to a higher intellectual level by using smart design. Treat people as smart, and they will be smart.

Join me next week for: Animated graphs, part II

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For example, the following combination of graphics from CNN.com shows the difference between the electoral votes in 2004 and 2008 (click for full size):

Election map comparison: 2004 and 2008

More after the break…

What is immediately apparent in the map above is two things:

1. The large block of red Republican states in 2004.
2. And the extent to which Obama and the Democrats broke up the continuity of that block in 2008.

The news was full of stories about red states from 2004 such as Virginia, North Carolina and Indiana being vulnerable to Democratic pressure. What the map shows in an instant, however, is the extent to which that matters. Look at how the blue “grows” from the edges of the country toward the center.

The map also highlights the profound demographic shift occuring in the United States. The rural countryside — Republican territory — is slowly losing its population. Urban areas are growing, adding to the ranks of the Democrats. The nation is also becoming more diverse: whites made up 74% of the electorate this year, down from 77% in 2004 and a high of 90% in 1976 (Washington Post, 11/6/08, p. A34).

These profound changes occur slowly and that’s why we need to be reminded of them. Graphics are one great way to do that.

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