A scalable method to quantify the relationship between urban form and socio-economic indexes
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...
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doaj-6168cc89bcf74cf88b1efeba24279f152020-11-25T01:43:10ZengSpringerOpenEPJ Data Science2193-11272018-02-017112110.1140/epjds/s13688-018-0132-1A scalable method to quantify the relationship between urban form and socio-economic indexesAlessandro Venerandi0Giovanni Quattrone1Licia Capra2Department of Civil, Environmental & Geomatic Engineering, UCLDepartment of Computer Science, UCLDepartment of Computer Science, UCLAbstract 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.http://link.springer.com/article/10.1140/epjds/s13688-018-0132-1Urban formSocio-economicsSpatial analysisOpen dataOpenStreetMap |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alessandro Venerandi Giovanni Quattrone Licia Capra |
spellingShingle |
Alessandro Venerandi Giovanni Quattrone Licia Capra A scalable method to quantify the relationship between urban form and socio-economic indexes EPJ Data Science Urban form Socio-economics Spatial analysis Open data OpenStreetMap |
author_facet |
Alessandro Venerandi Giovanni Quattrone Licia Capra |
author_sort |
Alessandro Venerandi |
title |
A scalable method to quantify the relationship between urban form and socio-economic indexes |
title_short |
A scalable method to quantify the relationship between urban form and socio-economic indexes |
title_full |
A scalable method to quantify the relationship between urban form and socio-economic indexes |
title_fullStr |
A scalable method to quantify the relationship between urban form and socio-economic indexes |
title_full_unstemmed |
A scalable method to quantify the relationship between urban form and socio-economic indexes |
title_sort |
scalable method to quantify the relationship between urban form and socio-economic indexes |
publisher |
SpringerOpen |
series |
EPJ Data Science |
issn |
2193-1127 |
publishDate |
2018-02-01 |
description |
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. |
topic |
Urban form Socio-economics Spatial analysis Open data OpenStreetMap |
url |
http://link.springer.com/article/10.1140/epjds/s13688-018-0132-1 |
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