Read more
Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offers a systematic, quantitative analysis of multi-sourced data and provides information about the spatial distribution and temporal dynamics of natural attributes (physical, biological, health, social). It includes models and techniques for handling data that may vary by space and/or time, and aims to improve understanding of the physical laws of change underlying the available numerical datasets, while taking into consideration the in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). It considers the synthesis of scientific theory-based methods (stochastic modeling, modern geostatistics) and data-driven techniques (machine learning, artificial neural networks) so that their individual strengths are combined by acting symbiotically and complementing each other.
The notions and methods presented in Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Including real-world practical applications as well as practice exercises, this book is a comprehensive step-by-step tutorial of theory-based and data-driven techniques that will help students and researchers master data analysis and modeling in earth and environmental sciences (including environmental health and human exposure applications).
List of contents
1. Introduction to Concepts 2. Data Classification, Characterization and Collection 3. Statistical Modeling 4. Geostatistical Modeling 5. Variography 6. Regional and Chrono-regional Estimators 7. Krigology 8. Bayesian Maximum Entropy 9. Software Tutorials
Appendix 1. Probability and Random Variable Theory 2. Instructor and Student Resources
About the author
Dr. Jiaping Wu is Director of the Institute of Islands and Coastal Ecosystems at Ocean College, Zhejiang University. His research interests include remote sensing of the environment and space-time data analysis. He has written over 70 journal articles on topics related to data analysis in the environment.Junyu He is Professor at the Institute of Island and Coastal Ecosystems at Ocean College,Zhejiang University. His research interests include geostatistics, environmental modeling, and risk analysis. His PhD dissertation was specifically on quantitative analysis and modeling of data with spatial variation and temporal dynamics.George Christakos is a Professor in the Department of Geography at San Diego State University (USA) and in the Institute of Island & Coastal Ecosystems, Ocean College at Zhejiang University (China). He is an expert in spatiotemporal random field modeling of natural systems, and his teaching and research focus on the integrative analysis of natural phenomena; spatiotemporal random field theory; uncertainty assessment; pollution monitoring and control; human exposure risk and environmental health; space-time statistics and geostatistics.
