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Specifying map requirements for automated generalization of topographic data / Jantien E. Stoter in Cartographic journal (the), vol 46 n° 3 (August 2009)  

Public [article]  inCartographic journal (the) > vol 46 n° 3 (August 2009) . - pp 214 - 227 Titre : Specifying map requirements for automated generalization of topographic data Type de document : Article/Communication Auteurs : Jantien E. Stoter, Auteur ; J.W.N. van Smaalen, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 214 - 227 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] acquisition de connaissances [Termes IGN] analyse des besoins [Termes IGN] données topographiques [Termes IGN] généralisation cartographique automatisée [Termes IGN] spécification de processus [Vedettes matières IGN] Généralisation Résumé : (Auteur) This study aims at acquiring knowledge on map requirements for automated generalization. First, interactively generalized map series were visually analysed together with the specifications that cartographers use to generalize the maps. Second, these map specifications were experimentally implemented on real data in automated processes and compared to an interactively generalized map to see if the results are according to the specifications; to see if the specifications are complete and well-formalized; and to identify situations that were not addressed in the specifications. If required, the specifications were enriched and re-implemented also adding extra information from other sources. The experiments revealed the 'deep' knowledge which cartographers add to the interactive process. Based on this revealed knowledge, recommendations are formulated to specify map requirements for automated generalization of topographic data. Copyright British Cartographic Society Numéro de notice : A2009-405 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/174327709X446637 En ligne : https://doi.org/10.1179/174327709X446637 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30036 [article]

Exemplaires(1)

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030-09031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Database requirements for generalisation and multiple representations / Sébastien Mustière (01/01/2007)

Public Titre : Database requirements for generalisation and multiple representations Type de document : Chapitre/Contribution Auteurs : Sébastien Mustière , Auteur ; J.W.N. van Smaalen, Auteur Editeur : Amsterdam [Pays Bas] : Elsevier Année de publication : 01/01/2007 Importance : pp 113 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] approche hiérarchique [Termes IGN] classe d'objets [Termes IGN] MADS [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modèle entité-association [Termes IGN] modèle orienté objet [Termes IGN] modèle topologique de données [Termes IGN] objet géographique [Termes IGN] représentation multiple [Termes IGN] structure hiérarchique de données Résumé : (Auteur) In this chapter, database requirements for generalisation and multiple representations are pre-sented. In particular the paper focuses on modelling requirements. Some general concepts in the field of databases are reviewed before elaboration on the modelling of geographical databases and discussion of the modelling of multiple representations for geographical databases. The chap-ter stresses the requirements for efficiently modelling data before and during the generalisation process. The reader will first encounter a description of the basic notions of model, schema and objects. Then geographic modelling and multiple representation will be described through the ex-ample of the MADS model. A discussion is presented on the different requirements for modelling multiple representations (1) during the generalisation process, or (2) to store the generalisation result, or (3) to perform the integration of independent databases. Then the meaning and role of topology, composite objects and hierarchies will be emphasised in the requirements for generalisation, with aggregation taken as an important example to illustrate this process. Numéro de notice : H2007-002 Affiliation des auteurs : IGN+Ext (1940-2011) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Chapître / contribution Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65894

Automated aggregation of geographic objects / J.W.N. van Smaalen (2003)  

Public Titre : Automated aggregation of geographic objects : a new approach to the conceptual generalisation of geographic databases Type de document : Thèse/HDR Auteurs : J.W.N. van Smaalen, Auteur Editeur : Delft : Netherlands Geodetic Commission NGC Année de publication : 2003 Collection : Netherlands Geodetic Commission Publications on Geodesy, ISSN 0165-1706 num. 55 Importance : 94 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-90-6132-282-5 Note générale : Bibliographie doctoral dissertation Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation de données [Termes IGN] base de données localisées [Termes IGN] généralisation cartographique automatisée [Termes IGN] généralisation de base de données [Termes IGN] implémentation (informatique) [Termes IGN] modèle (conceptuel) de généralisation [Termes IGN] modèle conceptuel de données [Termes IGN] modèle topologique de données [Termes IGN] relation spatiale [Termes IGN] représentation multiple [Vedettes matières IGN] Généralisation Résumé : (Auteur) Since the late 1960's automated methods for map generalisation have been studied, but thus far no comprehensive system has been achieved. This is due to the general complexity of the matter, part of which is caused by the inability to separate the conceptual and the graphic issues. These aspects of map generalisation are considered separate issues ever since the advent of GIS but in practice it has been difficult to disconnect the conceptual issues from the impediments of graphic representation, either in the form of a paper map or on a computer screen. Current research into automated map generalisation generally appears to be in a cul-de-sac for this reason. This study therefore aims to concentrate on strictly non-graphic operations and large generalisation steps, i.e. big scale changes. Whereas most existing methods work towards a clear end result, this approach does not. Instead, it is entirely based on the input data. Minimizing generalisation errors is a priority and assessment of the generalisation results is also an issue to consider. The goal is to develop a system for the generalisation of object-and vector-based categorical maps, such as large-scale topographic data, that is to a large extent automated and can be operated by non-expert users. In the past, several generalisation procedures have been developed for individual objects and dichotomous maps, but the number of procedures for categorical maps is still limited and the methods that do exist rely on similarity and importance factors that are hard to determine. Large-scale categorical data mostly form an area partition, i.e. the whole spatial extent of the dataset is covered by objects and the objects do not overlap. This implies that objects cannot simply be removed - since this would cause 'holes' -but have to be combined or aggregated. Objects can be aggregated based on taxonomy or partonomy relationships. Taxonomy relationships are based on similarity between the objects or classes. Aggregation based on taxonomy relationships has been described extensively in map generalisation literature, but only works within a limited spatial range. Since this study is aimed at large-scale changes it is based on the much less described partonomy relationships. Inter-object and inter-class relationships are used to determine functionally related classes in order to aggregate the object instances of the class. It is assumed that spatial correlations indicate functional relationships. The class adjacency index is used as a measure of spatial correlation between classes. Combinations of classes with a high class adjacency index are likely candidates for the creation of composite classes. Adjacent objects of these classes can subsequently be aggregated and reclassified to create composite objects. The class adjacency index is determined based on adjacency measures of the member objects. The input dataset must therefore form a topologically correct, object-based area partition. The implementation is based on a stored adjacency graph and uses regular relational database management software. The data model is object-based and supports the concept of composite objects. In the process a multiple representations dataset is produced by connecting the composite objects created in every aggregation cycle to the constituent parts in the previous level. The process can be fully automated but it is also possible to allow user interaction at several points in the process without compromising the approach. Since it is entirely based on characteristics of the input dataset, the method is also suited for exploratory purposes. To a certain degree, the meaning of the classes is not even relevant, although in interactive mode the user naturally has to be aware of the classes. The method was applied to two Dutch topographic datasets: TOP10 vector and GBKN. The results show that this is a very promising method for conceptual generalisation. The concept of composite classes makes that generalisation errors are not an issue. Therefore, it cannot be evaluated using conventional generalisation effect measures. The output of the aggregation process is not readily suitable for mapping purposes, and additional cartographic generalisation is in that case required. The current implementation is not intended as a complete solution for conceptual generalisation. But since it is set in an environment of other conceptual generalisation operations, such as structural generalisation and extended adjacency graphs, it can be extended to create such a comprehensive system. Note de contenu : Chapter 1 : Introduction Chapter 2 : Related literature Chapter 3 : Conceptual data model Chapter 4 : Abstraction process Chapter 5 : Datasets Chapter 6 : Implementation Chapter 7 : Results and discussion Chapter 8 : Conclusions and recommendations Numéro de notice : 15084 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geo-information : Wageningen University : 2003 DOI : sans En ligne : https://www.ncgeo.nl/downloads/55VanSmaalen.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=55057

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
15084-02	37.10	Livre	Centre de documentation	Géomatique	Disponible
15084-01	37.10	Livre	Centre de documentation	Géomatique	Disponible
15084-03	37.10	Livre	Centre de documentation	Géomatique	Disponible