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.2018.73
URN: urn:nbn:de:0030-drops-94016
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9401/
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Zhu, Di ; Liu, Yu

Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper)

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LIPIcs-GISCIENCE-2018-73.pdf (1 MB)

Abstract

The understanding of geographical reality is a process of data representation and pattern discovery. Former studies mainly adopted continuous-field models to represent spatial variables and to investigate the underlying spatial continuity/heterogeneity in a regular spatial domain. In this article, we introduce a more generalized model based on graph convolutional neural networks that can capture the complex parameters of spatial patterns underlying graph-structured spatial data, which generally contain both Euclidean spatial information and non-Euclidean feature information. A trainable site-selection framework is proposed to demonstrate the feasibility of our model in geographic decision problems.

BibTeX - Entry

@InProceedings{zhu_et_al:LIPIcs:2018:9401,
  author =	{Di Zhu and Yu Liu},
  title =	{{Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{73:1--73:7},
  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/2018/9401},
  URN =		{urn:nbn:de:0030-drops-94016},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.73},
  annote =	{Keywords: Spatial pattern, Graph convolution, Big geo-data, Deep neural networks, Urban configuration}
}

Keywords: Spatial pattern, Graph convolution, Big geo-data, Deep neural networks, Urban configuration
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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