Feature Extraction and K-means Clustering Approach to Explore Important Features of Urban Identity

Abstract

Public spaces play an important role in the processes of formation, generation and change of urban identity. Under present day conditions, the identities of cities are rapidly deteriorating and vanishing. Therefore, the importance of urban design, which is a means of designing urban spaces and their physical and social aspects, is ever growing. This paper proposes a novel methodology by using Principle Component Analysis (PCA) and K-means clustering approach to find important features of the urban identity from public space. K. Lynch’s work and Space Syntax theory are reconstructed and integrated with POI (Points of Interest) to quantify the quality of the public space. A case study of Zürich city is used to test of these redefinitions and features of urban identity. The results show that PCA and K-means clustering approach can identify the urban identity and explore important features. This strategy could help to improve planning and design processes and generation of new urban patterns with more appropriate features and qualities.