MOOC Weeks 5-6: UK Aid to India

For our last assignment of the MOOC, Alberto Cairo decided to give us enough rope to hang ourselves: "do whatever you want". I proceeded to swiftly spend half the allocated time deciding on a topic. Returning to aid, the subject of week 3, was a natural fit and I knew the data would be available. After considering a few generic variations on the themes "where does aid come from" and "where does aid go", I realized I needed an angle. The recent announcement by the UK that they are cutting their aid to India seemed intriguing enough and calling for some data. Then, I set as my goal to create one of these long, vertical infographic, but without resorting to some of the misleading and unhelpful techniques that plagues too may of them. Let's recap some of the lessons of the first four weeks.

  1. Look for a story in the data.
  2. Convey a narrative.
  3. Use good copy to draw the reader in.
  4. Combine several graphs.
  5. Present the same data in different ways.
  6. Use the appropriate graph for the data.
  7. Pick the color scheme carefully.
  8. Label and include legends.

Here is the result.

UK Aid to India. Francis Gagnon

UK Aid to India. Francis Gagnon

The story is that it is a big deal that the UK will cut its aid to India and there are many ways to understand the causes and consequences. It is a delicate topic and I did not want to turn the infographic into an editorial. It is rather designed to help the reader think about the issue and maybe open a few new perspectives, especially since some of the actors have strong opinions about this shift.

It starts by showing the reader how important this decision is: India is a top recipient of UK aid. Then it goes into a comparison of the two countries, to reflect on their relative economic health. This leads into an exploration of poverty in India and finally an overture towards the other potential beneficiaries of this change, showing this policy decision into a larger context. The sources are also an important aspect of an infographic and I wanted to provide them in a clear way to support the credibility of the data above.

This has taken much longer than anticipated. Dataviz nerds, look for a making-of in the coming days.

Week 3: Aid Transparency

The third week's assignment was right up my alley: aid transparency. It is even more disappointing then that I was not able to complete something worthy. The source data comes from the Transparency Index of Publish What You Fund and takes the form of a ranking of aid agencies according to their transparency score. I expected the students to visualize this ranking, making more apparent the comparison between agencies, highlighting their strengths and weaknesses. I decided to try something different by visualizing the indicators themselves. I thought that it could be a nice way of explaining data transparency by detailing how it is measured. Here is my entry.

Aid Transparency Graph FG

The assignment was for an interactive visualization, so the image below shows some of the interactivity that could be prompted by the users, namely a series of definitions and the capacity to select a subset of indicators.

Aid Transparency Graph FG2

Using Adobe InDesign, means drawing every data point and this took way longer than it should, mainly because it does not add so much to the graph to have very precise data. Most people just give a quick look and are mostly interested in the ways in which the data is visualized, more than the result of the visualization. This point was driven home by the multiple hand drawn sketches of fellow students that did not approach accuracy, but that sometimes conveyed clearly enough their concept. The next week, I wouldn't be caught.

Given the call for a narrative, I spent some time finding and writing some analysis. Again, this is not something that I expect anyone to read -- at least, no one has ever commented on the text -- so it does not seem like a good investment of time.

Aid Transparency Graph FG3

In general, I like to use colors to visually group things, but more than once my audience has been more interested to see things grouped by subcategories, so that's what I have done here with the three categories of transparency. See how the colors are grouped. I have to say that it worked better than I had anticipated.

Aid Transparency Graph FG32

This slide shows only the improvement of each indicator. The main message is that all indicators have improved over the last year, although some much more than others.

In the end, I did not get to produce something of the quality I was hoping for. I picked my colors at random, I did not include a legend, I did not push the analysis, etc. But the week was over and another assignment was waiting. The point is not so much to create a perfect infographic, but to learn and this goal was already achieved.