Identifying correlations between depression and urban morphology through generative deep learning

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.

Advancing justice in a city’s complex systems using designs enabled by space

“…Whereas GANs are primarily deployed as an analytical tool, Newton employed multiple maps with varying data to crystalize the correlation between mental health and the built environment. 14,15 GANs are also disseminated as a generative vehicle for speculative urban fiction, which uses urban satellite images as style images and uses abstract renderings and patterns as target images. These speculative depictions of fictional urban environments, however, ignore the scale and correlation between different datasets and architectural elements.…”