Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using...

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Bibliographic Details
Main Authors: Guangyuan Zhang, Xiaoping Rui, Stefan Poslad, Xianfeng Song, Yonglei Fan, Zixiang Ma
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Sensors
Subjects:
population density
deep learning
mobile phone data
spatial–temporal data analysis and prediction
kernel density estimation (KDE)
long short-term memory (LSTM)
convolution LSTM (ConvLSTM)
autoregressive moving average (ARMA)
Online Access:https://www.mdpi.com/1424-8220/19/9/2156

Internet

https://www.mdpi.com/1424-8220/19/9/2156

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