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.
Vacancy Visual Analytics Method: Evaluating adaptive reuse as an urban regeneration strategy through understanding vacancy