{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T23:41:47Z","timestamp":1768693307951,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:00:00Z","timestamp":1557360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0503600"],"award-info":[{"award-number":["2017YFB0503600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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\u2019 spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people\u2019s mobility derived from the mobile phone users\u2019 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.<\/jats:p>","DOI":"10.3390\/s19092156","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"2156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users\u2019 Distributions Based upon a Convolution Long Short-Term Model"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5028-2291","authenticated-orcid":false,"given":"Guangyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China, <email>zhangguangyuan16@mails.ucas.ac.cn<\/email> (G.Z.)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoping","family":"Rui","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering; Hohai University; Nanjing 211000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Poslad","sequence":"additional","affiliation":[{"name":"Queen Mary University of London, London E1 4NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianfeng","family":"Song","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China, <email>zhangguangyuan16@mails.ucas.ac.cn<\/email> (G.Z.)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonglei","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China, <email>zhangguangyuan16@mails.ucas.ac.cn<\/email> (G.Z.)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixiang","family":"Ma","sequence":"additional","affiliation":[{"name":"Queen Mary University of London, London E1 4NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MPRV.2011.44","article-title":"A tale of one city: Using cellular network data for urban planning","volume":"10","author":"Becker","year":"2011","journal-title":"IEEE Pervasive Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"De Nadai, M., Staiano, J., Larcher, R., Sebe, N., Quercia, D., and Lepri, B. 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