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A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

Mohajeri, Nahid; Walch, Alina; Smith, Alison; Gudmundsson, Agust; Assouline, Dan; Russell, Tom; Hall, Jim; (2023) A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom. Sustainable Cities and Society , 92 , Article 104451. 10.1016/j.scs.2023.104451. Green open access

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Abstract

Regional-scale urban residential densification provides an opportunity to tackle multiple challenges of sustainability in cities. But framework for detailed large-scale analysis of densification potentials and their integration with natural capital to assess the housing capacity is lacking. Using a combination of Machine Learning Random Forests algorithm and exploratory data analysis (EDA), we propose density scenarios and housing-capacity estimates for the potential residential lands in the Oxford–Cambridge Arc region (whose current population of 3.7 million is expected to increase up to 4.7 million in 2035) in the UK. A detailed analysis was done for Oxfordshire, assuming different densities in urban and rural areas and protecting lands with high-value natural capital from development. For a 30,000 dwellings-per-year scenario, the land allocated in Local Plans could cover housing growth in the four districts but not in Oxford City itself (which accounts for 48% of the demand); only 19% of the need would be covered in low but 59% in high housing density scenarios. Our study suggests a decision-support method for quantifying how the impact of housing growth on natural capital can be significantly reduced using more compact development patterns, protection of land with high-value natural capital, and use of low-biodiversity brownfield sites where available.

Type: Article
Title: A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.scs.2023.104451
Publisher version: https://doi.org/10.1016/j.scs.2023.104451
Language: English
Additional information: Crown Copyright © 2023 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Machine Learning, Exploratory data analysis, Oxford-Cambridge Arc, Housing density, Brownfield lands, Local Plans, Regional scale
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10165613
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