How mobility gives language models a deeper understanding of place

Summary

Google Research researchers introduce Mobility-Embedded POIs (ME-POIs), a dynamic, mobility-informed framework that enhances language models' understanding of places by integrating aggregate, anonymized mobility patterns such as arrival times, stay durations, and surrounding movement. This self-supervised approach blends text descriptions with large-scale mobility data to create embeddings that capture both a place's identity and its dynamic functionality, improving predictions of attributes like opening hours, price levels, and busyness. The framework demonstrates that complementing semantic labels with mobility data significantly enriches geospatial representations, offering a deeper understanding of the built environment.