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

Public spaces play an important role in theprocesses of formation, generation and change of urban identity.Under present day conditions, the identities of cities are rapidlydeteriorating and vanishing. Therefore, the importance of urbandesign, which is a means of designing urban spaces and theirphysical and social aspects, is ever growing. This paper proposesa novel methodology by using Principle Component Analysis(PCA) and K-means clustering approach to find importantfeatures of the urban identity from public space. K. Lynch`s work and Space Syntax theory are reconstructed and integratedwith POI (Points of Interest) to quantify the quality of the publicspace. A case study of Zürich city is used to test of theseredefinitions and features of urban identity. The results showthat PCA and K-means clustering approach can identify theurban identity and explore important features. This strategycould help to improve planning and design processes andgeneration of new urban patterns with more appropriate featuresand qualities.