Positional Encoder Graph Neural Networks for Geographic Data

2021/11/19 by Konstantin Klemmer, Klemmer, Konstantin, Nathan Safir +3 · 3 citations
Computer Science · Social Sciences · #Advanced Graph Neural Networks #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geographic Information Systems Studies #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2111.10144

openalex publication_date 2021/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, they often rely on Euclidean distances to construct the input graphs. This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e.g., road networks). Here, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary task learning and semantic spatial embeddings, our proposed method (1) learns a context-aware vector encoding of the geographic coordinates and (2) predicts spatial autocorrelation in the data in parallel with the main task. On spatial interpolation and regression tasks, we show the effectiveness of our approach, improving performance over different state-of-the-art GNN approaches. We observe that our approach not only vastly improves over the GNN baselines, but can match Gaussian processes, the most commonly utilized method for spatial interpolation problems.

Citations

• Sparse spatial autoregressions
• Local Indicators of Spatial Association—LISA
• Semi-Supervised Classification with Graph Convolutional Networks
• Reinforcement Learning with Unsupervised Auxiliary Tasks
• Inductive Representation Learning on Large Graphs
• Fast Graph Representation Learning with PyTorch Geometric
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

Cited by

• RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation
• Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports
• On the use of Graphs for Satellite Image Time Series

Related

• Positional Encoder Graph Quantile Neural Networks for Geographic Data
• Auxiliary-task learning for geographic data with autoregressive\n embeddings
• Neural networks for geospatial data
• Augmenting correlation structures in spatial data using deep generative models
• Modelling Irregular Spatial Patterns using Graph Convolutional Neural Networks