SemiGPS: GraphGPS-based Semi-supervised Graph Learning for Sector-Specific GDP Mapping

Abstract

Accurate forecasting of granular socioeconomic indicators, such as GDP, is essential for informed economic decision-making. Despite pioneering efforts to harness multi-modal data using traditional supervised or self-supervised learning methods for economic prediction, effectively integrating their features remains a significant hurdle. To address the challenge, we propose a semi-supervised enhanced graph learning framework, SemiGPS, which leverages multi-modal geospatial big data to extract features linked to regional economic development. By integrating multiple data modalities, such as street view images and POIs, and adopting a semi-supervised learning paradigm, SemiGPS strikes a balance between spatial-interacted learning from self-supervision and the fitting capabilities from supervised learning, thereby empowering the model to learn more informative and effective representations. Experiments in the Pearl River Delta region of China demonstrate the effectiveness of our approach, with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores of 0.85, 0.95, and 0.88 for the primary, secondary, and tertiary sectors, respectively. Our results highlight the potential of SemiGPS to capture nuanced regional economics and pave the way for more accurate economic forecasting using geospatial data.