A scalable method to quantify the relationship between urban form and socio-economic indexes
- Submitting institution
-
Middlesex University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1705
- Type
- D - Journal article
- DOI
-
10.1140/epjds/s13688-018-0132-1
- Title of journal
- EPJ Data Science
- Article number
- 4
- First page
- -
- Volume
- 7
- Issue
- 1
- ISSN
- 2193-1127
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
http://eprints.mdx.ac.uk/25517/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a scalable method that delves deeper into the relationship between features of cities and socioeconomics. The method uses open datasets to extract multiple metrics of urban form and then models the relationship between urban form and socioeconomic levels through spatial regression analysis. It is significant because after analysing six major conurbations of the UK, it found that urban form explains up to 70% of the English official socioeconomic index variance. 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.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -