Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction

TL;DR

This paper introduces a novel multi-source spatial point data prediction framework that uses a fidelity score and a geo-location-aware graph neural network to effectively integrate heterogeneous data sources without ground truth labels, outperforming existing methods.

Contribution

The paper presents a new framework combining a fidelity score and geo-location-aware graph neural networks for multi-source spatial data prediction without ground truth labels.

Findings

Outperforms state-of-the-art methods on real-world datasets

Effectively evaluates data source reliability using fidelity scores

Accurately models spatial relationships with geo-location-aware GNNs

Abstract

Multi-source spatial point data prediction is crucial in fields like environmental monitoring and natural resource management, where integrating data from various sensors is the key to achieving a holistic environmental understanding. Existing models in this area often fall short due to their domain-specific nature and lack a strategy for integrating information from various sources in the absence of ground truth labels. Key challenges include evaluating the quality of different data sources and modeling spatial relationships among them effectively. Addressing these issues, we introduce an innovative multi-source spatial point data prediction framework that adeptly aligns information from varied sources without relying on ground truth labels. A unique aspect of our method is the 'fidelity score,' a quantitative measure for evaluating the reliability of each data source. Furthermore, we develop a geo-location-aware graph neural network tailored to accurately depict spatial relationships between data points. Our framework has been rigorously tested on two real-world datasets and one synthetic dataset. The results consistently demonstrate its superior performance over existing state-of-the-art methods.