Name
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 the regular spatial domain. In this article, we introduce a more generalized model based on graph convolutional neural networks (GCNs) 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 semi-supervised prediction framework is proposed to model the spatial distribution patterns of intra-urban points of interest(POI) check-ins. This work demonstrates the feasibility of GCNs in complex geographic decision problems and provides a promising tool to analyze irregular spatial data. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Spatial analysis,Decision problem,External Data Representation,Pattern recognition,Convolutional neural network,Artificial intelligence,Euclidean geometry,Point of interest,Spatial ecology,Mathematics,Machine learning,Spatial distribution |
DocType | Volume | Citations |
Journal | abs/1808.09802 | 0 |
PageRank | References | Authors |
0.34 | 3 | 2 |