In the process working on time series data for my dissertation, I recently came across an excellent utility for data visualization called CartoDB. Besides being an excellent mapping tool, it is particularly helpful for people less familiar with GIS technologies but nonetheless want to see their data spatially.
As my dissertation relates to leadership change in non-democratic regimes, succession events are a good candidate for taking this visualization tool for a spin. So I pared down the dataset I’m working on to a few key variables to create a dynamic view of leadership change across the world and over time. I should also note that I did have to import geocodings for the countries in my data (specifically their capitals) as this was not a piece of information I had previously. This was a pretty simple task thanks to an excellent geocoding database called “Countries of the World” The map I created is below and depicts all leadership changes (via Goemans, Gleditsch, & Chiozza’s ARCHIGOS dataset) in non-democratic regimes since 1946 (from Milan Svolik’s data from his excellent book on authoritarianism).
You can go HERE for the direct link to the visualization.
I have other variables in the table that differentiate between types of succession (regular, irregular, natural death of the incumbent), but for now this visualization gives a good sense of the density of leadership change in authoritarian regimes across the globe. It would be nice to distinguish between these different types of succession the map, but as Jay Ulfelder notes in his visualization of coups d’etat, it doesn’t seem possible yet on CartoDB’s Torque (time series) option.
There are a bunch of other visualizations I’d like to do using CartoDB given the efficiency of using this cloud mapping utility and its high-quality products. John Beieler has mapped protests from GDELT data which received a lot of media attention, and there’s a lot more to visualize with that data at the country-level going forward.