Data Visualisation: Beauty and Clarity?

This blog post focuses on an analysis of the donations made, by country, to assist the healing of Haiti after the devastating series of earthquakes in January 2010.  The process of tagging donations as specifically coming from organization, governments and countries is complex, and not as quick as one might expect. For example, the European Union has an emergency aid fund, but so do most of its members. So, for example, Germany might donate to the EU fund, and provide its own donation as well. This makes it difficult to track exactly where the generous donations are coming from, due to the actual route of the donation. Further, the type of donation can also make things more complex. For example, Canada citizens will have their donations matched by a donation by Canadian government – in addition to the funds that the kind-hearted Canadian government has already pledged as an emergency response to funds required immediately. This means that the actual full amount of donations is still to be determined since funds are still coming in from different sources within Canada in mind.
The data community is finding its own way of determining donation amounts. Jer Thorp uses the concept of an ‘Avatar minute’, is how much of the film that a viewer would be able to watch with a given amount of money. For example, 16 cents would purchase you about 3 minutes of the film, for example. So, Thorp’s analysis is broken down into two pieces: the first part aims to show each government’s donation is worth per head, and secondly, how many minutes of Avatar each citizen would be able to watch with the donation.  The ‘Avatar minute’ concept has been applied to visualizations of Haiti Earthquake donations at Jer Thorp’s website and if you haven’t had a look yet, I would recommend it. The images are incredibly beautiful, like this one below, which aims to show the amount of donations pledged by each country in ‘Avatar minutes’. All credit (and copyright) is given to Jer Thorp for this beautiful image:


Thorp’s image is undoubtedly beautiful, so the image met its objective as displaying data as a beautiful piece of art. Displaying information beautifully is emerging field, and Microsoft’s Pivot project is an example of this phenomenon.  Further, Stephen Few provides an analysis in a recent blog post.
As a data analyst, however, the visualisation raised more questions. For example, the imagery made it difficult to compare donations between countries. In the above example, the size of the letters seemed to indicate the donation amount pledged in ‘Avatar minutes’. However, the analysis didn’t seem to make it clear that Luxembourg, Denmark and Guyana made similar donations per head, partly because the word length and letter size made it difficult to compare between them easily. Comparison was made more difficult by the 3D imagery, which adds beauty but didn’t add clarity. As Stephen Few points out, 3D imagery can make data more difficult for viewers to decode. In the above image, it isn’t clear whether the length or width of the film reel has a relation with the donation amount, or whether it is underlying the headline point about the concept of ‘Avatar minutes’.
With these issues in mind, the data has been re-visualised here using Tableau in order to clarify underlying patterns in the data, and make comparison straightforward. With this in mind, I set out to use the same data, from two sources: ReliefWeb and the ever-excellent Guardian Data Source. Before we look at the visualization note that, with the aforementioned issues in mind, the underlying data set has been restricted to include indubitable funds from different countries at the time of writing. This has been done to focus the ‘business question’ of the graph, this excludes money from individuals, organizations, and money that hasn’t been tagged definitively as coming from a specific source yet. Further, this data from ReliefWeb was accurate as at 21st January, 2010, and it is clear that this situation regarding inbound donations is subject to rapid change. The sample dashboard can be found here, or a larger sample can be obtained by clicking here:
Overall Dashboard

One single unified dashboard, with a number of graphs showing different views of the data, can allow the viewer to compare and contrast patterns more easily. For example, the top graph undoubtedly shows that the United States have provided the most funds, and a larger image can be obtained by clicking here:
Top 10 Donations by Country
The sheer size of the US Government donation is further clarified by the right-hand image, which shows that the United States have provided 5 times the individual donations of France and the UK, which have provided second and third highest donations respectively.
Top 3 Donations
When population numbers are added to the mix, then some additional features of the data are highlighted. The following graph shows population as the thin blue line, and donation amount as a thicker blue background line. For example, it’s possible to see that, at the time of writing, Australia and Sweden have given proportionally more per head than the other countries, since their ‘donation’ line is longer than their ‘population’ line. A larger sample can be obtained by clicking here:

Haiti Total Donations by Country and Population
This is further quantified by the final section of the dashboard, which shows the amount of donation, per head, provided by each government. This was calculated simply as: [Donations in US Dollars]/[Population] The image is provided next:
Donor Ranking
The donation numbers have been included, and the proportion is also indicated by the strength of the blue colour; in other words, the stronger the blue colour, the higher the population.  This allows  the user the facility to look at the numbers, or just have an ‘at-a-glance’ view in order to obtain the ‘sense’ of the data. It’s possible to see that Australia, Sweden and Norway have given more then one dollar per head, at the time of writing (21st Jan, 2010).
To summarise, the initial data visualization was incredibly beautiful. However, the features that made it beautiful, also served to make the underlying data less clear. To conclude, in information visualization, there can be a payoff between beauty and clarity, but both domains can live side-by-side to serve different needs, and to learn from each other.

4 thoughts on “Data Visualisation: Beauty and Clarity?

  1. Hi Jen,

    Thanks for the critique. I agree with most of your points.

    The Haiti/Avatar images in my blog post are screenshots from an interactive tool in development and aren't necessarily meant to serve as truly useful infographics on their own.

    The actual tool lets you sort by various criteria, and investigate the numbers in more detail. Hopefully I'll get a chance to release it shortly.



  2. Thanks! It's fun to follow your thought process as to how to provide a more sophisticated look at data.

    I didn't find your visualizations to be intuitively decipherable. Perhaps this is due to the lack of labels and other clues about the meaning of the numbers and graphs.


    * What does Australia 1.1031 mean? I think it may be US$ per capita… If so, I think you should label the data with a US dollar sign and include the term, “per capita.” It's well understood and would clarify your narrative for your viewers.

    * What is the difference in meaning between the thin and thick bars? (The label, “value,” provides no assistance.) I think that one is US$ and the other is population. Labels would help.

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