Comparing and Clustering Residential Layouts Using a Novel Measure of Grating Difference
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Comparing and Clustering Residential Layouts Using a Novel Measure of Grating Difference Ran Xiao1 Accepted: 29 September 2020 © The Author(s) 2020
Abstract Clustering is widely used as a knowledge discovery method in scientific studies but is not often used in architectural research. This paper applies clustering to a dataset of 129 residential layouts, which were collected from contemporary architectural practices, to reveal underlying design patterns. To achieve this, this paper introduces a novel measure for the topological properties of layouts: ‘grating difference measure’. It was benchmarked against an alternative that measures geometrical properties and the advantages are explained. The grating difference measure indicates the extent of design differences, which is used in the clustering method to obtain the distance between datapoints. The results from clustering were grouped into design schematics and qualitatively assessed, showing a convincing separation of characteristics. The method demonstrated in this paper may be used to reveal topological patterns in datasets of existing designs for both academic and practical purposes. Keywords Grating representation · Topology · Mappings · Design analysis · Residential architecture · Algorithms
Introduction Clustering is widely used as a knowledge discovery method, to organise objects in a dataset into groups, thereby revealing underlying patterns. In architectural design, this term was first referred to by Carter and Whitehead (1975) in the clustering of rooms according to their functional connections. Their research, however, was primarily concerned with the spatial clustering of architectural programmes. Recently, clustering has been used in the categorisation of architectural designs generated by algorithms. Rodrigues et al. (2017) and Yousif and Yan (2019) addressed the problem of presenting large quantities of computer-generated layout * Ran Xiao [email protected] 1
Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK Vol.:(0123456789)
R. Xiao
options to human users. They used geometry-based methods to measure similarities between layouts, then applied a clustering method to categorise the generated design options into groups, which made them easier to assess and apply. Deep learning techniques have also been used in architectural studies to cluster datapoints. For instance, algorithms to recognise architectural styles or authorship were trained with datasets that consisted of prelabelled photographs of buildings, demonstrating varying degrees of success (Obeso et al. 2017; Yoshimura et al. 2019). In Yoshimura et al.’s research, an additional clustering step measured similarity between each architect’s work and identified clusters of architects whose works are stylistically similar. However, these methods are limited by the visual information that can be captured by a camera. In contrast, this paper uses a clustering method to uncover topological patterns in layout designs. It gathers data from plan drawings collected from
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