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

Dazhou Yu, Xiaoyun Gong, Yun Li, Meikang Qiu, Liang Zhao

TL;DR

The paper tackles multi-source spatial point data prediction under limited ground truth by introducing Deep Multi-source Spatial Prediction (DMSP). It combines a self-supervised mutual-information objective with learnable fidelity scores to weigh heterogeneous data sources and a geo-location-aware multi-source graph neural network to model spatial relationships. Key contributions include a novel fidelity score mechanism, a shared spatial relation encoder, and source-specific GNN convolutions, validated across real and synthetic datasets with ablations. Results show consistent improvements over state-of-the-art methods and provide practical insights into data source quality and spatial integration, with scalable training and robust hyperparameter behavior.

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.

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

TL;DR

The paper tackles multi-source spatial point data prediction under limited ground truth by introducing Deep Multi-source Spatial Prediction (DMSP). It combines a self-supervised mutual-information objective with learnable fidelity scores to weigh heterogeneous data sources and a geo-location-aware multi-source graph neural network to model spatial relationships. Key contributions include a novel fidelity score mechanism, a shared spatial relation encoder, and source-specific GNN convolutions, validated across real and synthetic datasets with ablations. Results show consistent improvements over state-of-the-art methods and provide practical insights into data source quality and spatial integration, with scalable training and robust hyperparameter behavior.

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.
Paper Structure (33 sections, 2 theorems, 10 equations, 5 figures, 3 tables, 1 algorithm)

This paper contains 33 sections, 2 theorems, 10 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Formulation
  4. Methodology
  5. Adaptive Self-supervised Fusion of Multiple Data Sources
  6. Training Procedure and Framework
  7. Self-consistent Training Procedure
  8. Deep Multi-source Spatial Prediction
  9. Geo-location-aware Graph Neural Networks
  10. Spatial K-Nearest Neighbor Graph
  11. Spatial Relationship Encoder
  12. Graph Convolution Operators
  13. Experiment
  14. Experiment Setting
  15. Dataset
  16. ...and 18 more sections

Key Result

Theorem 4.1

For two random variables $Y^{(i)}, \hat{Y}\in \mathbb{R}$, any variational approximation of the conditional distribution $p_{Y^{(i)} \vert \hat{Y}}$ will increase the conditional entropy $H(Y^{(i)}\vert \hat{Y})$.

Figures (5)

  • Figure 1: An example of multi-source spatial point prediction problem: Varying distribution of three data sources including (A) 74 AQMSs, (B) 3704 LASS AirBox sensors, and (C) 9701 EPA MicroStations.
  • Figure 2: Illustration of the DMSP framework. For a target location $s$, the process involves: (a) building a KNN graph for each data source and using a shared spatial relationship encoder for spatial representations as edge features; (b) applying distinct graph convolution operators for updating node representations per data source; (c) employing a shared decoder for outputting predictions, which are fused by $f$ for the final prediction. (d) shows the self-consistent training procedure of the framework.
  • Figure 3: Visualization for the prediction and approximate ground truth for (a) SouthCal and (b) SCR
  • Figure 4: The scalability study about the number of samples.
  • Figure 5: The sensitivity study of two hyperparameters in DMSP on the SCR dataset. The dashed lines represent the average baseline performance.

Theorems & Definitions (2)

  • Theorem 4.1
  • Theorem 4.2