How mobility gives language models a deeper understanding of place

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.

To demonstrate this complementary capability, we introduce Mobility-Embedded POIs (ME-POIs), a novel framework that improves text-based place representations derived by language models. Using publicly available benchmark datasets, ME-POIs incorporates aggregated and anonymized mobility patterns, such as arrival times, stay durations, and surrounding movement patterns. Rather than treating a place as a frozen set of words, ME-POIs use a self-supervised approach to blend text descriptions with large-scale, anonymized mobility patterns from public benchmarks (capturing the aggregate spatial activity footprints of the environment throughout the day). In doing so, the model constructs a numerical vector representation (a mathematical “signature”, technically called an embedding) that encodes both the identity of a place and its dynamic functionality. Integrating ME-POIs with advanced text models delivered a context advantage that yielded up to an 81.9% relative gain in predicting visit intent, a 75.1% improvement in price level classification, and a 24.7% increase in busyness estimation accuracy across unseen places.