Zicheng Fan is a PhD candidate in the Urban Analytics Lab. He holds masters’ degree in space syntax theory and spatial data science. His research interest lies in the intersection of urban morphology, spatial perception, and machine learning. Zicheng has experience in investigating Covid-19’s impact on noise complaints in London, with time-series clustering and multiple machine learning methods. He has also contributed to investigating and simulating pedestrians’ visual interest in 3D built environments via isovist, street view images and gaze analysis. In UAL, Zicheng hopes to explore the wide application of 3D GIS and digital twins in revealing various people-space interactions in 3D built environment and urban dynamic scenarios.