Web-Centric Human Mobility Analytics: Methods, Applications, and Future Directions in the LLM Era

Human mobility analytics is essential to enabling a broad range of web-related applications, such as navigation, urban planning, and point-of-interest (POI) recommendation. The proliferation of mobility data, including geo-social media check-ins and geo-location data, offers unprecedented opportunities for analyzing human mobility. This lecture-style tutorial offers an in-depth look at web-centric human mobility analytics, organized according to three levels: location-level, individual-level, and macro-level. Location-level analytics focus on spatial activities within specific geographical locations, using points of interest and other data to forecast future visits and identify urban mobility patterns. Individual-level analytics delve into the movements of individuals, e.g., considering sequences of visited locations over time, elucidating individual movement behaviors. Macro-level analytics broaden the scope of analyses to include large-scale spatial patterns and population flows across regions, offering a macro perspective on mobility. The tutorial encompasses cutting-edge learning frameworks such as federated learning as well as continual learning and innovative applications of Large Language Models (LLMs), which enhance predictive analytics and expand the capabilities of mobility analysis. The tutorial aims to afford the participants a comprehensive overview of the current state and future directions of web-centric human mobility analytics, making it an invaluable resource for using web-sourced human mobility data to facilitate a more informed and interconnected world. The video teaser is available at https://shorturl.at/HShNc. © 2025 held by the owner/author(s). Publication rights licensed to ACM.