Exploring city dynamics through tweets: A framework for capturing urban activities as complex spatiotemporal patterns

Abstract

This paper presents a novel framework for analysing urban activities as spatiotemporal patterns using Location-Based Social Media (LBSM) data. The methodology integrates the spatial, temporal, and semantic dimensions of geolocated tweets to investigate cities as Complex Adaptive Systems (CAS) and their relationship with urban form. By combining spatiotemporal clustering (ST-DBSCAN) and topic modelling (LDA), the framework uncovers dynamic activity patterns shaped by top-down mechanisms and bottom-up self-organizing behaviours. A custom tool and Graphical User Interface was developed to support data exploration and experimentation, enabling the contextual analysis of activity clusters. The framework was tested in Manchester City Centre as an exploratory case study, focusing on the impact of Covid-19 lockdown measures as a significant disturbance. The results reveal how urban characteristics, urban form, and social behaviours influence activity levels and patterns, demonstrating fluctuations that highlight different degrees of adaptability. By exploring cities as hybrid urban-digital spaces, this approach provides an alternative approach for understanding cities as CAS, linking space to place and for exploring adaptive behaviour. The paper concludes by reflecting on the framework, use of LBSM for researching cities, and outlining directions for future work of comparing cities and integrating alternative data.