Terrorist attacks are different in many ways: they take place in different countries, with different motivations behind them, using different mechanisms, and with varying degrees of success. But are there any commonalities that could be used, for example, to categorize them and so to defend against them in more focused ways? The answer is yes, there are large-scale similarities.
To do this analysis, I started from the Global Terrorism Database developed by START, the National Consortium for the Study of Terrorism and Responses to Terrorism. The database contains details of all incidents that meet their coding standards since the beginning of 1970, and I used the version released at the end of 2012. There was one major discontinuity where new fields were added but overall the coding has been consistent over the entire 40+ year period.
The image below shows the clustering of all attacks over that time period:
The large structure looks like a hinge with clusters A and B at the top, clusters C and D forming the hinge itself, and clusters E, F, G, and H at the bottom. There’s also a distinction between the clusters at the front (B, D, F, and H) and those at the back (A,C,E, and G). (You’ll have to expand the figure to see the labels clearly.)
The first thing to notice is that there are only 8 clusters and, with the exception of H which is quite diffuse, they clusters are fairly well defined. In other words, there are 8 distinctive kinds of terrorist attack (and only 8, over a very long time period).
Let’s dig into these clusters and see what they represent. The distinction between the front and the back is almost entirely related to issues of attribution: whether the attack was claimed, how clear that claim is (for example, are there multiple claim of responsibility for the same incident), and whether the incident should be properly claimed as terrorism or something else (quasi-military, for example).
The structure of the hinge differentiates between incidents involving capturing people (hijackings or kidnappings in A and B) and incidents that are better characterized as attacks (C, D, E, F, G, H). The extremal ends of A and B (to the right) are incidents that lasted longer and/or the ransom was larger.
The differences between C/D, E/F, and G/H arise from the number of targets (which seems to be highly correlated with the number of different nationalities involved). So C and D are attacks on a single target, E and F are attacks on two targets, and G and H are attacks on three targets. Part of the diffuse structure of H happens because claims are always murkier for more complex attacks and part because there is a small group of incidents involving 4 targets that appears, as you’d expect, even further down and to the right.
Here are some interesting figures which overlay the intensity of a property on the clustering, so that you can see how it’s associated with the clusters:
This figure shows whether the incident was claimed or not. The color coding runs from dark red to bright yellow; I’m not specifying the direction, because it’s complicated, but the contrast shows differences. In each case, the available color spectrum is mapped to the range of values.
This figure shows the differences between incidents where there were some hostages or kidnapped and those where there weren’t.
This figure shows that the country in which the incident took place is mostly unrelated to other properties of the incident; in other words, attacks are similar no matter where they take place.
This analysis shows that, despite human variability, those designing terrorist incidents choose from a fairly small repertoire of possibilities. That’s not to say that there couldn’t be attacks in which some people are also taken hostage; rather that those doing the planning don’t seem to conceptualize incidents that way, so when it happens it’s more or less by accident. Perhaps some kind of Occam’s razor plays a role: planning an incident is already difficult so there isn’t a lot of brainpower to try for extra cleverness, and there’s probably also a perception that complexity increases risk.
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