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A space-time varying graph for modelling places and events in a network / Ikechukwu Maduako in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1915 - 1935 Titre : A space-time varying graph for modelling places and events in a network Type de document : Article/Communication Auteurs : Ikechukwu Maduako, Auteur ; Monica Wachowicz, Auteur Année de publication : 2019 Article en page(s) : pp 1915 - 1935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] accident de la route [Termes descripteurs IGN] analyse des risques [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] connexité (graphes) [Termes descripteurs IGN] graphe [Termes descripteurs IGN] relation topologique [Termes descripteurs IGN] représentation spatiale [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] voisinage (topologie) Résumé : (auteur) Modelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time. Numéro de notice : A2019-393 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1603386 date de publication en ligne : 17/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1603386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93497 [article]