License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.45
URN: urn:nbn:de:0030-drops-93731
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9373/
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Mc Cutchan, Marvin ; Giannopoulos, Ioannis

Geospatial Semantics for Spatial Prediction (Short Paper)

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LIPIcs-GISCIENCE-2018-45.pdf (0.3 MB)

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.

BibTeX - Entry

@InProceedings{mccutchan_et_al:LIPIcs:2018:9373,
  author =	{Marvin Mc Cutchan and Ioannis Giannopoulos},
  title =	{{Geospatial Semantics for Spatial Prediction (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{45:1--45:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9373},
  URN =		{urn:nbn:de:0030-drops-93731},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.45},
  annote =	{Keywords: Geospatial semantics, spatial prediction, machine learning, Linked Data}
}

Keywords: Geospatial semantics, spatial prediction, machine learning, Linked Data
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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