Quantitative Analysis and Modeling of Earth and Environmental Data

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

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
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