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A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data / Biao He in ISPRS International journal of geo-information, vol 7 n° 6 (June 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 6 (June 2018) Titre : A simple line clustering method for spatial analysis with origin-destination data and its application to bike-sharing movement data Type de document : Article/Communication Auteurs : Biao He, Auteur ; Zhang Yan, Auteur ; Yu Chen, Auteur ; Zhihui Gu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] bicyclette [Termes IGN] entropie [Termes IGN] extraction de modèle [Termes IGN] origine - destination [Termes IGN] raisonnement spatial [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) Clustering methods are popular tools for pattern recognition in spatial databases. Existing clustering methods have mainly focused on the matching and clustering of complex trajectories. Few studies have paid attention to clustering origin-destination (OD) trips and discovering strong spatial linkages via OD lines, which is useful in many areas such as transportation, urban planning, and migration studies. In this paper, we present a new Simple Line Clustering Method (SLCM) that was designed to discover the strongest spatial linkage by searching for neighboring lines for every OD trip within a certain radius. This method adopts entropy theory and the probability distribution function for parameter selection to ensure significant clustering results. We demonstrate this method using bike-sharing location data in a metropolitan city. Results show that (1) the SLCM was significantly effective in discovering clusters at different scales, (2) results with the SLCM analysis confirmed known structures and discovered unknown structures, and (3) this approach can also be applied to other OD data to facilitate pattern extraction and structure understanding. Numéro de notice : A2018-345 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7060203 Date de publication en ligne : 29/05/2018 En ligne : https://doi.org/10.10.3390/ijgi7060203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90568 [article]