Using convolutional autoencoders to extract visual features of leisure and retail environments

Visual characteristics of leisure and retail environments provide sensory cues that can influence how consumers experience and behave within these spaces. In this paper, we provide a computational method that summarises the "visual features" of shopping districts by analysing a national database of geocoded store frontage images. While the traditional focus of social scientific research explores how drivers such as proximity to shopping environments factor into location choice decisions, the visual characteristics that describe the enclosing urban area are often neglected. This is despite the assumption consumers translate visual appearance of a retail area into a judgement of its functional utility which mediates consumer behaviour, patronage intention and the image a retail location projects to passers-by. Such judgements allow consumers to draw fine distinctions when evaluating between competing destinations. Our approach introduces a deep learning model known as Convolutional Autoencoders to extract visual features from storefront images of leisure and retail amenities. These features are partitioned into five clusters before several measures describing the environment around the leisure and retail properties are introduced to differentiate between the clusters and assess which variables are distinctive for particular groupings. Our empirical strategy unpacks different groupings from the clusters, which implies the existence of relationships between visual features of shopping areas and functional characteristics of the surrounding urban environment. Ultimately, using the example of retail landscapes, the core contribution of this paper demonstrates the utility of unsupervised deep learning methods to research questions in urban planning.