Detecting Mobility Patterns in Mobile Phone Data from the Ivory Coast

This paper investigates the Data for Development (D4D) challenge [3], an open challenge set by the French mobile phone company, Orange, who have provided anonymized records of their customers in the Ivory Coast. This data spans a 5 month (150 day) horizon spread across 4 different sets containing antenna-to-antenna traffic, trace data for 50,000 customers at varying spatial resolution, and social graphs for 5,000 customers. By leveraging cloud-based and open-source analytics infrastructure to (1) merge the D4D datasets with Geographic Information System (GIS) data and (2) apply data mining algorithms, this paper presents a number of techniques for detecting mobility patterns of Orange customers in the Ivory Coast. By applying a k-medoid clustering algorithm to the antenna locations and their average distance to nearby antennas, we show how the high spatial resolution mobile phone dataset reveals a number of daily mobility patterns and properties, including trends in week-day versus weekend, public holiday mobility behavior, and distributional properties of daily trip distances across each cluster. With a view towards providing tools to assist with transport infrastructure planning, we combine the high spatial resolution D4D dataset with GIS data for transport infrastructure and demonstrate an approach for detecting whether a mobile phone user is traveling on a segment of transport infrastructure. This work culminates in a preliminary cloud-based GIS tool for visualizing mobility traces