Methodological Foundation of a Numerical Taxonomy of Urban Form

Cities are complex products of human culture, characterised by a startling diversity of visible traits. Their form is constantly evolving, reflecting changing human needs and local contingencies, manifested in space by many urban patterns. Urban Morphology laid the foundation for understanding many such patterns, largely relying on qualitative research methods to extract distinct spatial identities of urban areas. However, the manual, labour-intensive and subjective nature of such approaches represents an impediment to the development of a scalable, replicable and data-driven urban form characterisation. Recently, with advances in Geographic Data Science and the growing availability of digital mapping products, researchers in this field have developed an interest in quantitative urban morphology, or urban morphometrics, with the potential to overcome such limitations. In this paper, we present a method for numerical taxonomy of urban form derived from biological systematics, which allows the rigorous detection and classification of urban types. Initially, we produce a rich numerical characterisation of urban space from minimal data input, minimizing limitations due to inconsistent data quality and availability. These are street network, building footprint, and morphological tessellation, a spatial unit derivative of Voronoi tessellation, obtained from building footprints. Hence, we derive homogeneous urban tissue types (or taxa) and, by determining overall morphological similarity between them, generate a hierarchical classification (phenetic taxonomy) of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam - and discuss potential applications and further developments.