Identifying correlations between depression and urban morphology through generative deep learning. (March 2023)

• Record Type: Journal Article
• Authors: Newton, David William
• Is Part Of: International journal of architectural computing. Volume 21:Number 1(2023)
• Page Start: 136
• Page End: 157
• Publication Date: 2023-03
• Subjects: generative deep learning -- depression -- urban planning -- generative adversarial network; Architecture -- Data processing -- Periodicals; Architecture -- Informatique -- Périodiques; Virtual reality in architecture -- Periodicals; Computer-aided design -- Periodicals; Architecture -- Data processing; Periodicals; 720.2840285536
• DOI: 10.1177/14780771221089885
• Languages: English
• ISSNs: 1478-0771

Abstract

Mental health disorders, such as depression, have been estimated to account for the largest proportion of global disease burden. Existing research has established significant correlations between the built environment and mental health. This research, however, has relied on traditional statistical methods that are not amenable to working with large remote sensing image-based datasets. This research addresses this challenge and contributes new knowledge and a novel method for using generative deep learning for urban analysis and synthesis tasks involving mental health. The research specifically investigates three mental state measures: depression, anxiety, and the perception of safety. The experimental results demonstrate the efficacy of the process—providing a new method to find correlational signals, while providing insights on the correlation between specific urban design features and the incidence of depression.