Learning from Spatio-Temporal Data in the LLM Era: Foundations, Models, and Emerging Trends
Year: 2025
Type: conference-paper
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 systemlevel 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.
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G8299714314
OpenAlex ID: W4415149554