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Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records / Zhang Liu in Transactions in GIS, vol 22 n° 2 (April 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 2 (April 2018) . - pp 494 - 513 Titre : Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records Type de document : Article/Communication Auteurs : Zhang Liu, Auteur ; Ting Ma, Auteur ; Yunyan Du, Auteur ; Tao Pei, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 494 - 513 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] carte thématique [Termes IGN] cartographie des flux [Termes IGN] classification par réseau neuronal [Termes IGN] mobilité urbaine [Termes IGN] population urbaine [Termes IGN] régression [Termes IGN] téléphone intelligent [Termes IGN] trace numérique [Termes IGN] trajet (mobilité) Résumé : (Auteur) Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation. Numéro de notice : A2018-215 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12323 Date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1111/tgis.12323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90006 [article]