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Modelling Spatial Patterns Using Graph Convolutional Networks (Short Paper)

Authors Di Zhu , Yu Liu

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Author Details

Di Zhu
  • Institute of Remote Sensing and Geographical Information Systems, Peking University, 5th Yiheyuan Road, Beijing, China
Yu Liu
  • Institute of Remote Sensing and Geographical Information Systems, Peking University, 5th Yiheyuan Road, Beijing, China

Funding

This research was supported by the National Key Research and Development Program of China [Grant Number: 2017YFB0503602] and the National Natural Science Foundation of China [Grant Number: 41625003].

Cite AsGet BibTex

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)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.73

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.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • Spatial pattern
  • Graph convolution
  • Big geo-data
  • Deep neural networks
  • Urban configuration

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