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A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships / Ick-Hoi Kim in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1042 - 1060 Titre : A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships Type de document : Article/Communication Auteurs : Ick-Hoi Kim, Auteur ; Chen-Chieh Feng, Auteur ; Yi-Chen Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1042 - 1060 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] appariement de données localisées [Termes IGN] axe médian [Termes IGN] base de données historiques [Termes IGN] classification par arbre de décision [Termes IGN] conflation [Termes IGN] distance de Hausdorff [Termes IGN] données anciennes [Termes IGN] objet géographique linéaire [Termes IGN] relation topologique [Termes IGN] réseau routier [Termes IGN] similitude [Termes IGN] valeur moyenne Résumé : (auteur) Linear feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified linear feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple linear feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for linear feature matching. Numéro de notice : A2017-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1267736 En ligne : http://dx.doi.org/10.1080/13658816.2016.1267736 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85177 [article]

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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]