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100			_aBeck, Anthony _957396
245			_aAutomated classification metrics for energy modelling of residential buildings in the UK with open algorithms/
260			_bSage, _c2020.
300			_aVol. 47, Issue 1, 2020, ( 45–64 p.)
520			_aEstimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc. and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this article, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain: MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment.
700			_aLong, Gavin _957397
700			_aDoreen S Boyd _957398
700			_aRosser, Julian F _957399
700			_aMorley, Jeremy _957400
700			_aDuffield, Richard _957401
700			_aSanderson, Mike _957402
700			_aRobinson, Darren _957403
773	0		_08876 _917104 _dLondon Pion Ltd. 2010 _tEnvironment and planning B: planning and design _x1472-3417
856			_uhttps://doi.org/10.1177/2399808318762436
942			_2ddc _cEJR
999			_c14425 _d14425