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Open AccessArticle

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

1
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China, [email protected] (G.Z.)
2
School of Earth Sciences and Engineering; Hohai University; Nanjing 211000, China
3
Queen Mary University of London, London E1 4NS, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2156; https://doi.org/10.3390/s19092156
Received: 26 March 2019 / Revised: 27 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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 a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM. View Full-Text
Keywords: 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) 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)
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Zhang, G.; Rui, X.; Poslad, S.; Song, X.; Fan, Y.; Ma, Z. Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model. Sensors 2019, 19, 2156.

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