Identification of residential building typologies by applying clustering techniques to cadastral data

Abstract

Building typologies are usually classified according to their shape, distribution and construction features depending on the time period they were built. As a result, a subjective classification arises which highly depends on the criteria used to differentiate buildings. When this analysis is carried out at the urban scale, data sets become bigger, making patterns difficult to uncover. Mistakes in deciding the variables to classify buildings lead to incorrect typologies and, consequently, wrong results. The aim of this study is to test a new methodology based on clustering techniques to identify typologies related to energy retrofitting, which would allow obtaining a more objective classification and better pattern recognition by reducing human intervention. To that end, a data set from the Spanish cadastre is used, with additional information to reflect the influence of existing standards on constructive solutions. By applying three clustering techniques (Ward's method, Partitioning Around Medoids, and a combination of both), new proposals of building typologies are obtained and discussed in comparison to traditional classifications. The results show that the Ward's method produces building typologies with significantly high quality metrics and meaningfulness. The agglomeration coefficient is 99.8%, which indicates that hardly another hierarchical method could generate a better clusters structure. Six clusters comprise 86% of the dwellings as most occupants do not declare retrofitting works, thus not being reflected in the cadastre database. This research provides a new classification method that can notably influence the estimation of costs, environmental impact and cost effectiveness of energy retrofitting actions at urban scale.