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Introducing diversion graph for real-time spatial data analysis with location based social networks / Sameera Kannangara (2021)  

Public Titre : Introducing diversion graph for real-time spatial data analysis with location based social networks Type de document : Article/Communication Auteurs : Sameera Kannangara, Auteur ; Hairuo Xie, Auteur ; Egemen Tanin, Auteur ; Aaron Harwood, Auteur ; Shanika Karunasekera, Auteur Editeur : Leibniz [Allemagne] : Schloss Dagstuhl – Leibniz-Zentrum für Informatik Année de publication : 2021 Conférence : GIScience 2021, 11th International Conference on Geographic Information Science 27/09/2021 30/09/2021 Poznań Pologne Open Access Proceedings Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] chemin le plus court, algorithme du [Termes IGN] graphe [Termes IGN] image Flickr [Termes IGN] objet mobile [Termes IGN] réseau social géodépendant [Termes IGN] temps réel [Termes IGN] triangulation de Delaunay [Termes IGN] Twitter Résumé : (auteur) Neighbourhood graphs are useful for inferring the travel network between locations posted in the Location Based Social Networks (LBSNs). Existing neighbourhood graphs, such as the Stepping Stone Graph lack the ability to process a high volume of LBSN data in real time. We propose a neighbourhood graph named Diversion Graph, which uses an efficient edge filtering method from the Delaunay triangulation mechanism for fast processing of LBSN data. This mechanism enables Diversion Graph to achieve a similar accuracy level as Stepping Stone Graph for inferring travel networks, but with a reduction of the execution time of over 90%. Using LBSN data collected from Twitter and Flickr, we show that Diversion Graph is suitable for travel network processing in real time. Numéro de notice : C2021-079 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Communication DOI : 10.4230/LIPIcs.GIScience.2021.I.7 Date de publication en ligne : 25/09/2020 En ligne : https://doi.org/10.4230/LIPIcs.GIScience.2021.I.7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100930