A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records
dc.contributor.author | Zhang, G | |
dc.contributor.author | Rui, X | |
dc.contributor.author | Poslad, S | |
dc.contributor.author | Song, X | |
dc.contributor.author | Fan, Y | |
dc.contributor.author | Wu, B | |
dc.date.accessioned | 2021-03-31T09:15:25Z | |
dc.date.available | 2021-03-31T09:15:25Z | |
dc.date.issued | 2020-08 | |
dc.identifier.citation | Zhang, Guangyuan et al. "A Method For The Estimation Of Finely-Grained Temporal Spatial Human Population Density Distributions Based On Cell Phone Call Detail Records". Remote Sensing, vol 12, no. 16, 2020, p. 2572. MDPI AG, doi:10.3390/rs12162572. Accessed 31 Mar 2021. | en_US |
dc.identifier.other | ARTN 2572 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/70984 | |
dc.description.abstract | Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN). In this experiment, the highest spatial resolution of every grid cell is 125125 square metre, while the temporal resolution can vary from minutes to hours with varying accuracy. To demonstrate our method, we present an application of how to map the estimated population density distribution dynamically for CDR big data from Beijing, choosing a half hour as the temporal resolution. Finally, in order to cross-check previous studies that claim the population distribution at nighttime (from 8 p.m. to 8 a.m. on the next day) mapped by Beijing census data are similar to the ground truth data, we estimated the baseline distribution, first, based upon records in CDRs. Second, we estimate a baseline distribution based upon Global Navigation Satellite System (GNSS) data. The results also show the Root Mean Square Error (RMSE) is about 5000 while the two baseline distributions mentioned above have an RMSE of over 13,500. Our estimation method provides a fast and simple process to map people’s actual density distributions at a more finely grained, i.e., hourly, temporal resolution. | en_US |
dc.publisher | MDPI | en_US |
dc.relation.ispartof | REMOTE SENSING | |
dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | population density distribution estimation | en_US |
dc.subject | call detail records (CDR) | en_US |
dc.subject | artificial population density | en_US |
dc.subject | deep convolutional generative adversarial networks (DCGANs) | en_US |
dc.subject | big data | en_US |
dc.title | A Method for the Estimation of Finely-Grained Temporal Spatial Human Population Density Distributions Based on Cell Phone Call Detail Records | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2020, The Author(s) | |
dc.identifier.doi | 10.3390/rs12162572 | |
pubs.author-url | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000565445900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6a | en_US |
pubs.issue | 16 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 12 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
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Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.