Cartography and Geographic Information Science / Cartography and geographic information society . vol 36 n° 4Mention de date : October 2009 Paru le : 01/10/2009 ISBN/ISSN/EAN : 1523-0406 |
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est un bulletin de Cartography and Geographic Information Science / Cartography and geographic information society (1999 -)
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Code-barres | Cote | Support | Localisation | Section | Disponibilité |
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032-09041 | RAB | Revue | Centre de documentation | En réserve L003 | Disponible |
Dépouillements
Ajouter le résultat dans votre panierA prototype feature system for feature retrieval using relationships / J. Choi in Cartography and Geographic Information Science, vol 36 n° 4 (October 2009)
[article]
Titre : A prototype feature system for feature retrieval using relationships Type de document : Article/Communication Auteurs : J. Choi, Auteur ; E. Lynn Usery, Auteur Année de publication : 2009 Article en page(s) : pp 331 - 345 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] attribut sémantique
[Termes IGN] base de données localisées
[Termes IGN] bati
[Termes IGN] Etats-Unis
[Termes IGN] modèle orienté objet
[Termes IGN] prototype
[Termes IGN] relation topologique
[Termes IGN] requête non spatiale
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features. Numéro de notice : A2009-440 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1559/152304009789786353 En ligne : https://doi.org/10.1559/152304009789786353 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30071
in Cartography and Geographic Information Science > vol 36 n° 4 (October 2009) . - pp 331 - 345[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-09041 RAB Revue Centre de documentation En réserve L003 Disponible A neural network-based method for solving "nested hierarchy" areal interpolation problems / D. Merwin in Cartography and Geographic Information Science, vol 36 n° 4 (October 2009)
[article]
Titre : A neural network-based method for solving "nested hierarchy" areal interpolation problems Type de document : Article/Communication Auteurs : D. Merwin, Auteur ; R. Cromley, Auteur ; Daniel L. Civco, Auteur Année de publication : 2009 Article en page(s) : pp 347 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse comparative
[Termes IGN] Connecticut (Etats-Unis)
[Termes IGN] figuration de la densité
[Termes IGN] interpolation par pondération de zones
[Termes IGN] interpolation spatiale
[Termes IGN] prévision
[Termes IGN] recensement démographique
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] structure hiérarchique de donnéesRésumé : (Auteur) This study proposes a neural network approach to solving areal interpolation scenarios, specifically the “nested hierarchy” problem. The neural network method presented adopts the approach taken by intelligent interpolation methods where ancillary spatial information is presented to assist in achieving more accurate results. For this study, the data to be estimated are total populations for census tracts and block groups in Hartford County, Connecticut. A number of neural network models are generated containing various combinations of ancillary spatial information. The neural-network-derived predictions are compared with the predicted populations derived from three existing interpolation methods: areal weighting, a dasymetric areal weighting approach using remote sensing data, and ordinary least squares (OLS) regression. For each scenario presented, the proposed neural network approach outperforms each of the existing methods. Numéro de notice : A2009-441 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article DOI : 10.1559/152304009789786335 En ligne : https://doi.org/10.1559/152304009789786335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30072
in Cartography and Geographic Information Science > vol 36 n° 4 (October 2009) . - pp 347 - 365[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-09041 RAB Revue Centre de documentation En réserve L003 Disponible