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Compositional data analysis in the geosciences from theory to practice 1st Edition by Vera Pawlowsky Glahn ISBN 1862392056 9781862392052

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Author(s): Vera Pawlowsky-Glahn
ISBN(s): 9781862392052, 1862392056
Edition: illustrated edition
File Details: PDF, 29.70 MB
Year: 2006
Language: english
SKU: EB-2517854 Category: Tag:

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ISBN 10: 1862392056 
ISBN 13: 9781862392052
Author: Vera Pawlowsky Glahn

Atchison’s proposal in 1980 to use log-ratios in compositional data analysis led to the notion of building from “natural geometry,” a geometry that is coherent with the intuitive concept of difference associated with the particular type of data. This collection of papers reflects the state of the art in this rapidly expanding field while emphasizing practical applications and relevant software. Topics include case studies of compositional discrimination in Hungarian volcanites, Scottish limestone, Russian cassiterite, and in an Italian volcanic fumarolic field, with other applications in studies canonical variate vector components and tertiary sandstone composition in an evaluation of the Dickinson model. Three papers evaluate software and several address general theory and methods, including simplicial geometry, using exploratory compositional data analysis, determining frequency distributions and natural laws in geochemistry, using practical aspects of rounded zeros, and understanding topological concepts. Distributed in the US by the American Association of Petroleum Geologists. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

Compositional data analysis in the geosciences from theory to practice 1st Table of contents:

Section I: Introduction to Compositional Data Analysis

  1. Understanding Compositional Data

    • Defining Compositional Data
    • Characteristics and Importance in Geosciences
    • Examples of Compositional Data in Geoscience Research

  2. Mathematical Foundations of CoDA

    • Basic Concepts: Closure, Subcompositions, and Relative Compositions
    • Differences Between Compositional and Classical Data
    • Geometric Interpretations of Compositional Data

  3. Key Challenges in Compositional Data

    • The Closure Problem: Sum Constraint and Its Implications
    • Handling Sparsity and Zero Values
    • Data Transformation Methods for Compositional Data

Section II: Theory and Methods for Compositional Data

  1. Log-Ratio Transformations

    • Introduction to Log-Ratio Transformations
    • The Aitchison Transformation: A Geometric Approach
    • Other Log-Ratio Variants and Their Applications

  2. Multivariate Statistical Methods for Compositional Data

    • Principal Component Analysis (PCA) for Compositional Data
    • Canonical Correlation Analysis (CCA)
    • Clustering and Classification Techniques

  3. Exploratory Data Analysis (EDA) in CoDA

    • Visualizing Compositional Data
    • Biplots and Other Geometric Tools for EDA
    • Case Studies in Geoscience Applications

  4. Multivariate Statistical Inference in CoDA

    • Hypothesis Testing for Compositional Data
    • Bayesian Methods for Compositional Data Analysis
    • Regression Models for Compositional Data

Section III: Applications in Geosciences

  1. Geochemistry and Geochemical Data

    • Analysis of Elemental and Isotopic Compositions
    • Geochemical Classification and Mapping Techniques
    • Case Study: Elemental Composition of Rocks and Sediments

  2. Petrology and Mineralogy

    • Compositional Analysis in Petrology
    • Mineral Composition and Its Relation to Rock Properties
    • Case Study: Mineral Assemblage Analysis in Volcanic Rocks

  3. Geostatistics and Spatial Analysis

    • Compositional Data in Spatially Structured Data
    • Kriging and Geostatistical Modeling for Compositional Data
    • Spatial Distribution of Geochemical and Petrological Data

  4. Sedimentology and Stratigraphy

    • Sediment Composition and Provenance Analysis
    • Stratigraphic Data Analysis Using CoDA
    • Case Study: Sediment Transport and Depositional Environments

Section IV: Practical Considerations and Software

  1. Data Preparation and Preprocessing

    • Handling Zero Values and Missing Data
    • Transformations and Normalization for Compositional Data
    • Best Practices for Data Cleaning and Quality Control

  2. Software for Compositional Data Analysis

    • Overview of Popular Software Tools for CoDA
    • Using R for Compositional Data Analysis
    • Python and Other Programming Languages for CoDA

  3. Practical Case Studies in Geoscience

    • Real-World Examples of CoDA in the Geosciences
    • Step-by-Step Analysis of Geochemical and Geological Data
    • Discussion of Results and Interpretation

Section V: Advanced Topics and Future Directions

  1. Advanced Methods in CoDA

    • Non-Linear Methods for Compositional Data
    • Machine Learning Approaches for CoDA
    • Advances in Multivariate and Geostatistical Methods

  2. Challenges and Open Problems in CoDA

    • Addressing the Complexity of High-Dimensional Compositional Data
    • Dealing with Temporal and Spatial Variability
    • Future Research Directions in CoDA

  3. Applications Beyond the Geosciences

    • Extending CoDA to Other Scientific Disciplines
    • Industry Applications: Environmental Science, Ecology, and Economics
    • Potential Cross-Disciplinary Collaborations

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