Predictability of population displacement after the 2010 Haiti earthquake

Abstract

Most severe disasters cause large population movements. These movements make it difficult for relief organizations to efficiently reach people in need. Understanding and predicting the locations of affected people during disasters is key to effective humanitarian relief operations and to long-term societal reconstruction. We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, to 341 d after the devastating Haiti earthquake of January 12, 2010. Nineteen days after the earthquake, population movements had caused the population of the capital Port-au-Prince to decrease by an estimated 23%. Both the travel distances and size of people's movement trajectories grew after the earthquake. These findings, in combination with the disorder that was present after the disaster, suggest that people's movements would have become less predictable. Instead, the predictability of people's trajectories remained high and even increased slightly during the three-month period after the earthquake. Moreover, the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds. For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought.

Fig. 1.

Overview of population movements. (A) Shows the geography of Haiti, with distances from PaP marked. The epicenter of the earthquake is marked by a cross. (B) Gives the proportion of individuals who traveled more than d km between day t - 1 and t. Distances are calculated by comparing the person’s current location with his or her latest observed location. In (C), we graph the change in the number of individuals in the various provinces in Haiti. (D) Gives a cumulative probability distribution of the daily travel distances d for people in PaP at the time of the earthquake. (E) Shows the cumulative probability distribution of d for people outside PaP at the time of the earthquake. Finally, (F) gives the exponent α of the power-law dependence of d—the probability of d is proportional to d^-α. These are obtained by a maximum-likelihood method (33), and differ from the slopes of the lines in (D) and (E) by unity since these are the cumulative distributions.

Fig. 2.

Trajectory analysis of mobile phone users who were present in PaP on the day of the earthquake. (A) Shows the cumulative distribution of the radius of gyration: r_g. (B) Displays the distribution of entropy S. (C) Gives the distribution of the maximal predictability Π. (D) Shows the correlation between radius of gyration r_g and Π. In (E), we graph the fraction of time a person spent in the top n visited communes . (F) gives the averaged R/R^rand versus the radius of gyration r_g, showing a relative stable dependence.

Fig. 3.

Analysis of population movements out of PaP. (A) Shows the distribution of PaP residents moving out of PaP for the first time by t days after the day of the earthquake. In (B), we plot the maximum distance to the center of PaP traveled by PaP residents. Reference curves represent sample periods from June 1, 8, 15, 22, and 29 to 170 d after these dates. (C) Gives the cumulative distribution of people’s relative distances on January 3 and 31 to their locations on the day of the earthquake for four different categories of people. (D) Gives the distribution of distance to the center of PaP for individuals present in PaP on the sampled day and outside PaP 19 d later. Results for the period after February 9 are averaged for clarity.