A scalable method to quantify the relationship between urban form and socio-economic indexes

Venerandi, Alessandro, Quattrone, Giovanni and Capra, Licia (2018) A scalable method to quantify the relationship between urban form and socio-economic indexes. EPJ Data Science, 7 . ISSN 2193-1127 (doi:10.1140/epjds/s13688-018-0132-1)

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Abstract

The world is undergoing a process of fast and unprecedented urbanisation. It is reported that by 2050 66% of the entire world population will live in cities. Although this phenomenon is generally considered beneficial, it is also causing housing crises and more inequality worldwide. In the past, the relationship between design features of cities and socio-economic levels of their residents has been investigated using both qualitative and quantitative methods. However, both sets of works had significant limitations as the former lacked generalizability and replicability, while the latter had a too narrow focus, since they tended to analyse single aspects of the urban environment rather than a more complex set of metrics. This might have been caused by the lack of data availability. Nowadays, though, larger and freely accessible repositories of data can be used for this purpose. In this paper, we propose a scalable method that delves deeper into the relationship between features of cities and socio-economics. The method uses openly accessible datasets to extract multiple metrics of urban form and then models the relationship between urban form and socio-economic levels through spatial regression analysis. We applied this method to the six major conurbations (i.e., London, Manchester, Birmingham, Liverpool, Leeds, and Newcastle) of the United Kingdom (UK) and found that urban form could explain up to 70% of the variance of the English official socio-economic index, the Index of Multiple Deprivation (IMD). In particular, results suggest that more deprived UK neighbourhoods are characterised by higher population density, larger portions of unbuilt land, more dead-end roads, and a more regular street pattern.

Item Type: Article
Additional Information: Article number = 4
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 25517
Notes on copyright: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made
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Depositing User: Giovanni Quattrone
Date Deposited: 05 Nov 2018 11:13
Last Modified: 05 Apr 2019 10:08
URI: https://eprints.mdx.ac.uk/id/eprint/25517

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