Evaluating the Relationship Between Urban Structure and Mobility Through Clustering Analysis: A Case Study

Complex urban analysis requires dealing with large amounts of data, making using advanced machine analysis techniques beneficial. While employing machine learning and analysis methods to process standard industry metrics in architecture and urban analysis has advantages, it also comes with various limitations. This paper presents a successful example of applying a clustering-based methodology to examine the connection between urban structure, as described through morphological metrics selected by experienced urban analysts, and relevant urban data regarding public transportation infrastructure and mobility data.