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.

Highlights

  • IntroductionWhen comparing their spatial form, marked differences can be clearly observed at all scales

  • Very much like the study of organismal phenotypes and the statistical description of biological forms were instrumental to the separation of individuals into recognisable, homogeneous groups (Raup, 1966), extending numerical taxonomy to the study of urban form, offers an operationally viable and reliable conceptual and methodological framework for a systematic classification of homogeneous urban form types

  • The paper presents an original data-driven approach for the systematic unsupervised classification and characterisation of urban form patterns grounded on numerical taxonomy in biological systematics and which clusters urban tissues based on phenetic similarity, delivering a systematic numerical taxonomy of urban form

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Summary

Introduction

When comparing their spatial form, marked differences can be clearly observed at all scales. Despite these variations, their heterogeneous fabrics share geometric characteristics, which make it possible to compare them to one another through the analysis of their constituent elements and to recognise patchworks of distinct urban tissues within each city. Further research has focused on classification of morphological elements into ‘types’ This includes the series of works by Steadman (Steadman et al, 2000, 2009) on the classification of buildings based on a handful of empirically measured geometrical parameters as well as the work by (Marshall, 2005) on the classification of street pattern types

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