Evolution of urban patterns: urban morphology as an open reproducible data science

This repository contains complete reproducible workflow for a research paper "Evolution of urban patterns: urban morphology as an open reproducible data science".

Fleischmann M, Feliciotti A and Kerr W (2021) Evolution of urban patterns: urban morphology as an open reproducible data science. Geographical Analysis. DOI: 10.1111/gean.12302.

Martin Fleischmann¹, Alessandra Feliciotti², William Kerr³

1 Research Associate, Department of Geography and Planning, University of Liverpool, [email protected].

2 Lecturer, Urban Design Studies Unit, Department of Architecture, University of Strathclyde, [email protected]

3 Urban Designer, Gillespies, [email protected]

@article{fleischmann2021,
  title = {Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science},
  author = {Fleischmann, Martin and Feliciotti, Alessandra and Kerr, William},
  year = {2021},
  doi = {10.1111/gean.12302},
  journal = {Geographical Analysis}
}

The repository is archived as DOI: 10.5281/zenodo.5034747.

@software{martin_fleischmann_2021_5034747,
  author       = {Martin Fleischmann},
  title        = {martinfleis/evolution-gean: Repository archive},
  month        = jun,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {archive},
  doi          = {10.5281/zenodo.5034747},
  url          = {https://doi.org/10.5281/zenodo.5034747}
}

Code and data

The research has been executed within a Docker container martinfleis/momepy:0.4.2. Alternatively, the environment can be built using a Dockerfile or as a conda environment using environment.yml.

To reproduce the analysis in the MyBinder cloud environment:

To reproduce the analysis locally, download or clone the repository or its archive, navigate to the folder (cd evolution_gean) and start docker using the following command:

docker run --rm -ti -p 8888:8888 -v ${PWD}:/home/jovyan/work martinfleis/momepy:0.4.2

That will start Jupyter Lab session on localhost:8888 and mount the current working directory to work folder within the container.

Note that you may need to pass -e NB_UID=$UID -e NB_GID=100 to the docker command to avoid user permission issues.

Docker container is based on jupyter/minimal-notebook. Please see its documentation for details.

Abstract

The recent growth of geographic data science (GDS) fuelled by increasingly available open data and open source tools have influenced urban sciences across a multitude of fields. Yet, there is limited application in urban morphology - a science of urban form. While quantitative approaches to morphological research are finding momentum, existing tools for such analyses have limited scope and are predominantly implemented as plugins for standalone GIS software. This inherently restricts transparency and reproducibility of research. Simultaneously, the Python ecosystem for GDS is maturing to the point of fully supporting highly specialised morphological analysis. In this paper, we use the open source Python ecosystem in a workflow to illustrate its capabilities in a case study assessing the evolution of urban patterns over six historical periods on a sample of 42 locations. Results show a trajectory of change in the scale and structure of urban form from pre-industrial development to contemporary neighbourhoods, with a peak of highest deviation during the post-WW2 era of Modernism, confirming previous findings. The wholly reproducible method is encapsulated in computational notebooks, illustrating how modern GDS models can be applied to urban morphology research to promote open, collaborative and transparent science, independent of proprietary or otherwise limited software.