Predicting the distribution of informal camps established by the displaced after a catastrophic disaster, Port-au-Prince, Haiti

In January, 2010 an earthquake struck Haiti near its capital of Port-au-Prince, causing possibly the largest urban natural disaster in modern times. Within a week of the earthquake, hundreds of informal camps were erected across Port-au-Prince by persons displaced by the earthquake, termed internally displaced persons (IDPs). This paper attempts to determine the extent to which the geographic distribution of these IDP camps can be explained using geographic factors such as topography, population density, and availability of open space. A logistic regression model revealed that the three factors most predictive of IDP camp distribution were distance from the international airport, distance from the city center, and elevation. Together with five other significant variables, the logistic model predicted the presence of IDP camps in a 50-m-cell grid across the study area with up to 70% accuracy. Further statistical analysis explained roughly 35% of variance in IDP camp size, though these results were difficult to interpret. The resulting method and predictive maps are promising in their ability to inform natural disaster managers when preparing for extensive displacement, evacuation, or sustenance of an urban population following a natural disaster. These methods can be used to improve estimates of risk and social vulnerability to natural hazards. However, more research is needed to validate the methods for other locations and natural disasters.

► We examine informal camps constructed in Port-au-Prince after the 2010 catastrophic earthquake.
► Using logistic regression, we predict camp location with an accuracy of 70% with validation data.
► Camps were more likely to be located closer to the city center and airport, and on higher ground.
► We related the same variables to camp size and explained 35% of variance in camp area.
► Damage concentration and distance from major roads were the strongest explanatory variables.