De Iaco, S., Palma, M. and Posa, D. (2013) Prediction of Particle Pollution through Spatio-Temporal Multivariate Geostatistical Analysis: Spatial Special Issue. AStA Advanced Statistical Analysis, 97, 133-150. http://dx.doi.org/10.1007/s10182-012-0199-0

has been cited by the following article:

• TITLE: Correspondence Analysis on a Space-Time Data Set for Multiple Environmental Variables

  AUTHORS: Palma Monica

  KEYWORDS: Space-Time Data, Indicator Transform, Correspondence Analysis

  JOURNAL NAME: International Journal of Geosciences, Vol.6 No.10, October 29, 2015

  ABSTRACT: Applications of the multivariate technique called correspondence analysis for environmental studies are relatively new and are limited to spatial multivariate data set. In this paper, a procedure of applying correspondence analysis to a large space-time data set for multiple environmental variables is shown. In particular, nitrogen dioxide and carbon monoxide hourly concentrations measured during January 1999 at several monitored stations in a district of Northern Italy are analyzed. The procedure consists in transforming the continuous variables into categorical ones by the means of appropriate indicator variables, generating special contingency tables and applying correspondence analysis. The use of this classical multivariate technique allows the identification of important relationships among pollution levels and monitoring stations and/or relationships among pollution levels and observation times.