Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks

TL;DR

This paper introduces a graph convolutional neural network framework to model and analyze complex, irregular spatial patterns in geographic data, especially for intra-urban points of interest, demonstrating its effectiveness in geographic decision-making.

Contribution

The paper presents a novel GCN-based model that captures both Euclidean and non-Euclidean spatial features for irregular spatial data analysis.

Findings

GCNs effectively model complex spatial patterns.

The framework improves prediction of intra-urban POI check-ins.

Demonstrates potential for geographic decision support.

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.