  Recent researches have shown the increasing use of machine learn-ing methods
in geography and urban analytics, primarily to extract features and patterns
from spatial and temporal data using a supervised approach. Researches
integrating geographical processes in machine learning models and the use of
unsupervised approacheson geographical data for knowledge discovery had been
sparse. This research contributes to the ladder, where we show how latent
variables learned from unsupervised learning methods on urbanimages can be used
for geographic knowledge discovery. In particular, we propose a simple approach
called Convolutional-PCA(ConvPCA) which are applied on both street level and
street network images to find a set of uncorrelated and ordered visual
latentcomponents. The approach allows for meaningful explanations using a
combination of geographical and generative visualisations to explore the latent
space, and to show how the learned representation can be used to predict urban
characteristics such as streetquality and street network attributes. The
research also finds that the visual components from the ConvPCA model achieves
similaraccuracy when compared to less interpretable dimension reduction
techniques.
