Research presented by Linus Bengsston’s Flowminder Foundation to the recent Netmob 2013 held at MIT’s Media Lab showed that our movements after conflicts and disasters are highly predictable. Analysis of mobile phone data from the 2011 civil war in Cote D’Ivoire (CIV), showed that population movements were up to 88% predictable, an accuracy that was consistent with data collected after the 2010 earthquake in Haiti. In fact, we become more, rather than less, predictable in crises.
You Can Run For Your Life, But You Can’t Hide
Major disasters and conflicts can displace whole populations of people, yet we have no way track these movements with any accuracy. This makes it difficult for relief agencies to respond with water, food, shelter, sanitation, and medical aid in a timely way. Timeliness is key, because long-term effects can be more catastrophic than the original cause of displacement. For instance, up to 50 times more people die in cholera outbreaks among displaced populations, and once infected, populations travel with their diseases, potentially infecting new areas.
“NGOs make guesstimates. You get reports from some city saying ‘We estimate that we have received this many people,’” says Bengtsson. Hundreds of thousands of people fled in panic from the Haitian capital Port-au-Prince after the 2010 earthquake. Where did they all go? Bengtsson tracked their flight via mobile phone data. “Nobody had thought about using this kind of data for this purpose before,” he says. The Haitian National Civil Protection Agency (NCPA) estimated population movements after the earthquake by counting ship and bus traffic, information they distributed to relief agencies. These data provided an accurate count of the number of people moving, but not where they went. For instance, the NCPA’s estimates for the number of people moving to the Departments of Sud (South) and Ouest (West), were less than a third of the actual figures.
With mobile phone location data from 1.9 million SIM cards, Bengtsson and his research team at Flowminder Foundation compared 42 days of pre-earthquake data against 158 days of data after the event. Their analysis included calculations for the radius of gyration (a measure of the size of trajectories) to estimate how much a subscriber moved, and entropy measures to define the disorder and predictability of an individual’s movements.
“We were surprised at the regularity of people’s movements, which we expected would be much more chaotic and unpredictable. The patterns of movement were quite similar to usual times, but the absolute levels of people’s movements were much larger. People were just as predictable as before, but they moved more. People go to places where they have their social support structures,” says Bengtsson.
In fact, people leaving the city mostly went to the same places they had visited for Christmas and New Year’s where they were likely to have friends and family. The geographical distribution of the population obtained from SIM movements closely matched that reported by a United Nations Population Fund household survey performed several months after the earthquake.
One limitation of mobile phone monitoring is that the most vulnerable groups, such as pregnant women, the elderly, and the poorest people are least likely to have phones. “Children and elderly people are often not alone, so they are usually moving with phones, but still there will be fewer phones per person than on average. But that’s a long research process going forward,” says Bengtsson.