Unravelling Urban Complexity: Harnessing Open Network Analytics and Data for Informed City Planning and Design

This research explores the potential of urban networks to model, analyse, and visualise the structure and flow of complex urban systems. It makes three primary contributions: 1) re-evaluating the theoretical foundations of urban networks through the integration of graph machine learning, urban complexity, and network sciences; 2) harnessing real-world datasets, including volunteered geographic and remote sensing open data, for city scale urban sensing and multi-dimensional representation learning on urban graphs; and 3) developing practical deep learning based workflows for evidence-based planning and urban sustainability.

Through a data-driven perspective, the study aims to advance an understanding of the structure and dynamics of urban networks at an unprecedented scale and resolution.