• Dwelling size and usability in London: A study of floor plan data using machine learning

Ozer, Seyithan ORCID: https://orcid.org/0000-0003-4380-2700 and Jacoby, Sam ORCID: https://orcid.org/0000-0002-9133-5177, 2022, Journal Article, Dwelling size and usability in London: A study of floor plan data using machine learning Building Research & Information, 50 (6). pp. 694-708. ISSN 1466-4321

Abstract or Description:

Based on a dataset of dwelling unit plans (n = 2283) with detailed dimensions derived from open-access plan data using machine learning, this paper analyses the size and usability of dwellings in London. Half of London’s housing stock was built before the Second World War but has been extensively modified. Due to greater pressure on the housing market and problems with dwelling size, London was the first local authority in England to reintroduce space standards for all housing sectors in 2011. Providing a first comprehensive analysis of space standards and dwelling size in London at room level and across all built periods, the data shows that 61% of London homes fail the recommended minimum dwelling sizes of the London Housing Design Guide (2010), 51% a bedroom standard and 88% at least one of the dimensional requirements. The paper quantifies the extent to which homes fail both recent and historical space standards and discusses their effectiveness in relation to dwelling usability and issues of design.

Official URL: https://www.tandfonline.com/doi/full/10.1080/09613...
Subjects: Architecture > K100 Architecture > K120 Interior Architecture
Architecture > K400 Planning (Urban > K450 Housing
School or Centre: School of Architecture
Funders: Prosit Philosophiae Foundation
Identification Number or DOI: https://doi.org/10.1080/09613218.2022.2070452
Uncontrolled Keywords: Dwelling size; space standards; machine learning; floor plans
Date Deposited: 09 May 2022 14:27
Last Modified: 19 Jul 2022 13:43
URI: https://researchonline.rca.ac.uk/id/eprint/5033
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