EVEN BETTER Animated Map of Coup Attempts Worldwide, 1946-2013

[Click here to go straight to the map]

A week ago, I posted an animated map of coup attempts worldwide since 1946 (here). Unfortunately, those maps were built from a country-year data set, so we couldn’t see multiple attempts within a single country over the course of a year. As it happens, though, the lists of coup attempts on which that animation was based does specify the dates of those events. So why toss out all that information?

To get a sharper picture of the distribution of coup attempts across space and time, I rebuilt my mashed-up list of coup attempts from the original sources (Powell & Thyne and Marshall), but now with the dates included. Where only a month was given, I pegged the event to the first day of that month. To avoid double-counting, I then deleted events that appeared to be duplicates (same outcome in the same country within a single week). Finally, to get the animation in CartoDB to give a proper sense of elapsed time, I embedded the results in a larger data frame of all dates over the 68-year period observed. You can find the daily data on my Google Drive (here).

WordPress won’t seem to let me embed the results of my mapping directly in this post, but you can see and interact with the results at CartoDB (here). I think this version shows more clearly how much the rate of coup attempts has slowed in the past couple of decades, and it still does a good job of showing change over time in the geographic distribution of these events.

The two things I can’t figure out how to do so far are 1) to use color to differentiate between successful and failed attempts and 2) to show the year or month and year in the visualization so we know where we are in time. For differentiating by outcome, there’s a variable in the data set that does this, but it looks like the current implementation of the Torque option in CartoDB won’t let me show multiple layers or differentiate between the events by type. On showing the date, I have no clue. If anyone knows how to do either of these things, please let me know.

Playing Telephone with Data Science

You know the telephone game, where a bunch of people sit in a circle or around a table and pass a whispered sentence from person to person until it comes back to the one who started it and they say the version they heard out loud and you all crack up at how garbled it got?

Well, I wonder if John Beieler is cracking up or crying right now, because the same thing is happening with a visualization he created using data from the recently released Global Dataset on Events, Language, and Tone, a.k.a. GDELT.

Back at the end of July, John posted a terrific animated set of maps of protest activity worldwide since 1979. In a previous post on a single slice of the data used in that animation, John was careful to attach a number of caveats to the work: the maps only include events covered in the sources GDELT scours, GDELT sometimes codes events that didn’t happen, GDELT sometimes struggles to put events in their proper geographic location, event labels in the CAMEO event classification scheme GDELT uses doesn’t always mean what you think they mean, counts of events don’t tell you anything about the size or duration of the events being counted, etc., etc.  In the blogged cover letter for the animated series, John added one more very important caveat about the apparent increase in the incidence of protest activity over time:

When dealing with the time-series of data, however, one additional, and very important, point also applies. The number of events recorded in GDELT grows exponentially over time, as noted in the paper introducing the dataset. This means that over time there appears to be a steady increase in events, but this should not be mistaken as a rise in the actual amount of behavior X (protest behavior in this case). Instead, due to changes in reporting and the digital recording of news stories, it is simply the case that there are more events of every type over time. In some preliminary work that is not yet publicly released, protest behavior seems to remain relatively constant over time as a percentage of the total number of events. This means that while there was an explosion of protest activity in the Middle East, and elsewhere, during the past few years, identifying visible patterns is a tricky endeavor due to the nature of the underlying data.

John's post deservedly caught the eye of J. Dana Stuster, an assistant editor at Foreign Policy, who wrote a bit about it last week. Stuster's piece was careful to repeat many of John's caveats, but the headline---"Mapped: Every Protest on the Planet since 1979"---got sloppy, essentially shedding several of the most important qualifiers. As John had taken pains to note, what we see in the maps is not all that there is, and some of what's shown in the maps didn't really happen.

Well, you can probably where this is going.  Not long after that Foreign Policy piece appeared, I saw this tweet from Chelsea Clinton:

In fewer than 140 characters, Clinton impressively managed to put back the caveat Foreign Policy had dropped in its headline about press coverage vs. reality, but the message had already been garbled, and now it was going viral. Fast forward to this past weekend, when the phrase "Watch a Jaw-dropping Visualization of Every Protest since 1979" made repeated appearances in my Twitter timeline. This next iteration came from Ultraculture blogger Jason Louv, and it included this bit:

Also fruitful: Comparing this data with media coverage and treatment of protest. Why is it easy to think of the 1960s and 70s as a time of dissent and our time as a more ordered, controlled and conformist period when the data so clearly shows that there is no comparison in how much protest there is now compared to then? Media distortion much?

So now we get a version that ignores both the caveat about GDELT's coverage not being exhaustive or perfect and the related one about the apparent increase in protest volume over time being at least in part an artifact of "changes in reporting and the digital recording of news stories." What started out as a simple proof-of-concept exercise---"The areas that are 'bright' are those that would generally be expected to be so," John wrote in his initial post---had been twisted into a definitive visual record of protest activity around the world in the past 35 years.

As someone who thinks that GDELT is an analytical gusher and believes that it's useful and important to make work like this accessible to broader audiences, I don't know what to learn from this example. John was as careful as could be, but the work still mutated as it spread. How do you prevent this from happening, or at least mitigate the damage when it does?

If anyone's got some ideas, I'd love to hear them.

Coup Risk in 2013, Mapped My Way

This blog’s gotten a lot more traffic than usual since yesterday, when Max Fisher of the Washington Post called out my 2013 coup forecasts in a post on WorldViews.

I’m grateful for the attention Max has drawn to my work, but if it had been up to me, I would have done the mapping a little differently. As I said to Max in an email from which he later excerpted, the forecasts simply aren’t sharp enough to parse the world as finely as their map did. Our theories of what causes coup attempts are too fuzzy and our measures of the things in those theories are too spotty to estimate the probability of these rare events with that much precision.

But, hey, I’m a data guy. I don’t have to stick to grumbling about the Post‘s map; I can make my own! So…

The map below sorts the countries of the world into three groups based on their relative coup risk for 2013: highest (red), moderate (orange), and lowest (beige). I emphasize “relative” because coup attempts are very rare, so the estimated risk of coup attempts in any given country in any single year is pretty small. For example, Guinea-Bissau tops my list for 2013, and the estimated probability of at least one coup attempt occurring there this year is only 25%. Most countries worldwide are under 2%.

Consistent with an emphasis on relative risk, the categories I’ve mapped are based on rank order, not predicted probability. The riskiest fifth of the world (33 countries) makes up the “highest” group, the second fifth the “moderate” group, and the bottom three-fifths the “lowest” group.

This forecasting process doesn’t have enough of track record for me to say exactly how those categories relate to real-world risk, but based on my experience working with similar data and models, I would expect roughly four of every five coup attempts to occur in countries identified here as high risk, and the occasional “miss” to come from the moderate-risk set. Only very rarely should coup attempts come from the 100 or so countries in the low-risk group.

coup_risk_map_2013

FTR, this map was made in R using the ‘rworldmap‘ package.

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