Quantitative Analysis and Modeling of Earth and Environmental Data: Applications for Spatial and Temporal Variation offers a systematic, quantitative analysis of multi-sourced data, including the spatial distribution and temporal dynamics of natural attributes. It covers data handling techniques that may vary by space and/or time, and aims to improve understanding of physical laws of change underlying available numerical datasets, while also considering in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). Featuring real-world practical applications and practice exercises, this book is a comprehensive step-by-step tutorial of data-driven techniques that will help students and researchers master data analysis in earth and environmental sciences.
The notions and methods presented in the book 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).
- Addresses the analysis and processing data that varies spatially and/or temporally, which is the case with the majority of data in scientific and engineering disciplines
- Covers a wide range of data describing a variety of attributes characterizing physical phenomena and systems including earth, ocean and atmospheric variables, environmental and ecological parameters, population health states, disease indicators, and social and economic characteristics
- Includes case studies and practice exercises at the end of each chapter for both real-world applications and deeper understanding of the concepts presented
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
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.