Inferring and Modeling Migration Flows Using Mobile Phone Network Data

• Main Authors: Soranan Hankaew, Santi Phithakkitnukoon, Merkebe Getachew Demissie, Lina Kattan, Zbigniew Smoreda, Carlo Ratti
• Format: Journal
• Published: 2020
• Subjects: Computer Science; Engineering; Materials Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075789712&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/67763
• Institution: Chiang Mai University

© 2013 IEEE. Estimating migration flows and forecasting future trends is important, both to understand the causes and effects of migration and to implement policies directed at supplying particular services. Over the years, less research has been done on modeling migration flows than the efforts allocated to modeling other flow types, for instance, commute. Limited data availability has been one of the major impediments for empirical analyses and for theoretical advances in the modeling of migration flows. As a migration trip takes place much less frequent compared to the commute, it requires a longitudinal set of data for the analysis. This study makes use a massive mobile phone network data to infer migration trips and their distribution. Insightful characteristics of the inferred migration trips are revealed, such as intra/inter-district migration flows, migration distance distribution, and origin-destination (O-D) movements. For migration trip distribution modelling, log-linear model, traditional gravity model, and recently introduced radiation model were examined with different approaches taken in defining parameters for each model. As the result, the gravity and log-linear models with a direct distance (displacement) used as its travel cost and district centroids used as the reference points perform best among the other alternative models. A radiation model that considers district population performs best among the radiation models, but worse than that of the gravity and log-linear models.