Understanding Urban Vibrancy and Third Places: A Computational Study of the Social Life of Cities Through Multi-Source Digital Trace Data

Urban vibrancy is a central concept in understanding how cities function socially, reflecting patterns of activity, interaction, and engagement within urban space. Although widely discussed in urban theory and planning, vibrancy remains difficult to observe directly and is typically examined through indirect measures such as population presence, mobility,and land-use characteristics. Recent advances in digital technologies have generated large-scale behavioural datasets that offer new opportunities to study these dynamics at high spatial and temporal resolution. However, the social dimensions of urban life beyond residential and workplace settings—particularly those associated with third places—remain comparatively under-explored in computational urban research. This thesis investigates how large-scale digital data can be integrated with urban theory to improve understanding of urban activity patterns and their relationship with third places across different spatial and temporal contexts. Drawing on mobile phone Call Detail Records, app usage data, OpenStreetMap features, and street-level imagery,the thesis develops a series of complementary empirical studies examining gendered activity patterns, national-scale behavioural structures, and the visual and perceptual characteristics of urban environments. Spatial econometric models, multivariate time series clustering, convolutional neural networks, and geographically weighted regression are employed to analyse how socially meaningful places, mobility patterns, and urban form relate to observed activity levels. The findings demonstrate that incorporating measures of third places and social infrastructure enhances the interpretability and spatial sensitivity of models of urban activity and vibrancy. Results reveal systematic gender differences associated with the distribution of socially active spaces, consistent multi-cluster structures in national-scale app usage patterns, and context-dependent relationships between urban appearance,outdoor environments, and activity intensity. Together, these analyses show that diversity and configuration of social spaces are more informative than simple density measures for understanding urban vibrancy. The thesis also critically reflects on the limitations of using digital traces as proxies for social interaction, acknowledging constraints related to data bias, representativeness,and causal inference. By integrating multiple data sources and analytical approaches, this research contributes a more nuanced and theoretically grounded framework for studying urban vibrancy at scale. The findings provide evidence to support urban planning and policy interventions aimed at sustaining diverse, accessible, and socially meaningful urban environments in contemporary digitally mediated cities.<p></p>

Author dc:creator

• Thomas Collins (21059351)

• Computational Social Science
• Geospatial Analysis
• Mobile Phone Data
• Spatial Modelling
• Third Places
• Urban Analytics
• Urban Vibrancy

Statement dc:rights

• All rights reserved

Identifier

10779/exe.32775114.v1

OAI identifier oai:identifier

oai:figshare.com:article/32775114