  This paper presents a novel application of Generative Adverserial Networks
(GANs) to study visual aspects of social processes. I train a a StyleGAN2-model
on a custom dataset of 14,564 images of London, sourced from Google Streetview
taken in London. After training, I invert the images in the training set,
finding points in the model's latent space that correspond to them, and compare
results from three inversion techniques. I connect each data point with
metadata from the Indices of Multiple Deprivation, describing income, health
and environmental quality in the area where the photographs were taken. It is
then possible to map which parts of the model's latent space encode visual
features that are distinctive for health, income and environmental quality, and
condition the synthesis of new images based on these factors. The synthetic
images created reflect visual features of social processes that were previously
unknown and difficult to study, describing recurring visual differences between
deprived and privileged areas in London. GANs are known for their capability to
produce a continuous range of images that exhibit visual differences. The paper
tests how to exploit this ability through visual comparisons in still images as
well as through an interactive website where users can guide image synthesis
with sliders. Though conditioned synthesis has its limitations and the results
are difficult to validate, the paper points to the potential for generative
models to be repurposed to be parts of social scientific methods.

Computer Vision and Pattern Recognition