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Geospatial Semantics for Spatial Prediction (Short Paper)

Authors Marvin Mc Cutchan, Ioannis Giannopoulos

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Marvin Mc Cutchan
  • Vienna University of Technology, Austria
Ioannis Giannopoulos
  • Vienna University of Technology, Austria

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Marvin Mc Cutchan and Ioannis Giannopoulos. Geospatial Semantics for Spatial Prediction (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 45:1-45:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.45

Abstract

In this paper the potential of geospatial semantics for spatial predictions is explored. Therefore data from the LinkedGeoData platform is used to predict landcover classes described by the CORINE dataset. Geo-objects obtained from LinkedGeoData are described by an OWL ontology, which is utilized for the purpose of spatial prediction within this paper. This prediction is based on an association analysis which computes the collocations between the landcover classes and the semantically described geo-objects. The paper provides an analysis of the learned association rules and finally concludes with a discussion on the promising potential of geospatial semantics for spatial predictions, as well as potentially fruitful future research within this domain.

Subject Classification

ACM Subject Classification
  • Information systems → Association rules
  • Information systems → Geographic information systems
  • Software and its engineering → Semantics
Keywords
  • Geospatial semantics
  • spatial prediction
  • machine learning
  • Linked Data

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References

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