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Formal Qualitative Spatial Augmentation of the Simple Feature Access Model

Authors Shirly Stephen, Torsten Hahmann

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Ontology engineering
  • Computing methodologies → Spatial and physical reasoning
  • Information systems → Geographic information systems
Keywords
  • space
  • geometry
  • geospatial semantics
  • qualitative spatial representation (QSR)
  • simple feature access
  • topological relations
  • formal ontology

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Abstract

The need to share and integrate heterogeneous geospatial data has resulted in the development of geospatial data standards such as the OGC/ISO standard Simple Feature Access (SFA), that standardize operations and simple topological and mereotopological relations over various geometric features such as points, line segments, polylines, polygons, and polyhedral surfaces. While SFA’s supplied relations enable qualitative querying over the geometric features, the relations' semantics are not formalized. This lack of formalization prevents further automated reasoning - apart from simple querying - with the geometric data, either in isolation or in conjunction with external purely qualitative information as one might extract from textual sources, such as social media. To enable joint qualitative reasoning over geometric and qualitative spatial information, this work formalizes the semantics of SFA’s geometric features and mereotopological relations by defining or restricting them in terms of the spatial entity types and relations provided by CODIB, a first-order logical theory from an existing logical formalization of multidimensional qualitative space.

Cite As Get BibTex

Shirly Stephen and Torsten Hahmann. Formal Qualitative Spatial Augmentation of the Simple Feature Access Model. In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.COSIT.2019.15

Author Details

Shirly Stephen
  • School of Computing and Information Science, University of Maine, Orono, ME 04469, USA
Torsten Hahmann
  • School of Computing and Information Science, University of Maine, Orono, ME 04469, USA

Funding

This material is based upon work supported by the National Science Foundation under Grant Number III-1565811.

Acknowledgements

The authors are grateful for the four anonymous reviewer’s thoughtful comments that helped improve the final version of the paper.

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