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

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 ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.scs.2023.104451

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Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
ORCID:
0000-0003-2649-2202
Publisher:
Elsevier
Journal:
Sustainable Cities and Society More from this journal
Volume:
92
Article number:
104451
Publication date:
2023-02-14
Acceptance date:
2023-02-06
DOI:
EISSN:
2210-6715
ISSN:
2210-6707
Language:
English
Keywords:
Pubs id:
1327474
Local pid:
pubs:1327474
Deposit date:
2023-02-06

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