  Deep learning using neural networks has provided advances in image style
transfer, merging the content of one image (e.g., a photo) with the style of
another (e.g., a painting). Our research shows this concept can be extended to
analyse the design of streetscapes in relation to health and wellbeing
outcomes. An Australian population health survey (n=34,000) was used to
identify the spatial distribution of health and wellbeing outcomes, including
general health and social capital. For each outcome, the most and least
desirable locations formed two domains. Streetscape design was sampled using
around 80,000 Google Street View images per domain. Generative adversarial
networks translated these images from one domain to the other, preserving the
main structure of the input image, but transforming the `style' from locations
where self-reported health was bad to locations where it was good. These
translations indicate that areas in Melbourne with good general health are
characterised by sufficient green space and compactness of the urban
environment, whilst streetscape imagery related to high social capital
contained more and wider footpaths, fewer fences and more grass. Beyond
identifying relationships, the method is a first step towards
computer-generated design interventions that have the potential to improve
population health and wellbeing.

Computers and Society;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning