From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places

Capturing human mobility is essential for modeling how people interact with and move through physical spaces, reflecting social behavior, access to resources, and dynamic spatial patterns. To support scalable and transferable analysis across diverse geographies and contexts, there is a need for a generalizable foundation model for spatiotemporal data. This paper envisions a new approach for encoding geolocation and mobility data to learn general-purpose place embeddings, forming a core component of spatial foundation models that integrate geolocation semantics with human mobility across multiple scales. Our central idea involves shifting from modeling discrete points of interest to understanding places: dynamic, context-rich regions shaped by human behavior and mobility that may comprise many places of interest. Our goal is to guide the development of scalable, context-aware foundation models for next-generation geospatial intelligence. These models unlock powerful applications ranging from personalized place discovery and logistics optimization to urban planning, ultimately enabling smarter and more responsive spatial decision-making.