The Thailand coup and some recent forecasting work

I blogged earlier at Predictive Heuristics about the Thailand coup and some forecasting work I’ve recently been part of:

This morning (East Coast time), the Thai military staged a coup against the caretaker government that had been in power for the past several weeks, after months of protests and political turmoil directed at the government of Yingluck Shinawatra, who herself had been ordered to resign on 7 May by the judiciary. This follows a military coup in 2006, and more than a dozen successful or attempted coups before then.

We predicted this event last month, in a report commissioned by the CIA-funded Political Instability Task ForceĀ (which we can’t quite share yet). In the report, we forecast irregular regime changes, which include coups but also successful protest campaigns and armed rebellions, for 168 countries around the world for the 6-month period from April to September 2014. Thailand was number 4 on our list, shown below alongside our top 20 forecasts. It was number 10 on Jay Ulfelder’s 2014 coup forecasts. So much for our inability to forecast (very rare) political events, and the irrelevance of what we do.

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Associating points with polygons in R

Some time ago I posted on how to find geographic coordinates given a list of village or city names in R. Somebody emailed me about how to do the reverse: the person had a list of villages in France along with the population in 2010, and wanted to find which administrative unit each village was located in. The problem boils down to associating points, the village coordinates, with polygons, the administrative division which they are a part of.

The village data look like this:


munic <- read.xls("France-Population.xlsx")
                  Name       long      lat pop_2010
1                 Aast -0.0887339 43.28919 182.5416
2           Abainville  5.4947440 48.53057 327.2407
3            Abancourt  1.7649060 49.69672 687.2479
4            Abancourt  3.2127010 50.23528 448.1252
5            Abaucourt  6.2579230 48.89637 285.9438
6 Abaucourt-Hautecourt  5.5405000 49.19700  93.0353

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Quick lookup for country codes

After more than half a decade at this, it has finally dawned upon me that instead of downloading the Correlates of War state system membership table, or the Gleditsch and Ward refinement of it, every time I wonder what country “338″ is, it might be easier to upload them to Google:

COW codes and state system membership

G&W codes and state system membership

And, for the sake of self-promoting completeness, code to produce panel data reflecting COW or G&W state system membership, and old Stata code to change country names to COW codes.


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