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Urban Characterisation; Expanding Applications for, and New Approaches to Building Attribute Data Capture

Hudson, P; (2018) Urban Characterisation; Expanding Applications for, and New Approaches to Building Attribute Data Capture. Historic Environment: Policy & Practice , 9 (3-4) pp. 306-327. 10.1080/17567505.2018.1542776. Green open access

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Abstract

As cities face increasing pressure to develop long-term sustainability strategies, the need for detailed quantitative data on urban resources, and their behaviour over time, has become critical. Building stocks are a city’s largest socio-cultural and economic resource and account for around 40% of total energy consumption in developed countries. Despite ongoing problems with access to data on stock composition and dynamics, advances are now being made, particularly within sustainability science. Automated approaches to the analysis of historical building attribute data are also allowing long-term patterns of change in cities to be better understood. This is of significance to the conservation sector, and to the development of effective conservation strategies. At the same time, knowledge held by the conservation sector is of growing importance to sustainability science. This paper selects specific advances within this new research landscape, and identifies their importance in developing a more scientific, data-driven approach to the analysis of older stock. It concludes with an introduction to a new type of data collection and visualisation platform being developed for London, as a result of this review.

Type: Article
Title: Urban Characterisation; Expanding Applications for, and New Approaches to Building Attribute Data Capture
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/17567505.2018.1542776
Publisher version: https://doi.org/10.1080/17567505.2018.1542776
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Energy, urban rules, sustainability, urban science, building stock, building attribute data, VGI, machine learning, knowledge transfer, HUL
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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 > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10085916
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