Article2018

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

Jie Chen · Tao Pei · Shih‐Lung Shaw · Feng Lu · Mingxiao Li · Shifen Cheng +2 more

74 citations0/0 files storedInternational Journal of Geographical Information Systems

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Human Mobility and Location-Based Analysis Traffic Prediction and Management Techniques demographic modeling and climate adaptation

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.

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References

2008Understanding individual human mobility patterns 2014Dynamic population mapping using mobile phone data 2006Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis 2015Social Sensing: A New Approach to Understanding Our Socioeconomic Environments 2016Annals of the American association of geographers 2012Understanding individual mobility patterns from urban sensing data: A mobile phone trace example 2009Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network 2009Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data

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