Geography and Planning - Analysing urban mobility patterns for more inclusive and sustainable cities

Supervisor: Dr Carmen Cabrera-Arnau

Supervisor bio: Carmen Cabrera-Arnau is a Lecturer in Geographic Data Science (Assistant Professor) at the Geographic Data Science Lab within the University of Liverpool’s Department of Geography and Planning. Her areas of expertise are geographic data science, human mobility, network analysis and mathematical modelling. Carmen’s research focuses on developing quantitative frameworks to model and predict human mobility patterns across spatiotemporal scales and population groups, ranging from intraurban commutes to migratory movements. She is particularly interested in establishing methodologies to facilitate the efficient and reliable use of new forms of digital trace data in the study of human movement.

Email: c.cabrera-arnau@liverpool.ac.uk

School: Environmental Sciences

Department: Geography and Planning

Module code: ENVS290

Suitable for students of: Computer Science, Physics, Applied Mathematics, Engineering, Geography, Economics, Statistics, Urban Planning

Desired experience or requirements:

• Experience with computer programming in Python or R
• Quantitative background
• Open to learning new, technically challenging skills
• Interested in social science questions

Places available: 2

Start dates: 16 June 2025 only

Project length: 8 weeks 

Virtual option: Yes - In Person, Hybrid, and Virtual

Project description:

This summer research project invites motivated undergraduate students to contribute to cutting-edge research at the intersection of human mobility, data science, spatial analysis, urban planning, and social justice. The project aims to uncover how face-to-face interactions between diverse population groups in urban settings influence social cohesion and inclusivity. To achieve this, the research will leverage anonymised location data from mobile phone users, known as eXtended Data Records (XDRs), which provide detailed insights into human mobility at fine spatial and temporal scales. The project will focus on developing methods to estimate the likelihood and frequency of interactions between different social groups based on their movement patterns and geographic distribution. The results of the mobility analysis based on XDRs will then be integrated with additional datasets, such as area-level demographic data and survey-based measures of intergroup attitudes. This integrated approach will enable an exploration of how urban mobility patterns shape opportunities for intergroup contact and contribute to social outcomes such as tolerance, prejudice, or segregation. Candidates will gain hands-on experience using Python or R for data analysis, statistical modelling, and data visualization, while developing their understanding of how mobility data can inform key social science questions. They will also learn to process and analyse large-scale digital trace data, a valuable skill in today’s job market. This expertise can help us address critical, meaningful questions about urban inclusivity and mobility. The project has therefore potential to contribute to policies that promote more socioeconomically sustainable cities, equipping students with both technical expertise and a nuanced perspective on the role of urban mobility in shaping a more equitable society.

Additional requirements: N/A