Song Zimeng is a PhD student with a background in architecture and urban planning, and prior experience in municipal housing and planning departments in a major city. These roles provided practical exposure to policy and implementation.

Song holds two master’s degrees in urban analytics, from the University of Sheffield and UCL, focusing on quantitative, computational and also qualitative approaches to urban studies.

Current interests include urban big-data modeling, machine learning, and GeoAI. Song's research area covers urban spatial structure and infectious diseases (modeling and forecasting transmission), urban inequality, 15-minute city, and related urban socioeconomic issues. The goal is to develop analyses that may support evidence-informed planning and policy.

Research

Thesis title: ''Optimising Urban Spatial Structure for Pandemic Resilience: Built Environment, Segregation and Health Inequality'

PhD supervision

• Principal supervisor: Dr Zahratu Shabrina
• Secondary supervisor: Dr Daoping Wang

Further details

See Song's research profile