Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks
This provides a tool for geographic decision-making by addressing irregular spatial data analysis, but it is incremental as it applies an existing GCN method to a new domain.
The authors tackled the problem of modeling irregular spatial patterns by introducing a graph convolutional neural network (GCN) model to capture complex parameters in graph-structured spatial data, demonstrating its feasibility for analyzing intra-urban POI check-ins.
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.