How mobility gives language models a deeper

What happened

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.

To build a model with a deeper understanding of places, we must distinguish between a place's identity (its name and category) and its function (its aggregated visit footprint). In prior geospatial AI research works, mobility patterns were almost exclusively applied to predicting the next POI a user will visit. In contrast, the ME-POIs framework shifts mobility from an output prediction task to an input feature that defines the place itself.

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. A persistent challenge in geospatial data science is the "long tail" problem.

While famous landmarks, massive shopping malls, and popular downtown chains generate an abundance of visit data, the vast majority of local businesses — small neighborhood boutiques, specialized repair shops, or newly opened cafes — suffer from severe data sparsity. Previously, when a model encountered a place with few or no recorded visits, it would incorrectly assume the place had zero activity, leading to broken predictions.

ME-POIs solves this through a novel spatial multiscale visit propagation mechanism. The architecture recognizes that visits are usually regionally constrained; a small boutique on a high-end shopping street shares systemic behavioral traits with its neighbors. The framework looks at adjacent places across multiple spatial scales: the immediate street, the block, and the wider neighborhood. It then statistically transfers the aggregated visit patterns of busy, data-rich neighbors to nearby sparse places.

Why it matters

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.

7% increase in busyness estimation accuracy across unseen places. 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.

What to watch

By learning the regional "rhythm" from active areas, the model applies an intelligent geographical prior knowledge base to smaller shops, enabling it to learn something about them even when they have few to no appearances in the data. Rather than discarding textual descriptions, the ME-POIs framework enriches them.

It does so by aligning high-level language embeddings (the standard vector representations extracted from advanced models like Gemini) with the newly generated mobility vectors by maximizing their cosine similarity. We layer the mobility signal directly on top of the language representation, creating a more holistic view of the place or business. , knowing whether it functions as a lunch spot or a late-night diner from its mobility signature).