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dc.contributor.authorSalazar Llano, Lorena
dc.contributor.authorRosas Casals, Martí
dc.contributor.authorOrtego Martínez, María Isabel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.date.accessioned2019-07-15T08:15:58Z
dc.date.available2019-07-15T08:15:58Z
dc.date.issued2019-07-11
dc.identifier.citationSalazar-Llano, L.; Rosas-Casals, M.; Ortego, M.I. An Exploratory Multivariate Statistical Analysis to Assess Urban Diversity. "Sustainability", 11 Juliol 2019, vol. 11, núm. 14, p. 3812-1-3812-27.
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/2117/166182
dc.description.abstractUnderstanding diversity in complex urban systems is fundamental in facing current and future sustainability challenges. In this article, we apply an exploratory multivariate statistical analysis (i.e., Principal Component Analysis (PCA) and Multiple Factor Analysis (MFA)) to an urban system’s abstraction of the city’s functioning. Specifically, we relate the environmental, economical, and social characters of the city in a multivariate system of indicators by collecting measurements of those variables at the district scale. Statistical methods are applied to reduce the dimensionality of the multivariate dataset, such that, hidden relationships between the districts of the city are exposed. The methodology has been mainly designed to display diversity, being understood as differentiated attributes of the districts in their dimensionally-reduced description, and to measure it with Euclidean distances. Differentiated characters and distinctive functions of districts are identifiable in the exploratory analysis of a case study of Barcelona (Spain). The distances allow for the identification of clustered districts, as well as those that are separated, exemplifying dissimilarity. Moreover, the temporal dependency of the dataset reveals information about the district’s differentiation or homogenization trends between 2003 and 2015.
dc.language.isoeng
dc.rightsAttribution 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Desenvolupament humà i sostenible
dc.subject.lcshSustainable urban development
dc.subject.lcshBiological diversity
dc.subject.lcshEnvironmental indicators
dc.subject.otherUrban diversity
dc.subject.otherUrban resilience
dc.subject.otherUrban sustainability
dc.subject.otherSustainability indicators
dc.subject.otherPrincipal Component Analysis (PCA)
dc.subject.otherMultiple Factor Analysis (MFA)
dc.subject.otherBiplot
dc.subject.otherBarcelona
dc.titleAn Exploratory Multivariate Statistical Analysis to Assess Urban Diversity
dc.typeArticle
dc.subject.lemacDesenvolupament urbà sostenible
dc.subject.lemacBiodiversitat
dc.subject.lemacIndicadors socials
dc.subject.lemacIndicadors ambientals
dc.contributor.groupUniversitat Politècnica de Catalunya. SUMMLab - Sustainability Measurement and Modeling Lab
dc.contributor.groupUniversitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis
dc.identifier.doi10.3390/su11143812
dc.relation.publisherversionhttps://www.mdpi.com/2071-1050/11/14/3812
dc.rights.accessOpen Access
local.identifier.drac25545966
dc.description.versionPostprint (published version)
local.citation.authorSalazar-Llano, L.; Rosas-Casals, M.; Ortego, M.I.
local.citation.publicationNameSustainability
local.citation.volume11
local.citation.number14
local.citation.startingPage3812-1
local.citation.endingPage3812-27


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