Emily Badger is a former staff writer at CityLab. Her work has previously appeared in Pacific Standard, GOOD, The Christian Science Monitor, and The New York Times. She lives in the Washington, D.C. area.
Researchers say looters make rational decisions about how far they’re willing to travel to steal what they want.
Shortly after the riots that spread across London in the summer of 2011, media outlets in the city began publishing maps trying to make sense of the event. They ran illustrations showing the sites of the worst rioting, as well as other maps cross-referencing the clusters of violence with the known home addresses – using court records – of people who’d been arrested in it.
At the time, these maps struck several researchers studying urban systems at University College London. “We thought, ‘this is a spatial system, and it looks a bit like something we have looked at before,’” says Toby Davies, one of the academics. He and his colleagues were picturing, more specifically, spatial models of how shoppers behave in search of retail. And this got them thinking. “It looks like retail," Davies says, "and retail is something we know we can model.” Why not try to mathematically model the movement of rioters?
Their research on this question, just published in the journal Scientific Reports, yields two curious insights: Rioters in search of retail to loot make rational decisions just like shoppers do about where to find the good stuff and how far they’re willing to travel to get there. And this means that the spatial layout of a city may be just as important as its social dynamics in explaining the rise and spread of riots.
Most research about London’s much-studied summer of 2011 has focused instead on the latter, on human behavior rather than urban space. “We’re encouraging people to think in an explicitly geographic way,” Davies says, “to really think about the places where these riots are taking place, to think about how rioters prioritize on that basis.”
Those 2011 riots were particularly characterized by massive looting, which makes the analogy to shopping particular apt. Any time you model a scenario where people have choices, Davies says, you have to first consider how attractive a given destination is in the perception of the shopper – or looter – considering a trip there. People are hindered by the cost of traveling, but they’re also lured longer distances by prime targets. You buy your milk from around the corner. But you might drive several miles to a mall that has both an Apple Store and a Brookstone. Rioters make very similar calculations.
“Places where there are more goods to loot, in this context – or more shopping opportunities in the non-criminal case – attract people,” Davies says. This is an obvious idea: Looters will congregate at retail hubs, and so you may want to pinpoint them on your police map. But Davies and his colleagues have also looked at the proximity of potential rioters to destinations that might draw them. They examined areas that rank poorly on the U.K.’s index of multiple deprivation (a much more complex measure than the U.S. poverty rate of a given community).
“We’re very careful to say that deprivation isn’t necessarily a cause of [rioting],” Davies says. “But there is a clear statistical relationship with deprivation. In more deprived areas, the rate of offending is higher.”
This means that if you have a commercial hub but the populations nearby aren’t particularly deprived, the likelihood of looting there is smaller. Likewise, a deprived community with no retail around looks in this mathematical model like a less likely source of rioting. In the model, all of this is also calibrated by one significant difference between looters and shoppers: looting can be contagious.
In its present state, Davies says, this model isn’t ready just yet to be deployed by police in a live scenario (it doesn’t take into account, for example, London’s transportation system). But the researchers hope to continue to refine it to where the system might be used in simulations by officers training or strategizing for how to respond to a future event. Such a tool could tell them where an initial outbreak of rioting might spread, the size it could reach, or even which neighborhoods deserve some preventive attention to head off future risk.
“One of the challenges that the police face in riots is that they are very rare events, so they don’t get much chance to practice on how they react to them,” Davies says. “If we can produce a way which simply simulates riots properly, then they can, as it were, ‘set them off’ in a controlled way and practice how they might respond to them.’
In this spatial system – as in the London riots in 2011 – the worst offenses take place at retail sites. But the model could be transposed to other locations. A soccer riot, for instance, might spill out into nearby sports bars instead of shopping centers. The underlying idea is the same: If you’re a police captain in the midst of such chaos, the spatial layout of your city will likely play a major role in determining what happens next.
Top image of riots in London in August of 2011. (Toby Melville/Reuters)