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Computing with cognitive spatial frames of reference in GIS / Simon Scheider in Transactions in GIS, vol 22 n° 5 (October 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 5 (October 2018) . - pp 1083 - 1104 Titre : Computing with cognitive spatial frames of reference in GIS Type de document : Article/Communication Auteurs : Simon Scheider, Auteur ; Jürgen Hahn, Auteur ; Paul Weiser, Auteur ; Werner Kuhn, Auteur Année de publication : 2018 Article en page(s) : pp 1083 - 1104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] espace vectoriel [Termes IGN] logique floue [Termes IGN] modèle cognitif [Termes IGN] transformation géométrique Résumé : (Auteur) In everyday communication, people effortlessly translate between spatial cognitive frames of reference. For example, a tourist guide translates from a map (“the fountain is north‐west of the church”) into a cognitive frame for a tourist (“the fountain in front of the church”). While different types of cognitive reference frames and their relevance for language cultures have been studied in considerable depth, we still lack adequate transformation models. In this article, we argue that transformations in current Geographic Information Systems (GIS) are inappropriate to this end. Appropriate transformation models need to go beyond point discretization to take into account vague transformations, in order to deal with forms, sizes, and vagueness of spatial relations relative to ground objects. We argue that neural fields should be used to denote fuzzy positions, directions, and sizes in a particular frame. We propose fuzzy vector spaces to approximate neural field behavior with affine transformations, including fuzzy translation, rotation, and scaling, in order to efficiently transform between different cognitive perspectives. We use an implementation in Haskell to describe a geographic map from the perspective of six well‐known cognitive frames of reference. Based on these findings, we give an outlook on the principles of a “neural GIS.” Numéro de notice : A2018-570 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12318 Date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.1111/tgis.12318 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92289 [article]