Fine-grained prediction of urban population using mobile phone location data

ABSTRACT

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.

KEYWORDS:

• Urban population
• fine-grained prediction
• mobile phone data
• Shanghai

Acknowledgments

The authors thank Dr. May Yuan, Dr. David O’Sullivan, and the anonymous reviewers for their insightful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was jointly supported by the National Key Research and Development Program of China [2017YFB0503604], the National Natural Science Foundation of China [41571431, 41525004, 41421001, 41231171], the Arts and Sciences Excellence Professorship, and the Alvin and Sally Beaman Professorship at the University of Tennessee.