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Quantitative Analysis and Modeling of Earth and Environmental Data
◆JpGU2025 セール開催中！：2025年6月30日(月)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Space-Time and Spacetime Data Considerations Author: Wu, Jiaping (Professor, Zhejiang University, China) / He, Junyu (Research Associate, Zhejiang University, China) / Christakos, George (Professor, San Diego State University, USA and Zhejiang University, China) Publisher: Elsevier USA ISBN: 9780128163412 Cover: PAPERBACK Date: 2021年12月 DESCRIPTION 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). Table 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
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