Methodological foundation of a numerical taxonomy of urban form

Martin Fleischmann, Alessandra Feliciotti, Ombretta Romice, Sergio Porta

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
9 Downloads (Pure)

Abstract

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, advances in geographic data science and the availability of digital mapping products open the opportunity to overcome such limitations. And yet, our current capacity to systematically capture the heterogeneity of spatial patterns remains limited in terms of spatial parameters included in the analysis and hardly scalable due to the highly labour-intensive nature of the task. 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, minimising 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 and, by determining overall morphological similarity between them, generate a hierarchical classification 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. The proposed classification method represents a step towards the development of an extensive, scalable numerical taxonomy of urban form and opens the way to more rigorous comparative morphological studies and explorations into the relationship between urban space and phenomena as diverse as environmental performance, health and place attractiveness.
Original languageEnglish
Pages (from-to)1283-1299
Number of pages17
JournalEnvironment and Planning B: Urban Analytics and City Science
Volume49
Issue number4
Early online date15 Dec 2021
DOIs
Publication statusPublished - 1 May 2022

Keywords

  • management, monitoring, policy and law
  • nature and landscape conservation
  • urban studies
  • geography, planning and development
  • architecture

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