How mobility gives language models a deeper understanding of place

We introduce a dynamic, mobility-informed framework that allows AI models to understand the temporal activity rhythms of places over time, and, in doing so, significantly improve predictions about real-world attributes like opening hours, price levels, and busyness.

Artificial intelligence has made incredible progress in understanding the world through text. However, to build AI models that truly understand the physical world, they must comprehend more than just words: they need to capture the dynamic, real-world functionality of the built environment. Every place has two distinct signatures: its identity on paper, and its actual functional rhythm.

Traditional language models typically build representations of places (commonly referred to as “points of interest” or POIs), whether it’s a business or a place like a park or landmark, by relying heavily on this static metadata. They successfully analyze addresses, business categories, and text descriptions. While world-class language models like Gemini are incredibly proficient at processing text data, their geospatial representations can be significantly enriched by incorporating the real-world functional dynamics of the urban environment. Complementing semantic labels with mobility data can enable these models to effectively capture the unique temporal activity rhythms of POIs in a city.

How the ME-POIs framework works

By providing a pre-enriched representation of a place, the ME-POIs framework makes it vastly easier for AI models to draw accurate inferences about many distinct attributes — such as operating hours, target price levels, and current business status — without needing to calculate those attributes from scratch every single time.

But how do you transform raw geographic points into a clean and numerically useful mathematical signature (or embedding)? We accomplish this through a three-step pipeline: visit alignment, spatial multiscale visit propagation, and text-mobility synergy.