2 Search Results for "Mc Cutchan, Marvin"


Document
Short Paper
Unfolding Urban Structures: Towards Route Prediction and Automated City Modeling (Short Paper)

Authors: Paolo Fogliaroni, Marvin Mc Cutchan, Gerhard Navratil, and Ioannis Giannopoulos

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)


Abstract
This paper extends previous work concerning intersection classification by including a new set of statistics that enable to describe the structure of a city at a higher level of detail. Namely, we suggest to analyze sequences of intersections of different types. We start with sequences of length two and present a probabilistic model to derive statistics for longer sequences. We validate the results by comparing them with real frequencies. Finally, we discuss how this work can contribute to the generation of virtual cities as well as to spatial configuration search.

Cite as

Paolo Fogliaroni, Marvin Mc Cutchan, Gerhard Navratil, and Ioannis Giannopoulos. Unfolding Urban Structures: Towards Route Prediction and Automated City Modeling (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 26:1-26:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{fogliaroni_et_al:LIPIcs.GISCIENCE.2018.26,
  author =	{Fogliaroni, Paolo and Mc Cutchan, Marvin and Navratil, Gerhard and Giannopoulos, Ioannis},
  title =	{{Unfolding Urban Structures: Towards Route Prediction and Automated City Modeling}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{26:1--26:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.26},
  URN =		{urn:nbn:de:0030-drops-93548},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.26},
  annote =	{Keywords: intersection types, spatial structure, spatial modeling, graph theory}
}
Document
Short Paper
Geospatial Semantics for Spatial Prediction (Short Paper)

Authors: Marvin Mc Cutchan and Ioannis Giannopoulos

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)


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.

Cite as

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)


Copy BibTex To Clipboard

@InProceedings{mccutchan_et_al:LIPIcs.GISCIENCE.2018.45,
  author =	{Mc Cutchan, Marvin and Giannopoulos, Ioannis},
  title =	{{Geospatial Semantics for Spatial Prediction}},
  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 =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.45},
  URN =		{urn:nbn:de:0030-drops-93731},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.45},
  annote =	{Keywords: Geospatial semantics, spatial prediction, machine learning, Linked Data}
}
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