Data Visualization in the Geosciences / Edition 1by James Carr
Pub. Date: 01/01/1902
Publisher: Pearson Education
The unique aspect of this entire book is the emphasis on visualizing both raw data and the results of their analysis. The emphasis of this book is data analysis with coverage of both classical and unique topics. Classical topics include univariate, bivariate, and multivariate data analysis. Unique topics include kriging, cokriging, geostatistical simulation, digital image analysis, and data composing. For data analysts.
- Pearson Education
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Table of ContentsMost chapters include How Do I Reproduce Results in This Chapter, or analyze Mt Own Data..., Literature and Exercises.
Purpose. Scope. Nevada_Landsat Data Set.
2. Univariate data Analysis.
Histograms. Data Distributions: Probability Density Functions. Statistical Hypothesis Testing. Other Distribution Functions and Suggested Applications.
3. Bivariate Data Analysis.
Correlation. Visual Regression. Applications of Correlation Analysis to the Nevada_Landstat Data Set. Weighted Regression. A Review of the Nevada_Landstat Data Set.
4. Multivariate Data Analysis.
Analysis of Variance (Anova). Statistical Hypothesis Tests for Two Data Sets. Principal Components Analysis. Multivariate, Linear Regression (Multiple Regression). A Summary of Analyses Thus Far Obtained of the Nevada_Landstat Data Set. Final Thoughts on the Robustness of principal Components Methods.
5. Univariate Spatial Analysis.
Autocorrelation. Spatial Autocorrelation. Fractal Geometry. Kriging: Spatial Interpolation as a Function of Spatial Autocorrelation. The Practice of Kriging. Visualizations. Application to the Nevada_Landstat_Data. M-Kriging.
6. Multivariate Data Analysis.
Theory. On the Practice of Cokriging. Autokrigeability. The Undersampled Case. Final Thoughts on Cokriging.
7. Geostatistical Simulation.
Random Numbers and Their Generation. One-Dimensional Spatial Simulation. Extension to Three-Dimensional Space: The Method of Random Lines. Simulation Using Fractals. Nonconditional Simulation: The Need for Data Transformation. Nonconditional Simulation ofNevada_Landstat_Data. Conditioning the Simulation. Why Is Spatial Simulation Useful?
8. Digital Image Processing.
Pixels. Adjusting Pixel Contrast. Filtering Digital Images. Principal Components Analysis of Multispectral Digital Images. Dust Devils on Mars.
9. Composite Display.
Multispectral Digital Image Compositing for Classification. Composing a Contour Map With a Digital Image. Three-Dimensional Perspectives. Animation.
Why Be Normal. Human Prerogative. Is Kriging the Best Spatial Interpolation Method? The Robustness of Kriging.
Appendix A. Critical Values of the Chi-Square Distribution.
Appendix B. Critical Values of Squared Correlation Coefficient, p-plot.
Appendix C. Critical Values of F Distribution.
Appendix D. Critical Values of t Distribution.
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