An Exploratory Multivariate Statistical Analysis to Assess Urban Diversity
| Autor: | M. I. Ortego, Martí Rosas-Casals, Lorena Salazar-Llano |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Màquines i Motors Tèrmics, Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Universitat Politècnica de Catalunya. SUMMLab - Sustainability Measurement and Modeling Lab, Universitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Barcelona
Multivariate statistics Biplot media_common.quotation_subject Geography Planning and Development lcsh:TJ807-830 0211 other engineering and technologies 0507 social and economic geography lcsh:Renewable energy sources Urban diversity 02 engineering and technology Management Monitoring Policy and Law Urban sustainability urban diversity urban resilience ComputerApplications_MISCELLANEOUS Indicadors socials Statistics Multiple factor analysis Sustainability indicators lcsh:Environmental sciences Principal Component Analysis (PCA) media_common lcsh:GE1-350 Desenvolupament humà i sostenible [Àrees temàtiques de la UPC] sustainability indicators Renewable Energy Sustainability and the Environment lcsh:Environmental effects of industries and plants 05 social sciences Sustainable urban development 021107 urban & regional planning Indicadors ambientals Biodiversitat Identification (information) Geography lcsh:TD194-195 Biological diversity Environmental indicators urban sustainability Multiple Factor Analysis (MFA) Principal component analysis Urban resilience Desenvolupament urbà sostenible Scale (map) 050703 geography Diversity (politics) biplot |
| Zdroj: | Sustainability Volume 11 Issue 14 Recercat. Dipósit de la Recerca de Catalunya instname Sustainability, Vol 11, Iss 14, p 3812 (2019) UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | Understanding 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&rsquo s abstraction of the city&rsquo 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&rsquo s differentiation or homogenization trends between 2003 and 2015. |
| Databáze: | OpenAIRE |
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