Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs

2024-05-23

AI Summary

High acquisition costs and extreme sparsity of global socioeconomic indicators—coupled with the limitations of conventional spatial interpolation methods, which rely on strong assumptions of spatial continuity and thus fail to capture complex, discontinuous intra-regional variations—pose significant challenges for sustainable urban and human settlement management. To address this, we propose the Geographically Heterogeneous Graph (GeoHG) modeling framework. GeoHG is the first to integrate spatially aware mechanisms into heterogeneous graph neural networks, explicitly modeling discontinuous and heterogeneous spatial relationships by disentangling multi-source semantic associations in geographic space, encoding spatial topology, and leveraging masked reconstruction learning. Under an extreme 95% sampling sparsity, GeoHG achieves an R² exceeding 0.81—substantially outperforming classical interpolation methods and state-of-the-art graph-based models. This work establishes a scalable, robust paradigm for inferring socioeconomic indicators at globally low sampling rates.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

Regional socioeconomic indicators are critical across various domains, yet their acquisition can be costly. Inferring global socioeconomic indicators from a limited number of regional samples is essential for enhancing management and sustainability in urban areas and human settlements. Current inference methods typically rely on spatial interpolation based on the assumption of spatial continuity, which does not adequately address the complex variations present within regional spaces. In this paper, we present GeoHG, the first space-aware socioeconomic indicator inference method that utilizes a heterogeneous graph-based structure to represent geospace for non-continuous inference. Extensive experiments demonstrate the effectiveness of GeoHG in comparison to existing methods, achieving an $R^2$ score exceeding 0.8 under extreme data scarcity with a masked ratio of 95%.

Problem

• Infer global socioeconomic indicators
• Address complex spatial variations
• Utilize heterogeneous graph structure

Innovation

• Heterogeneous graph-based structure
• Space-aware inference method
• Non-continuous geospace representation