Detecting Damaged Regions after Natural Disasters using Mobile Phone Data: The Case of Ecuador

Abstract

In this work, we use mobile phone activity data to infer the affected zones in the Ecuadorian province of Manabí, after the 2016 earthquake, with epicenter in the same province. We calculate a series of features to train a classifier based on the K-Nearest Neighbors algorithm to detect affected zones with a 75% of precision. We compare our results with official reports published two months after the disaster.