This books presents some of the most recent and advanced statistical methods used to analyse environmental and climate data, and addresses the spatial and spatio-temporal dimensions of the phenomena studied, the multivariate complexity of the data, and the necessity of considering uncertainty sources and propagation. The topics covered include: detecting disease clusters, analysing harvest data, change point detection in ground-level ozone concentration, modelling atmospheric aerosol profiles, predicting wind speed, precipitation prediction and analysing spatial cylindrical data.

The volume presents revised versions of selected contributions submitted at the joint TIES-GRASPA 2017 Conference on Climate and Environment, which was held at the University of Bergamo, Italy. As it is chiefly intended for researchers working at the forefront of statistical research in environmental applications, readers should be familiar with the basic methods for analysing spatial and spatio-temporal data.

Autorentext

Michela Cameletti is an Associate Professor of Statistics at the Department of Management, Economics and Quantitative Methods, University of Bergamo, Italy.  Her research interests include spatial and spatio-temporal models for environmental applications and computational methods for Bayesian inference.

  Francesco Finazzi is researcher in Statistics at the Department of Management, Information and Production Engineering, University of Bergamo, Italy. His research interests include spatio-temporal models, sensor networks and scientific software.

Inhalt

1 Fast Bayesian classification for disease mapping and the detection of disease clusters.- 2 *A Novel Hierarchical Multinomial Approach to Modelling Age-specific Harvest Data.- *3 *Detection of change points in spatiotemporal data in presence of outliers and heavy-tailed observations.- 4 *Modelling spatiotemporal mismatch for Aerosol profiles.- 5 *A SPATIOTEMPORAL APPROACH FOR PREDICTING WIND SPEED ALONG THE COAST OF VALPARAISO, CHILE.- 6 *Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin: 1998-2016.- 7 A hidden Markov random field with copula-based emission distributions for the analysis of spatial cylindrical data.