scholarly journals Hydrochemical Characteristics and Multivariate Statistical Analysis of Natural Water System: A Case Study in Kangding County, Southwestern China

Water ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 80 ◽  
Author(s):  
Yunhui Zhang ◽  
Mo Xu ◽  
Xiao Li ◽  
Jihong Qi ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Natural Water ◽  
Multivariate Statistical Analysis ◽  
Water System ◽  
Southwestern China ◽  
Hydrochemical Characteristics ◽  
Multivariate Statistical ◽  
System A

Investigation of groundwater hydrochemical characteristics using the multivariate statistical analysis in Ain Djacer area, Eastern Algeria

2016 ◽  
Vol 57 (56) ◽  
pp. 26993-27002 ◽  
Author(s):  
Dalila Ziani ◽  
Abderrahmane Boudoukha ◽  
Abderrahmane Boumazbeur ◽  
Lahcen Benaabidate ◽  
Chemseddine Fehdi
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Hydrochemical Characteristics ◽  
Multivariate Statistical ◽  
Eastern Algeria

scholarly journals An Exploratory Multivariate Statistical Analysis to Assess Urban Diversity

2019 ◽  
Vol 11 (14) ◽  
pp. 3812 ◽  
Author(s):  
Lorena Salazar-Llano ◽  
Marti Rosas-Casals ◽  
Maria Isabel Ortego
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Urban Systems ◽  
Multivariate System ◽  
Multiple Factor Analysis ◽  
Multivariate Statistical ◽  
Sustainability Challenges ◽  
The City

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’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.

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