Integrating AI into urban vitality promotion: The impact of building morphology on daily consumption quality in Hong Kong

The urban evolution of Hong Kong necessitates innovative approaches to enhance urban development. This study explores the integration of AI into urban planning and design, focusing on improving consumption quality as a key metric of urban vitality. Consumption quality is defined as the quality of the urban consumption environment, measured by user ratings of commercial points of interest, and serves as a key indicator of urban vitality. This study fills a research gap by introducing an AI-driven model that quantitatively links building morphology, particularly building height and age, via consumption quality, with urban vitality, addressing the limitations of previous qualitative or non-predictive approaches in urban design. Employing Generative adversarial networks (GAN), the research establishes correlations between building morphology, building height and age, consumption quality, and urban vitality, utilizing open-source datasets and spatial analysis. The GAN model predicts consumption quality based on urban design alterations, offering feedback for designers. This is a novel methodology that outperforms traditional approaches by enabling spatially explicit, data-driven predictions of consumption quality in response to building morphology changes, offering greater precision and practical utility for urban design. The results reveal that building height and age significantly influence consumption quality, with optimal configurations enhancing urban vibrancy. In addition, design morphologies such as podiums with atrium positively impact consumption quality. The study concludes AI-driven models can provide quantitative insights for urban design, fostering livable urban environments. It underscores the significance of AI in optimizing urban planning processes and advancing data-driven decision-making in high-density urban contexts like Hong Kong.