Learning from Spatio-Temporal Data in the LLM Era: Foundations, Models, and Emerging Trends

Abstract

Spatio-temporal data are foundational to understanding and modeling dynamic real-world phenomena such as human mobility, traffic flow, epidemic spread, and urban dynamics. With the growing availability of location-aware web data and the rise of intelligent urban infrastructures, analyzing spatio-temporal patterns has become both highly valuable and technically challenging. This tutorial provides a comprehensive overview of spatio-temporal data analytics, unifying perspectives from data management, research methodology, and emerging foundation models. We begin with a review of spatio-temporal data management systems, introducing the core data models, spatial-temporal indexing techniques, and scalable architectures for storing and querying large-scale mobility data. We then delve into trajectory learning, covering methods for prediction, generation, and reconstruction of movement sequences at the individual level. Next, we explore spatio-temporal graph learning, which focuses on forecasting region-level dynamics using dynamic graph neural networks. Multi-region, multi-task, and multi-domain spatio-temporal learning will be identified and introduced in detail. Finally, we present advanced learning frameworks that integrate federated learning, continual learning, and LLM-based approaches to build privacy-preserving, scalable, and adaptive spatio-temporal models. Through the lens of recent methodological and system-level advances, this tutorial bridges algorithmic design and practical deployment of spatio-temporal learning systems. It is suitable for researchers and practitioners working in machine learning, data mining, geospatial analysis, and intelligent systems.