On the discovery of urban typologies: data mining the many dimensions of urban form

Abstract

The use of typomorphology as a means of understanding urban areas has a long tradition amongst academics but the reach of these methods into urban design practice has been limited. In this paper we present a method to support the description and prescription of urban form that is contextsensitive, multi-dimensional, systematic, exploratory, and quantitative, thus facilitating the application of urban typomorphology to planning practice. At the core of the proposed method is the k-means statistical clustering technique to produce objective classifications from the large complex data sets typical of urban environments. Block and street types were studied as a test case and a context-sensitive sample of types that correspond to two different neighbourhoods were identified. This method is suitable to support the identification, understanding and description of emerging urban forms that do not fall into standard classifications. The method can support larger urban form studies through consistent application of the procedures to different sites. The quantitative nature of its output lends itself to integration with other systematic procedures related to the research, analysis, planning and design of urban areas.