Centre de documentation
scientifique

Spatio-temporal polygonal clustering with space and time as first-class citizens / Deepti Joshi in Geoinformatica, vol 17 n° 2 (April 2013)  

Public [article]  inGeoinformatica > vol 17 n° 2 (April 2013) . - pp 387 - 412 Titre : Spatio-temporal polygonal clustering with space and time as first-class citizens Type de document : Article/Communication Auteurs : Deepti Joshi, Auteur ; Ashok Samal, Auteur ; Leen-Kiat Soh, Auteur Année de publication : 2013 Article en page(s) : pp 387 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] données spatiotemporelles [Termes IGN] exploration de données [Termes IGN] polygonale Résumé : (Auteur) Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application. Numéro de notice : A2013-164 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-012-0157-8 Date de publication en ligne : 15/03/2012 En ligne : https://doi.org/10.1007/s10707-012-0157-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32302 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-2013021	RAB	Revue	Centre de documentation	En réserve L003	Disponible