An Exploratory Multivariate Statistical Analysis to Assess Urban Diversity

摘要: Understanding diversity in complex urban systems is fundamental facing current and future sustainability challenges. In this article, we apply an exploratory multivariate statistical analysis (i.e., Principal Component Analysis (PCA) Multiple Factor (MFA)) to system’s abstraction of the city’s functioning. Specifically, relate environmental, economical, social characters city a system indicators by collecting measurements those variables at district scale. Statistical methods are applied reduce dimensionality dataset, such that, hidden relationships between districts exposed. The methodology has been mainly designed display diversity, being understood as differentiated attributes their dimensionally-reduced description, measure it with Euclidean distances. Differentiated distinctive functions identifiable case study Barcelona (Spain). distances allow for identification clustered districts, well that separated, exemplifying dissimilarity. Moreover, temporal dependency dataset reveals information about district’s differentiation or homogenization trends 2003 2015.