Two things to talk about in this post – I continue my ramblings about the online viz tool Many Eyes, and discuss how normalizing data can provide radically different insights into data.
The data set I’m using is the donations by countries (government and corporations, but not private) to earthquake relief in Haiti. I’ve seen a few charts around this showing how the US has provided the most funding, but when normalized per capita, Canada and other countries stand out. On a purely data level, and not to denigrate any country’s assistance, is this normalization appropriate when the donation sums do not include donations by the public?
Instead it may be more appropriate to normalize by gross domestic product, especially as governments and corporations greatly influence GDP. However, even that’s not straightforward as GDP is affected by exchange rate and does not reflect purchasing power within a country. So we could also normalize based on GDP expressed as purchasing power parity, where differences in cost of living are accounted for.
This yields grossly different results, as shown below. I’ve made all of the bars in each series relative to the largest in that series. Guyana’s contribution of a million dollars is massive compared to its GDP expressed in either way, dwarfing other countries’ equivalent contributions. This shows that normalization, which is often appropriate, should be chosen carefully and assessed fully when interpreting charts.
Now onto Many Eyes – I’ve kept the visualization local again, so apologies for those reading without java. I like the result – it was certainly quick to produce and you can play around a little with it. It’s not perfect though – to get appropriate height bars meant messing around – I couldn’t get a scale to appear on the y-axis. For simple data, especially text based, I think Many Eyes excels, but this would have worked better in Tableau Public.
What I would love to have done would be to have shown this information as cartograms where the area a country occupies on the map is relative to the data value, not land mass. This would have added a visual geographical spin on where donations were coming from, especially for those of us who may have forgotten where Guyana is exactly..