Exploration of Spatio-Temporal Environmental Conditions: Harmonized Databases and Analytical Techniques
Description
This open access volume discusses advanced tools and techniques for the analysis and prediction of environmental variables with a spatial or spatio-temporal structure. In various environmental science applications, developing models that accurately describe the spatio-temporal evolution of key variables is essential for monitoring eco-sustainability. This book presents theoretical reviews on spatio-temporal covariance modeling, as well as innovative approaches for assessing environmental quality and its effects on climate change. These approaches integrate georeferenced data from multiple sources and apply novel methodologies for analyzing multivariate spatio-temporal data. This volume presents the scientific contributions presented at the Workshop "Exploration of Spatio-Temporal Environmental Conditions: Harmonized Databases and Analytical Techniques (ECoST-DATA)", held at the University of Bari on July 3-4, 2025.
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Persons
Sandra De Iaco is Full Professor in Statistics and Coordinator of a PhD programme at the Department of Economic Sciences of the University of Salento (Italy).
She earned a PhD in Statistics, with a dissertation on "Space-time covariance models", at the University "G. D'Annunzio" of Chieti-Pescara (Italy) in 1999. Her research interests are in spatial and spatio-temporal Geostatistics, particularly in covariance modeling and stochastic interpolation with applications to environmental data. She is an active member of the editorial board of Spatial Statistics and chair of the lecturers committee of the IAMG. She attended various international statistical conferences, as speaker, invited speaker and chairwoman, and published more than a hundred scientific articles, two software packages, as well as several didactic and specialized books.
Donato Posa is Full Professor in Statistics at the Department of Economic Sciences of the University of Salento (Italy). He earned a Degree in Physics (cum laude) from the University of Bari, with the thesis work conducted at CERN in Genève. This was followed by a Specialization in Physics. He has been involved in numerous research projects, focusing on themes such as statistical analysis of spatial and spatio-temporal data, and models for environmental pollutants and ecosystems. His primary research interests are centred on advanced statistical methodologies for complex data, including space-time covariance modelling, multivariate Geostatistics, stochastic conditional and non-conditional simulation, and time series analysis. He has been an invited speaker and chair of several statistical international conferences, and is the author of numerous scientific papers and specialized books.
Monica Palma is Associate Professor in Statistics at the Department of Economic Sciences of the University of Salento (Italy). After her PhD in Statistics, earned at the University "G. D'Annunzio" of Chieti-Pescara (Italy), she attended an advanced course in Multivariate Geostatistics at the Ecole de Mines, in Fontainebleau (France). Her research interests are in multivariate Geostatistics for environmental data, space-time covariance modeling and prediction, and stochastic conditional and non-conditional simulation. She is a referee for various international journals, such as Stochastic Environmental Research and Risk Assessment, Computers & Geosciences, and Science Journal of Applied Mathematics and Statistics. She has published numerous papers in international scientific journals and is the author of textbooks on statistics.
Content
I. Random fields and Covariance modelling.- Covariance functions.- Spatio-temporal Complex Covariance Functions for Vectorial Data.- Non-Separable Covariance Kernels for Spatiotemporal Gaussian Processes Based on the Hybrid Spectral Method.- Stationary subspace analysis for spatio-temporal data.- II. Environmental control and integration.- Space turns to Time: the Advent of Time series of Remote Sensing Images.- Ensemble Smoother with Multiple Data Assimilation for Atmospheric Dust Source Identification: A Generic Framework Approach.- Optimizing Pollution Control for an Economic Growth System.- Exploring Functional Structure and Variation of Italian Carbon Emissions.- III. Environmental analysis.- Ozone Predictions through a Generalized Additive Model.- Geostatistical Characterization of Climate Extremes Dynamics: The Drought Phenomenon.- Using Machine Learning Methods to explore the Effects of Environmental Variables on Biodiversity.- Modeling and Prediction of Ground-level Ozone Concentrations in a Spatio-temporal Multivariate Context.