Urban Pattern

Urban pattern research focuses on understanding and modeling the spatial and temporal distribution of urban elements, aiming to improve urban planning and management. Current research utilizes diverse data sources, including satellite imagery (nighttime lights), and human mobility data, analyzed through machine learning techniques like neural networks, generative adversarial networks (GANs), and tensor factorization to identify and predict urban patterns, including informal settlements and transportation indices. These analyses contribute to a more nuanced understanding of urban dynamics, informing sustainable urban development strategies and enabling more effective responses to challenges like traffic congestion and unequal resource distribution.