Journal
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
Volume 50, Issue 6, Pages 1607-1623Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/23998083221141428
Keywords
Urban development; city planning; master plan; land-use; zoning; function; plot; gross plot ratio; gross floor area; knowledge graph; semantic web; web ontology language; Google Maps; city energy analyst; semantic city planning systems; machine learning; TensorFlow; Singapore
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This paper develops a methodology to quantitatively define the characteristics of mixed-use developments and applies it in Singapore. The study shows how these defined archetypes can provide more detailed data for urban building energy modelling, with potential for automation of the workflow in the future.
Urban planning relies on the definition, modelling and evaluation of multidimensional phenomena for informed decision-making. Urban building energy modelling, for instance, usually requires knowledge about the energy use profile and surface area of each use that takes place within a building. We do not have a detailed understanding of such information for mixed-use developments, which are gaining prominence in urban planning. In this paper, we developed a methodology to quantitatively define the characteristics of mixed-use developments using archetypes of programme profiles (ratios of each programme type) of a city's mixed-use plots. We applied our methodology in Singapore, resulting in 163 mixed-use zoning archetypes using Singapore's master plan data and Google Maps API data. In a case study, we demonstrated how these archetypes can be used to provide more detailed data for urban building energy modelling, including energy demand forecasts and energy supply system design. To enable future automation of the workflow, the archetype definitions were represented and stored as a machine-readable ontology. This ontology can later be extended with for example, the mobility properties of archetypes; thus, enabling the archetypes' use in other urban planning applications beyond building energy modelling.
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