Centre de documentation
scientifique

Combining Geo-SOM and hierarchical clustering to explore geospatial data / Chen-Chieh Feng in Transactions in GIS, vol 18 n° 1 (February 2014)  

Public [article]  inTransactions in GIS > vol 18 n° 1 (February 2014) . - pp 125 - 146 Titre : Combining Geo-SOM and hierarchical clustering to explore geospatial data Type de document : Article/Communication Auteurs : Chen-Chieh Feng, Auteur ; Yi-Chen Wang, Auteur ; Chih-Yuan Chen, Auteur Année de publication : 2014 Article en page(s) : pp 125 - 146 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse combinatoire (maths) [Termes IGN] analyse de groupement [Termes IGN] carte de Kohonen [Termes IGN] données localisées [Termes IGN] exploration de données géographiques [Termes IGN] visualisation Résumé : (Auteur) Geo-SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo-SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo-SOM and hierarchical clustering to tackle this problem. Geo-SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo-SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo-SOM only identified two. Among the four hierarchical clustering methods, Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data. Numéro de notice : A2014-068 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12025 Date de publication en ligne : 16/09/2013 En ligne : https://doi.org/10.1111/tgis.12025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32973 [article]