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Density-based clustering for data containing two types of points / Tao Pei in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 2 (February 2015) . - pp 175 - 193 Titre : Density-based clustering for data containing two types of points Type de document : Article/Communication Auteurs : Tao Pei, Auteur ; Weiyi Wang, Auteur ; Hengcai Zhang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 175 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification par seuillage sur la limite la plus proche [Termes IGN] densité d'information [Termes IGN] échelle d'intensité [Termes IGN] groupe [Termes IGN] taxi [Termes IGN] transport routier Résumé : (Auteur) When only one type of point is distributed in a region, clustered points can be seen as an anomaly. When two different types of points coexist in a region, they overlap at different places with various densities. In such cases, the meaning of a cluster of one type of point may be altered if points of the other type show different densities within the same cluster. If we consider the origins and destinations (OD) of taxicab trips, the clustering of both in the morning may indicate a transportation hub, whereas clustered origins and sparse destinations (a hot spot where taxis are in short supply) could suggest a densely populated residential area. This cannot be identified by previous clustering methods, so it is worthwhile studying a clustering method for two types of points. The concept of two-component clustering is first defined in this paper as a group containing two types of points, at least one of which exhibits clustering. We then propose a density-based method for identifying two-component clusters. The method is divided into four steps. The first estimates the clustering scale of the point data. The second transforms the point data into the 2D density domain, where the x and y axes represent the local density of each type of point around each point, respectively. The third determines the thresholds for extracting the clusters, and the fourth generates two-component clusters using a density-connectivity mechanism. The method is applied to taxicab trip data in Beijing. Three types of two-component clusters are identified: high-density origins and destinations, high-density origins and low-density destinations, and low-density origins and high-density destinations. The clustering results are verified by the spatial relationship between the cluster locations and their land-use types over different periods of the day. Numéro de notice : A2015-577 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.955027 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.955027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77839 [article]