This post is about the Archigos data, which you can find here.
Political scientists, and maybe historians as well, are familiar with coups, rebellions, and mass protests as distinct phenomena that lead to the fall of regimes occasionally. Another way to view these events is from the perspective of state leaders, and how these events affect transitions between political leaders. Selectorate theory does this by considering the sets of people within a regime that a leader must rely on to remain in power, and how their relative sizes shape behavior. We do this empirically by modeling irregular leadership changes, where we draw our dependent variable from the Archigos dataset. I’ve been vaguely aware of these data for a while, but honestly did not understand well how useful they could be. In this post I’ll try to give a quick overview of the data.
Archigos is a dataset of the political leaders of states from 1875 on collected by Hein Goemans, Kristian Skrede Gleditsch, and Giacomo Chiozza. The most recent version, 2.9, has more than 3,000 leaders through 2004 and an update to 2014 is in the works. Aside from identifying leaders and when they gained and lost office, it codes how they did so (from the Archigos codebook):
Archigos codes the manner in which transfers between rulers occur. Our main interest is whether transfers of power between leaders take place in a regular or irregular fashion. We code transfers as regular or irregular depending on the political institutions and selection mechanisms in place. We identify whether leaders are selected into and leave political office in a manner prescribed by either explicit rules or established conventions. In a democracy, a leader may come to power through direct election or establishing a sufficient coalition of representatives in the legislature. Although leaders may not be elected or selected in particularly competitive processes, many autocracies have similar implicit or explicit rules for transfers of executive power. Leader changes that occur through designation by an outgoing leader, hereditary succession in a monarchy, and appointment by the central committee of a ruling party would all be considered regular transfers of power from one leader to another in an autocratic regime.
My limited knowledge of what happens in Terminal, and thus by extension shell, is mostly driven by PostgreSQL/PostGID/rgdal/RPostgreSQL install errors. In the latest variant of this,
rgdal throws the following error when attempting to build from source:
checking PROJ.4: epsg found and readable... no Error: proj/epsg not found Either install missing proj support files, for example the proj-nad and proj-epsg RPMs on systems using RPMs, or if installed but not autodetected, set PROJ_LIB to the correct path, and if need be use the --with-proj-share=configure argument.
I have to build from source by the way because the default
rgdal package for Mac does not include a PostgreSQL driver, meaning I have to build it against another version of GDAL that does. This was another fun thing to discover, but at least is easy to diagnose by checking whether
PostgreSQL shows up when you run
ogrDrivers() in R. Anyways, as far as I can tell the problem was that I installed
proj via homebrew, a package manager for OS X. As a result although
rgdal could find the
proj binary via a symlink, it could not find the
epsg and related data files that were in a little dark corner by themselves. The solution was to build the package with an option providing the file location manually:
install.packages("rgdal", type = "source", configure.args="--with-proj-share=/usr/local/Cellar/proj/4.8.0/share/proj")
This is I guess exactly what the install error message told me to do.
or, How I learned to stop worrying and love event data. (This post first appeared on Predictive Heuristics)
Nobody in their right mind would think that the chances of civil war in Denmark and Mauritania are the same. One is a well-established democracy with a GDP of $38,000 per person and which ranks in the top 10 by Human Development Index (HDI), while the other is a fledgling republic in which the current President gained power through a military coup, with a GDP of $2,000 per person and near the bottom of the HDI rankings. A lot of existing models of civil war do a good job at separating such countries on the basis of structural factors like those in this example: regime type, wealth, ethnic diversity, military spending. Ditto for similar structural models of other expressions of political conflict, like coups and insurgencies. What they fail to do well is to predict the timing of civil wars, insurgencies, etc. in places like Mauritania that we know are at risk because of their structural characteristics. And this gets worse as you leave the conventional country-year paradigm and try to predict over shorter time periods.
The reason for this is obvious when you consider the underlying variance structure. First, to predict something that changes, say dissident-government conflict, the nature of relationships between political parties, or political conflict, you need predictors that change.
It’s always easier to pick up new things like this with a strong motivating example, and for me it was visualizing the distribution of finish times in the SEB Tallinn Marathon in Estonia last weekend. My wife and I both ran and completed our first marathons, and one can look up the finish times and some other information on the event website. However, there was a post in the New York Times a few months ago that had a plot of the distribution of marathon times and which had spikes around the half hour marks as runners pushed themselves to meet arbitrary goals. So I was curious what the distribution of finish times was for the Tallinn Marathon. Along the way, it would also be nice to see where you fall in the distribution, and, since it is maybe not fair to lump all runners into one category, to do so by age and gender groups. Instead of producing dozens of separate plots in R, this seems like a candidate for something interactive, and hence Shiny. You can find the interactive results here, and they look like this:
This first appeared on Predictive Heuristics, my employer’s blog.
Improvised explosive devices, or IEDs, were extensively used during the US wars in Iraq and Afghanistan, causing half of all US and coalition casualties despite increasingly sophisticated countermeasures. Although both of these wars have come to a close, it is unlikely that the threat of IEDs will disappear. If anything, their success implies that US and European forces are more likely to face them in similar future conflicts. As a result there is value in understanding the process by which they are employed, and being able to predict where and when they will be used. This is a goal we have been working on for some time now as part of a project funded by the Office of Naval Research, using SIGACT event data on IEDs and other forms of violence in Afghanistan.
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 Force (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.
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:
library(foreign) library(gdata) library(sp) munic <- read.xls("France-Population.xlsx") head(munic)
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