Few people have pointed us to some more of the Venn Diagram Challenge diagrams in response to the Venn Diagram Challenge Summary 1:

Patrick Murphy has pointed us to other works of his

It’s nicely made and has a goodness of Venn Diagram combined with a bar chart. If you are interested there are more versions of his works here, and here.

Bernard Lebelle

Here is my [Bernard’s] attempt at this. Based on information provided by Igor my [Bernard’s] feeling is that the question behind the Venn Diagram is “which combinations of tests are consistent over time”.

Bernard’s work is good at comparing between Autism and Autism Spectrum. Although it would have been better if it had the baseline counts somewhere.

We are still working on the second part of the summary.

The Venn Diagram Challenge which started with this entry has spurred exciting discussions at Junk Charts, EagerEyes.org, and at Perceptual edge. So I thought I will do my best to put them together in one piece.

Outcomes people created can be divided into 2 classes, first group dealt with the problem of expressing the “3-way Venn diagram of percentage with different base frequency”. Second group went a little deeper to figure out the better way to express what the paper is trying to express in a graphical way. Our ultimate goal is the second one, however, first problem is it’s self a interesting challenge and thus I will deal with them separately. ( Second group will be dealt with in the Venn Diagram Challenge Summary 2 which should come shortly after this article. )

Venn diagram converted into a table:

(For background you can look at the previous posts original entry, on Antony Unwin’s Mosaic chart, and Stack Lee’s bar chart.)
How to express 3-way Venn diagram of percentage with different base frequency better
Here are 4 graphs that I am aware of that falls in this category:
Stack Lee

Robert Kosara

Patrick Murphy

Antony Unwin

It is always amazing to see how people make cool graphics out of same data.
There were 4 things ( percentage, base frequency, structure, possible trend, and maybe more) or maybe more, from the Venn diagram that could have been expressed graphically. When we dissect the above graphs by the 4 things noted above, result is the following:

So the biggest differences between the graphs are the way in which the structure is expressed. Another point to note is how the different graphs addressed the issue of the base frequency. It’s hard to say which one’s the best because they all have points which I like. For example, to express percentage Antony’s Mosaic chart seems the most suitable since it is clear that it is showing a proportion by having gray area with the green area on the bar. To express base frequency, again I like Antony’s Mosaic chart since it gives heavy weight on the ones with more samples, which are the results that we should focus more on. As for expressing structure, it is tough call between Patrick and Robert, I personally like them both in a different way. Stack’s bar chart seems very good at comparing between Autism and Autism Spectrum which I should have put in the chart.

What we did:
We made 2 graphs
Figure 1: line graph of prevalence of best-estimate diagnosis at age 9 years conditioned on clinician (clinician was chosen arbitrarily)

Figure 1. Prevalence of best-estimate diagnosis at age 9 years with frequency of diagnostic combinations at age 2 years expressed as area of circle. Vertical line show plus minus 2 standard error bounds based on the implicit binomial distribution with Bayesian correction (*1). Upper graph represents the case where clinician is yes and bottom is for clinician no. PL-ADOS stands for Pre-Linguistic Autism Diagnostic Observation Schedule; ADI-R stands for Autism Diagnostic Interview–Revised.

Figure 2: line graph of prevalence of best-estimate diagnosis at age 9 years by combination of tests

Figure 2. Prevalence of best-estimate diagnosis at age 9 years with frequency of diagnostic combinations at age 2 years expressed as area of circle. Autism. Blue line represents the Pre-Linguistic Autism Diagnostic Observation Schedule (PL-ADOS); Green line represents Autism Diagnostic Interview–Revised (ADI-R); and Red line represents Clinician.

If we do the same analysis we get this:

For figure 1 you can see the trend easily, with the cost of loosing the overall structure. Alternatively figure 2 keeps the structure, but it comes with the cost of visual complexity. Area of circle is not my favorite way to express the base frequency, but it does a good job of showing which points are more important without interfering with the trend line. Also this figure is generalizable to more complex Venn Diagrams.

What do you think? We appreciate your constructive comments!

( If you have charts that was not mentioned in this article and would like to be acknowledged give us a comment. Also those who tacked the issue of sensitivity and specificity, I didn’t forget you. You will be mentioned in Venn Diagram Challenge Summary 2. …to be continued…)

(*1) Calculation of standard error with Bayesian correction is done as:

We had this entry almost a year ago. This year Hans Rosling gives yet another talk titled “New insights on poverty and life around the world”. The talk is great, and the ending is quite shocking.

In a follow-up to his now-legendary TED2006 presentation, Hans Rosling demonstrates how developing countries are pulling themselves out of poverty. He shows us the next generation of his Trendalyzer software — which analyzes and displays data in amazingly accessible ways, allowing people to see patterns previously hidden behind mountains of stats. (Ten days later, he announced a deal with Google to acquire the software.) He also demos Dollar Street, a program that lets you peer in the windows of typical families worldwide living at different income levels. Be sure to watch straight through to the (literally) jaw-dropping finale.

The software for video presentation is quite useful. You can see the outline of the talk and jump to the section that you are interested in by overlaying the cursor on top of it.

I found this interesting article on information software and interaction.

Here is the abstract:

The ubiquity of frustrating, unhelpful software interfaces has motivated decades of research into “Human-Computer Interaction.” In this paper, I suggest that the long-standing focus on “interaction” may be misguided. For a majority subset of software, called “information software,” I argue that interactivity is actually a curse for users and a crutch for designers, and users’ goals can be better satisfied through other means.

Information software design can be seen as the design of context-sensitive information graphics. I demonstrate the crucial role of information graphic design, and present three approaches to context-sensitivity, of which interactivity is the last resort. After discussing the cultural changes necessary for these design ideas to take root, I address their implementation. I outline a tool which may allow designers to create data-dependent graphics with no engineering assistance, and also outline a platform which may allow an unprecedented level of implicit context-sharing between independent programs. I conclude by asserting that the principles of information software design will become critical as technology improves.

It’s Edward Tufte applied to software and web design. Since we are seeing more information that are presented on the web such as this, it may be good to give a thought on how to deal with “interaction”.

I’m not a pie chart person. But here is an example where I don’t mind the use (I found it here):

Using a list of countries generated by The World Factbook database, flags of countries fetched from Wikipedia (as of 26th May 2007) are analysed by a custom made python script to calculate the proportions of colours on each of them. That is then translated on to a piechart using another python script. The proportions of colours on all unique flags are used to finally generate a piechart of proportions of colours for all the flags combined. (note: Colours making up less than 1% may not appear)

It’s pretty, it’s something about proportion, it’s not trying to show clear numeric result, data-to-ink/pixel ratio is not a problem in this case, yet there’s some information that you will have hard time seeing from table. (Such as Tunisia has slightly more white then Turkey.)

Now I’m not for the alphabetical ordering of the countries, but then again I don’t have a better suggestion.

Is there any reason that no country uses pink?
This site has summary of what not to do with pie chart.
You can also look at this paper.

Here is interactive visualization of Election & Public Opinion by PIIM. It’s an interactive display of Red / Blue state. Election data goes all the way back to 1789, the first presidential election.

This application will familiarize you with the voting process of the United States. Explore how public opinion and “creative democracy” has such a persuasive effect on the country; and how just a handful of votes may cause significant impact.
Historical background, the current voting process, and informative visualization of every major election are available. The Issue and policy tools permit some creative “What if” experiments in redrawing an election based on subtle alternations to historical outcomes.

A friend of mine introduced this site to me. It’s a database for information graphics that Parsons Institute for Information Mapping (PIIM) is building. They are accepting submissions so if you have interesting graphical display, take a shot to be in the “most comprehensive, manually annotated (and taxonomically classified) information graphics database in the world” which they are aiming for. I was not able to pull out 1200 graphics, but there are displays that I’ve never seen before. A list of key words might be helpful.