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Qualitative spatial reasoning in a logical framework

Geographical data rarely, if ever, come truly free of error since imperfection is an endemic feature of geographical information. Imperfection can be thought of as comprising two distinct orthogonal concepts: error and imprecision. Error, or inaccuracy, concerns a lack of correlation of an observation with reality; imprecision concerns a lack of specificity in representation. Starting from this ontology of imperfection, formalisms to reason with these different aspects of uncertainty in spatio-temporal data are to be investigated (i.e. degrees of certainty, fuzzy and rough sets etc.). There is a widespread research activity on these topics. In particular we are working on exploiting computational intelligence techniques for spatial data analysis, by means of logic-based and constraint-based query languages.

An approach particularly interesting for uncertainty handling that is receiving much attention in literature is spatial qualitative reasoning such as reasoning on proximity, topology, and directions of spatial objects. In particular, we intend to stress here more the connection with the temporal aspect for qualitative spatio-temporal reasoning. We propose an approach to qualitative spatial reasoning based on the spatio-temporal language STACLP. In particular, we work on the topological 9-intersection model and the direction relations based on projections can be modelled in such a framework. STACLP is a constraint logic programming language where formulae can be annotated with labels (annotations) and where relations between these labels can be expressed by using constraints. Annotations are used to represent both time and space.

Reported by

CONSIGLIO NAZIONALE DELLE RICERCHE
ISTI, Area della Ricerca CNR di Pisa, Via G. Moruzzi 1
56124 PISA
Italy
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