Methodological Foundation of a Numerical Taxonomy of Urban Form

Code repository for the Methodological Foundation of a Numerical Taxonomy of Urban Form paper.

Fleischmann M, Feliciotti A, Romice O and Porta S (2021) Methodological Foundation of a Numerical Taxonomy of Urban Form. Environment and Planning B: Urban Analytics and City Science, doi: 10.1177/23998083211059835

Martin Fleischmann^{1, 2}, Alessandra Feliciotti², Ombretta Romice², Sergio Porta²

1 Department of Geography and Planning, University of Liverpool

2 Urban Design Studies Unit, Department of Architecture, University of Strathclyde

Contact: [email protected]

Date: 28/10/2021

The online interactive maps of the final classification are available at https://martinfleis.github.io/numerical-taxonomy-maps/.

Code

The code is split into two folders - code_method containing cleaned reproducible Python code for everyone willing to use the method, and code_production containing an archive of the used (and somewhat messy) code.

The method

The folder code_method contains generalised code for the method, that should be reproducible on a custom data. The main notebook morphometric_assessment.ipynb has been updated to work with the recent releases of software. You can create the reproducible environment to run it using conda or mamba and the environment.yaml file in the code_method folder.

conda env create -f environment.yaml

You can also create a new environment taxonomy manually:

conda create -n taxonomy
conda activate taxonomy
conda config --env --add channels conda-forge
conda config --env --set channel_priority strict
conda install momepy mapclassify seaborn

Alternatively, you can use the Docker container darribas/gds_py:7.0.

The code

The folder code_production is an archive of the actual production code used to generate the analysis presented in the paper. However, it is recommended to use the code in the code_method folder if you want to reproduce the work. The code in the folder is stored for archival purposes and different parts may depend on different versions of dependecies.

Data

Non-proprietary data are archived on figshare as 10.6084/m9.figshare.16897102. The archive contains input geometry, generated geometry, all measured morphometric characters and a final classification labels for Prague and Amsterdam. It does not contain validation data, which are available upon request (due to the licensing).

The online interactive maps of the final classification are available at https://martinfleis.github.io/numerical-taxonomy-maps/.

Preprint

Preprint of the final manuscript is available from arXiv.

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, 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 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.