Vacancy Visual Analytics Method: Evaluating adaptive reuse as an urban regeneration strategy through understanding vacancy. (August 2021)
- Record Type:
- Journal Article
- Title:
- Vacancy Visual Analytics Method: Evaluating adaptive reuse as an urban regeneration strategy through understanding vacancy. (August 2021)
- Main Title:
- Vacancy Visual Analytics Method: Evaluating adaptive reuse as an urban regeneration strategy through understanding vacancy
- Authors:
- Armstrong, Gill
Soebarto, Veronica
Zuo, Jian - Abstract:
- Abstract: Premature obsolescence of existing buildings is a significant challenge for sustainable regeneration in cities internationally. Adaptive reuse is one approach to address obsolescence in cities globally. High vacancy can be a crucial predictor of obsolescence, but vacancy is poorly understood and can be 'hidden'. This paper presents a novel quantitative method, called Vacancy Visual Analytics Method (VVAM), to identify vacancy in city populations. VVAM permits detailed visualisation of the location and quantity of vacancy, including Greyspace, a form of hitherto undetectable vacancy. To test VVAM, this paper reports its application to a population of office buildings ( n =118) in Adelaide, an Australian city reporting high office building vacancy. VVAM revealed the vacancy distribution did not lend itself to whole building adaptive reuse, despite the universal advocacy for greater adaptive reuse globally. This finding implies whole building adaptive reuse may not be appropriate to address vacancy in all cities. This study recommends policy formation should involve a thorough examination of vacancy across building populations to ensure policy efficacy. VVAM presents a useful tool to critically understand vacancy and inform policy to address urban vacancy, including cities affected by COVID-19 office building vacancy. Highlights: Vacancy data is useful for evaluating urban regeneration. Whole building adaptive reuse not always found to be a good fit for urban vacancy.Abstract: Premature obsolescence of existing buildings is a significant challenge for sustainable regeneration in cities internationally. Adaptive reuse is one approach to address obsolescence in cities globally. High vacancy can be a crucial predictor of obsolescence, but vacancy is poorly understood and can be 'hidden'. This paper presents a novel quantitative method, called Vacancy Visual Analytics Method (VVAM), to identify vacancy in city populations. VVAM permits detailed visualisation of the location and quantity of vacancy, including Greyspace, a form of hitherto undetectable vacancy. To test VVAM, this paper reports its application to a population of office buildings ( n =118) in Adelaide, an Australian city reporting high office building vacancy. VVAM revealed the vacancy distribution did not lend itself to whole building adaptive reuse, despite the universal advocacy for greater adaptive reuse globally. This finding implies whole building adaptive reuse may not be appropriate to address vacancy in all cities. This study recommends policy formation should involve a thorough examination of vacancy across building populations to ensure policy efficacy. VVAM presents a useful tool to critically understand vacancy and inform policy to address urban vacancy, including cities affected by COVID-19 office building vacancy. Highlights: Vacancy data is useful for evaluating urban regeneration. Whole building adaptive reuse not always found to be a good fit for urban vacancy. a novel, replicable method quantifying vacancy in building stocks visualises greyspace, a hidden phenomenon affecting urban vitality … (more)
- Is Part Of:
- Cities. Volume 115(2021)
- Journal:
- Cities
- Issue:
- Volume 115(2021)
- Issue Display:
- Volume 115, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 115
- Issue:
- 2021
- Issue Sort Value:
- 2021-0115-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-08
- Subjects:
- Vacancy -- Obsolescence -- Adaptive reuse -- Urban regeneration
City planning -- Periodicals
Urban policy -- Periodicals
711.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02642751 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cities.2021.103220 ↗
- Languages:
- English
- ISSNs:
- 0264-2751
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3267.792160
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17244.xml