Hover your mouse over a neighbourhood to see potential for social interaction with residents of nearby neighbourhoods — how easy is it to meet up with other urban residents by various modes of transportation?
Urban Mobility Research Group @ CASA UCL
We study the rhythms of city life through the digital footprints people leave behind. Our work combines human mobility data, GeoAI, and spatial simulations to explore urban change, assess policy impacts, and support the planning of sustainable and inclusive futures in global cities.
Emerging Human Location Data
We focus on analysing individual-level human mobility data—including travel card records, mobile phone data, and geolocated social media—to gain nuanced insights into how people move through and interact with urban environments.
Cutting-Edge GeoAI Methods
Drawing from spatial data science, machine learning, deep learning, spatial network analysis, and causal inference, we develop innovative analytical frameworks and simulation models to detect mobility patterns, predict demand, and model urban futures.
City Planning Scenarios
Our case studies span diverse urban contexts—including London, Shenzhen, and Nairobi. Addressing pressing urban challenges, we are particularly focused on socio-spatial inequality, transport decarbonisation, and the evolution of urban spatial structure.