An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics

Urban systems’ dynamics are the result of two intertwined processes that operate at different rhythms: their physical structure and underlying social processes. This paper suggests a novel approach to the spatial analysis of urban systems, using neighborhoods as a basic building block. Neighborhoods are usually the minimal homogeneous geographical unit in urban areas, both regarding their physical, and social characteristics, and the availability of governmental data. Using unsupervised machine learning algorithm, an extensive real-estate transaction dataset, and census data, a multi-scale analysis of neighborhoods’ dynamics in England and Wales is performed. The spatial and temporal dynamics of the resulting clusters of neighborhoods highlights the urban challenges faced by entire urban systems. The results suggest that processes triggered by urban inequalities may affect not only the social structure of cities (for example, through gentrification and displacement), but also the environmental sustainability of the whole urban system at a much larger scale: Suburbanization pressures seem to threaten rural areas at an unprecedented magnitude. The identification of the areas where this pressure is acute allows for the design of appropriate urban policy responses. The main message for the analysis of urban dynamics is that physical transformations and socio-political rearrangements that are intrinsically intertwined: Therefore, the joint management of the both aspects is the key to a sustainable future. The analysis also highlights the potential of machine learning algorithms for the benefit of urban science in general, and the study of urban dynamics in particular.