A Hybrid Geometric-Qualitative Spatial Reasoning System and Its Application in GIS

Felice, Giorgio De; Fogliaroni, Paolo; Wallgrün, Jan Oliver

doi:10.1007/978-3-642-23196-4_11

Cited by 2 publications

(1 citation statement)

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“…Clementini () defines transformations only on a qualitative spatial level (i.e., as mappings from 5‐intersection to spatial relations) and only for crisp objects. Eschenbach ()'s coordinate‐free axiomatic specification of Levinson's spatial frames of reference and De Felice, Fogliaroni, and Wallgrün ()'s geometric model of qualitative spatial relationships between extended objects account neither for spatial uncertainty nor for transformations. Matsakis, Wendling, and Ni () model spatial relationships in terms of summarized cross‐sections and force fields in a way very similar to the fuzzy vector approach taken in this article.…”

Section: Challenges Of Modeling Cognitive Spatial Transformationsmentioning

confidence: 99%

Computing with cognitive spatial frames of reference in GIS

Scheider

Hahn²,

Weiser

et al. 2018

Transactions in GIS

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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."

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Section: Challenges Of Modeling Cognitive Spatial Transformationsmentioning

confidence: 99%