Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction

Understand

Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making.

• Existing studies leverage knowledge graphs (KG) to model heterogeneous urban data, and further apply graph representation learning methods for socioeconomic prediction.
• However, these approaches heavily rely on heuristic ideas and expertise to extract task-relevant knowledge from diverse data, which may not be optimal for specific tasks.
• Additionally, they tend to overlook the inherent relationships between different indicators, limiting the prediction accuracy.