Urban Analytics

Urban analytics leverages diverse data sources, including street view imagery, sensor networks, and open data, to understand and model complex urban phenomena. Current research focuses on developing advanced machine learning models, such as graph attention networks, transformers, and 3D convolutional neural networks, to analyze these data and predict various urban processes, including traffic flow, human mobility, and environmental hazard exposure. These advancements enable more accurate and efficient urban planning, resource allocation, and policy development, ultimately improving urban sustainability and livability. The field also emphasizes addressing data biases and privacy concerns inherent in using large-scale urban datasets.