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

TL;DR

This paper introduces SemiGTX, a semi-supervised graph-based framework for fine-grained urban economic mapping that effectively integrates diverse geospatial data and performs multi-sector analysis, outperforming existing methods.

Contribution

The paper presents SemiGTX, a novel semi-supervised, multi-task graph learning framework that unifies various geospatial data modalities for comprehensive economic sector mapping.

Findings

Achieves high R2 scores of 0.93, 0.96, and 0.94 for primary, secondary, and tertiary sectors.

Demonstrates superior performance over existing methods in the Pearl River Delta region.

Shows generalizability through cross-regional experiments in Beijing and Chengdu.

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.