Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach
Journal:
arXiv
Published Date:
May 16, 2025
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.
