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.
| Original language | English (US) |
|---|---|
| Title of host publication | 10th International Conference on Geographic Information Science, GIScience 2018 |
| Editors | Amy L. Griffin, Stephan Winter, Monika Sester |
| Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |
| ISBN (Print) | 9783959770835 |
| DOIs | |
| State | Published - Aug 1 2018 |
| Externally published | Yes |
| Event | 10th International Conference on Geographic Information Science, GIScience 2018 - Melbourne, Australia Duration: Aug 28 2018 → Aug 31 2018 |
Publication series
| Name | Leibniz International Proceedings in Informatics, LIPIcs |
|---|---|
| Volume | 114 |
| ISSN (Print) | 1868-8969 |
Other
| Other | 10th International Conference on Geographic Information Science, GIScience 2018 |
|---|---|
| Country/Territory | Australia |
| City | Melbourne |
| Period | 8/28/18 → 8/31/18 |
Bibliographical note
Funding Information: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].
Publisher Copyright:
© Di Zhu and Yu Liu.
Keywords
- Big geo-data
- Deep neural networks
- Graph convolution
- Spatial pattern
- Urban configuration