Just to show how easy it can be to create a relatively complicated visualization, I’ve taken one of the examples presented in the showcase, and changed the data set. As the code is still being developed, I had to hack some of the code to work with the new data, but in the future I’m sure it will become much easier. Anyway, onto the example.
Dorling cartograms vary the size of a geographical region depending on a measure. They work well when you want to show a time history of regional variations in the measure. Why are they better than a just series of sparklines or a choropleth? Firstly, there may be trends larger than a state that add understanding to the data – for example, does a trend start in the southeast, and spread to other parts of the county? This would be hard to see on state-based line graphs.
Choropleths, where each geographical region is shaded to encode the measure can work well when animated to show the spread of a trend, but small geographical areas (I’m looking at you Rhode Island) can be almost impossible to read. This is where the Dorling cartogram comes into it’s own with some datasets. It can still suffer from smaller geographical regions being hard to see, or large ones dominating the view, depending on the variation in your measure. They also suffer in that same way that pie charts do – humans find it difficult to accurately compare 2D areas – but they do work very well on giving you a sense of how the data flow and regions change over time.
I’ve used the unemployment rates from 1980 to 2009 to produce this Dorling cartogram. Let the map settle for a bit – it may take up to 20s on slow computers/browsers. and then click the play button. I think it shows the ebb and flow of unemployment well over the three decades very well. If it doesn’t show, chances are, it’s your browser (Firefox sometimes has problems, Chrome seems fine, IE8 and before won’t work). If you have a non-compliant browser , I’ve embedded a video of the animation.
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