Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach

2025-05-16

AI Summary

To address data scarcity, fragmented multi-task modeling, and limited interpretability in fine-grained regional economic mapping, this paper proposes SemiGTX, a semi-supervised graph neural network framework. SemiGTX introduces a novel semi-informed loss function that jointly optimizes spatial self-supervision and local masked regression supervision. It establishes a unified multi-task graph learning architecture to simultaneously model the spatial distributions of GDP across primary, secondary, and tertiary industries while enabling cross-modal attribution analysis. Evaluated on the Pearl River Delta, SemiGTX achieves R² scores of 0.93, 0.96, and 0.94 for the three sectors, respectively. Cross-regional validation on Beijing and Chengdu demonstrates significant performance gains over state-of-the-art methods. The framework delivers high accuracy, strong generalizability across diverse urban contexts, and intrinsic interpretability through its integrated supervision and attribution mechanisms.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

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: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Fine-grained economic mapping through urban representation learning has emerged as a crucial tool for evidence-based economic decisions. While existing methods primarily rely on supervised or unsupervised approaches, they often overlook semi-supervised learning in data-scarce scenarios and lack unified multi-task frameworks for comprehensive sectoral economic analysis. To address these gaps, we propose SemiGTX, an explainable semi-supervised graph learning framework for sectoral economic mapping. The framework is designed with dedicated fusion encoding modules for various geospatial data modalities, seamlessly integrating them into a cohesive graph structure. It introduces a semi-information loss function that combines spatial self-supervision with locally masked supervised regression, enabling more informative and effective region representations. Through multi-task learning, SemiGTX concurrently maps GDP across primary, secondary, and tertiary sectors within a unified model. Extensive experiments conducted in the Pearl River Delta region of China demonstrate the model's superior performance compared to existing methods, achieving R2 scores of 0.93, 0.96, and 0.94 for the primary, secondary and tertiary sectors, respectively. Cross-regional experiments in Beijing and Chengdu further illustrate its generality. Systematic analysis reveals how different data modalities influence model predictions, enhancing explainability while providing valuable insights for regional development planning. This representation learning framework advances regional economic monitoring through diverse urban data integration, providing a robust foundation for precise economic forecasting.

Problem

• Addresses data-scarce semi-supervised economic mapping
• Unifies multi-task sectoral GDP prediction
• Enhances explainability with diverse geospatial data

Innovation

• Semi-supervised graph learning framework
• Fusion encoding for geospatial data
• Multi-task GDP mapping model