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Discovering spatial interaction communities from mobile phone data / Song Gao in Transactions in GIS, vol 17 n° 3 (June 2013)  

Public [article]  inTransactions in GIS > vol 17 n° 3 (June 2013) . - pp 463 - 481 Titre : Discovering spatial interaction communities from mobile phone data Type de document : Article/Communication Auteurs : Song Gao, Auteur ; Yu Liu, Auteur ; Yaoli Wang, Auteur ; Xiujun Ma, Auteur Année de publication : 2013 Article en page(s) : pp 463 - 481 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] Chine [Termes IGN] communauté urbaine [Termes IGN] comportement [Termes IGN] découverte de connaissances [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] interaction spatiale [Termes IGN] téléphonie mobile Résumé : (Auteur) In the age of Big Data, the widespread use of location-awareness technologies has made it possible to collect spatio-temporal interaction data for analyzing flow patterns in both physical space and cyberspace. This research attempts to explore and interpret patterns embedded in the network of phone-call interaction and the network of phone-users’ movements, by considering the geographical context of mobile phone cells. We adopt an agglomerative clustering algorithm based on a Newman-Girvan modularity metric and propose an alternative modularity function incorporating a gravity model to discover the clustering structures of spatial-interaction communities using a mobile phone dataset from one week in a city in China. The results verify the distance decay effect and spatial continuity that control the process of partitioning phone-call interaction, which indicates that people tend to communicate within a spatial-proximity community. Furthermore, we discover that a high correlation exists between phone-users’ movements in physical space and phone-call interaction in cyberspace. Our approach presents a combined qualitative-quantitative framework to identify clusters and interaction patterns, and explains how geographical context influences communities of callers and receivers. The findings of this empirical study are valuable for urban structure studies as well as for the detection of communities in spatial networks. Numéro de notice : A2013-294 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12042 Date de publication en ligne : 17/05/2013 En ligne : https://doi.org/10.1111/tgis.12042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32432 [article]