Tag
This paper proposes UrbanAgent, an agentic framework that reframes urban region profiling as a reasoning-driven inference problem using multi-agent collaborative reasoning and tool-augmented evidence retrieval. It outperforms baselines on global urban datasets for carbon emissions, GDP, and population estimation, achieving an average 8.1% improvement in R².
The author uses a photo of scuff marks on a subway station wall to estimate the height distribution of commuters, applying image processing and a heuristic body-to-scuff ratio, and discusses potential improvements using Bayesian methods.
Introduces CausalPOI, a spatio-temporal graph-based causal representation learning framework for cold-start POI check-in forecasting, which outperforms state-of-the-art baselines on real-world SafeGraph datasets.