Measuring human-scale urban form and its determinants of inequality.

We propose synthesising and developing methods from machine learning and statistics to quantitatively assess spatial urban inequalities with the use of imagery. Through measuring similarity of the built environment, we approach inequalities from the human-scale view of the street, examining the role of urban planning as a potential mechanism for economic, environmental and social inclusion or exclusion. Using the city of London as our first urban layout, we will do this by:
- harnessing information encoded in distribution of pixels from Google Street View images to define similarity metric between geo-located images. Clustering similar images and thus creating visually homogeneous sets of images, across space, and also spatially conditioned to define neighbourhoods.
- creating geospatial map of neighbourhoods using clustered images combined with additional data sources such as satellite images and road networks.
- developing methods for the use of imagery in spatial epidemiology, not only by defining new neighbourhood boundaries, but also, the use of imagery as covariates in spatial models.
- including throughout our analysis a focus on interpretability of image features, in order to draw conclusions about determinants of inequality in the built environment.

We believe that by developing methods for analysing urban inequalities using imagery, we will first; quantify the spatial distribution of human-scale urban form in an unsupervised manner, developing methods which are transferable to cities worldwide, second; develop methodologies for incorporating image covariates in Bayesian models, which are transferable to a wide range of domain applications, and lastly; examine urban form as a determinant of urban inequality, contributing to our broader understanding of human-scale urban form and its performance in three dimensions: economic, environmental and social.