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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).
- Explores the analysis and processing of chronotopologic (i.e., space-time and spacetime) data that varies spatially and/or temporally, which is the case with the majority of data in scientific and engineering disciplines.
- Studies the synthesis of scientific theory and empirical evidence (in its various forms) that offers a mathematically rigorous and physically meaningful assessment of real world phenomena.
- 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.
Graduate students in Earth and Environmental Science, Geography, GIS, and data analysis; GIS scientists, Earth Scientists, geographers
1. Introduction to Concepts
2. Data Classification, Characterization and Collection
3. Statistical Modeling
4. Geostatistical Modeling
6. Regional and Chrono-regional Estimators
8. Bayesian Maximum Entropy
9. Software Tutorials
1. Probability and Random Variable Theory
2. Instructor and Student Resources
- No. of pages:
- © Elsevier 2021
- 1st June 2021
- Paperback ISBN:
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.
Professor, Zhejiang University, China
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.
Research Associate, Zhejiang University, China
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.
Professor, San Diego State University, USA and Zhejiang University, China
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