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DOI: 10.4230/LIPIcs.GISCIENCE.2018.73

Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper)

Authors: Di Zhu and Yu Liu

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

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.

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Di Zhu and Yu Liu. Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 73:1-73:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{zhu_et_al:LIPIcs.GISCIENCE.2018.73,
  author =	{Zhu, Di and Liu, Yu},
  title =	{{Modelling Spatial Patterns Using Graph Convolutional Networks}},
  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 =	{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.2018.73},
  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}
}

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Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper)

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