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STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1559 - 1577 Titre : STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Zhenhong Du, Auteur ; Yuhua Gu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1559 - 1577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification barycentrique [Termes IGN] données spatiotemporelles [Termes IGN] exploration de données [Termes IGN] exploration de données géographiques [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] origine - destination [Termes IGN] Pékin (Chine) [Termes IGN] taxi Résumé : (Auteur) Clustering on spatiotemporal point events with multiple types is an important step for exploratory data mining and can help us reveal the correlation of event types. In this article, we present an effective method for discovering spatiotemporal multi‐type clusters containing events with different densities and event types (STME). Particularly, the type of events in a cluster can be different, and clusters with similar densities but different internal compositions should be distinguished. We use the distance to the kth nearest neighbour to define the size of the searched neighbourhood, and expand clusters by the concept of cluster reachable, ensuring that the proportion of various types of events in the cluster remains stable. The concept of clustering priority is also proposed to make the cluster always expand from the region with the highest density, which improves the robustness of clustering. Moreover, the density of multiple types of events in clusters is estimated to discover the internal structure of clusters and further explore the correlation between events. The effectiveness of the STME algorithm is demonstrated in several simulated and real data sets, including points of interest data in Beijing and the origins and destinations of taxi trips in New York. Numéro de notice : A2020-768 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12662 Date de publication en ligne : 19/07/2020 En ligne : https://doi.org/10.1111/tgis.12662 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96660 [article]