The module’s objective is to familiarise students with the modelling of urban systems and their analytical applications. Two major techniques will be introduced in this course: Network Science and Agent-based Modeling (ABM). In the Network Science section, students will learn the basic concepts and recent developments in network science and its applications in urban studies. They will learn how to represent various urban systems using a graph structure and apply advanced machine-learning techniques, including Graph Neural Networks (GNN) and Computer Vision (CV) techniques, to analyse the urban system, including street networks and mobility patterns. For the ABM part of the course, students are expected to understand the core concepts of ABM, including agents, environments, and behaviours. They will design and implement ABM for urban systems using tools like NetLogo or Mesa, simulate models, and analyse outcomes to study urban dynamics such as human dynamics and urban development. Additionally, students are expected to integrate advanced machine learning tools such as GNN and CV techniques in their projects. Prerequisites: basic knowledge of Python and GIS software.