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Fully automated generalization of a 1:50k map from 1:10k data / Jantien E. Stoter in Cartography and Geographic Information Science, vol 41 n° 1 (January 2014)  

Public [article]  inCartography and Geographic Information Science > vol 41 n° 1 (January 2014) . - pp 1 - 13 Titre : Fully automated generalization of a 1:50k map from 1:10k data Type de document : Article/Communication Auteurs : Jantien E. Stoter, Auteur ; Marc Post, Auteur ; Vincent Van Altena, Auteur ; Ron Nijhuis, Auteur ; Ben Bruns, Auteur Année de publication : 2014 Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] 1:10.000 [Termes IGN] 1:50.000 [Termes IGN] carte topographique [Termes IGN] chaîne de production [Termes IGN] données multiéchelles [Termes IGN] données topographiques [Termes IGN] généralisation automatique de données [Termes IGN] généralisation cartographique automatisée [Termes IGN] Pays-Bas [Vedettes matières IGN] Généralisation Résumé : (Auteur) This article presents research that implements a fully automated workflow to generalize a 1:50k map from 1:10k data. This is the first time that a complete topographic map has been generalized without any human interaction. More noteworthy is that the resulting map is good enough to replace the existing map. Specifications for the automated process were established as part of this research. Replication of the existing map was not the aim, because feasibility of automated generalization is better when compliance with traditional generalizations rules is loosened and alternate approaches are acceptable. Indeed, users valued the currency and relevancy of geographical information more than complying with all existing cartographic guidelines. The development of the workflow thus started with the creation of a test map with automated generalization operations. The reason for the test map was to show what is technologically possible and to refine the results based on iterative users’ evaluation. The generalization operations (200 in total) containing the relevant algorithms and parameter values were developed and implemented in one model. Particular effort was made to enrich the source data in order to improve the results. The model is context aware which means it is able to apply different algorithms or adjust parameter values in accordance with a specific area. The result of the research is a fully automated generalization workflow that produces a countrywide map at scale 1:50k from 1:10k data in 50 hours. A fully automated workflow may be the only way to produce flexible and on-demand products; consequently, the results were implemented as a new production line in 2013. Issues for further research have been identified. Numéro de notice : A2014-183 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/15230406.2013.824637 En ligne : https://doi.org/10.1080/15230406.2013.824637 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33088 [article]

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Visualizing robust geographically weighted parameter estimates / Robert G. Cromley in Cartography and Geographic Information Science, vol 41 n° 1 (January 2014)  

Public [article]  inCartography and Geographic Information Science > vol 41 n° 1 (January 2014) . - pp 100 - 110 Titre : Visualizing robust geographically weighted parameter estimates Type de document : Article/Communication Auteurs : Robert G. Cromley, Auteur ; Dean M. Hanink, Auteur Année de publication : 2014 Article en page(s) : pp 100 - 110 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatiale [Termes IGN] estimation des paramètres [Termes IGN] méthode robuste [Termes IGN] régression géographiquement pondérée [Termes IGN] visualisation cartographique [Termes IGN] visualisation de données Résumé : (Auteur) This article extends earlier work on visualizing parameter estimates for spatial analysis. Robust parameters are those that are resistant to outliers, and so are order-based rather than moment-based in their derivation. The principles of geographical weighting of descriptive statistics are reviewed and applied to the development and computation of geographically weighted quantile graphs. The principles are also applied to the development and mapping of robust geographically weighted regression models. The robust statistics and parameters developed in this article are especially useful for visual exploratory data analysis because they provide local information in two contexts: with respect to spatial proximity and with respect to proximity in the quantiles of the variable of interest. The suitability of the results for visualization in maps and quantile plots is demonstrated. Numéro de notice : A2014-184 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2013.831205 En ligne : https://doi.org/10.1080/15230406.2013.831205 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33089 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2014011	RAB	Revue	Centre de documentation	En réserve L003	Disponible