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Data Visualization in the Geosciences [Pehme köide]

  • Formaat: Paperback / softback, 267 pages, kõrgus x laius x paksus: 252x201x12 mm, kaal: 517 g
  • Ilmumisaeg: 06-Mar-2002
  • Kirjastus: Pearson
  • ISBN-10: 013089706X
  • ISBN-13: 9780130897060
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Data Visualization in the GeosciencesSuurem pilt
  • Formaat: Paperback / softback, 267 pages, kõrgus x laius x paksus: 252x201x12 mm, kaal: 517 g
  • Ilmumisaeg: 06-Mar-2002
  • Kirjastus: Pearson
  • ISBN-10: 013089706X
  • ISBN-13: 9780130897060
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780130897060.html
Märksõnad:
A textbook for a graduate or undergraduate course on analyzing data useful to the study of geology, geography, agricultural sciences and environmental sciences, mining, and similar fields. A balance is sought between the mathematical description of algorithms and the visualization of their outcomes, with an emphasis on application. The disk contains a Windows-based program for reproducing the analytical results shown in the text. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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.
Preface xiii
Introduction
1(8)
Purpose
1(1)
Scope
2(2)
Nevada_Landsat Data Set
4(4)
Literature
8(1)
Exercises
8(1)
Univariate Data Analysis
9(23)
Histograms
9(6)
Visualizing Univariate Data Through Statistical Parameters: A Summary
14(1)
Data Distributions: Probability Density Functions
15(4)
The Normal Distribution
16(1)
Selected Comments About the Normal Distribution
17(1)
Testing the Normal Probability Density for Goodness of Fit
17(2)
Statistical Hypothesis Testing
19(5)
The Chi-Square Test
19(3)
Discussion
22(1)
Hypothesis Test for the Probability Plot
23(1)
So, Which Hypothesis Test Should Be Used?
23(1)
Other Distribution Functions and Suggested Applications
24(2)
The Poisson Distribution
24(1)
The Exponential Distribution
24(2)
The Log-Normal Distribution
26(1)
How Do I Reproduce Results in This
Chapter, or Analyze My Own Data...
26(3)
...Using Visual_Data?
26(1)
...Using Microsoft Excel?
27(1)
...Using Matlab Student Version 5.3?
28(1)
Literature
29(3)
Exercises
29(3)
Bivariate Data Analysis
32(29)
Correlation
32(12)
The Method of Least Squares
32(2)
Linear Least Squares Regression
34(2)
Linear Regression, Type II
36(2)
On the Adequacy of the Linear Regression Model
38(2)
Hypothesis Testing of the Slope, M
40(1)
The F-Test
40(2)
Correlation Coefficient
42(1)
Covariance
43(1)
On the Statistical Significance of the Correlation Coefficient
43(1)
Visual Regression
44(5)
Linear Models
44(1)
Least Absolute Deviation (LAD) Regression
45(1)
Comment: Least Squares or Least Absolute Deviation, Which Should be Used?
46(1)
Analysis of Residuals to Infer the Need for Nonlinear Regression
47(2)
Application of Correlation Analysis to the Nevada_Landsat Data Set
49(5)
How Do I Reproduce Results in This
Chapter, or Analyze My Own Data...
54(4)
...Using Visual_Data?
54(1)
...Using Matlab 5.3?
55(1)
...Using Microsoft Excel?
56(2)
Weighted Regression
58(1)
A Review of the Nevada_Landsat Data Set
59(1)
Literature
59(2)
Exercises
59(2)
Multivariate Data Analysis
61(24)
Analysis of Variance (ANOVA)
61(2)
Application to the Nevada_Landsat_6x_Data
63(1)
Statistical Hypothesis Test for Two Data Sets
63(2)
Principal Components Analysis
65(12)
Principal Components
65(1)
Standardized Principal Components Analysis
65(1)
Principal Components are Eigenvectors
66(1)
The Matrix Algebra
67(2)
Eigendecomposing the Correlation Matrix for the Nevada_Landsat_6x_Data
69(2)
Correspondence Analysis
71(3)
Application to the Nevada_Landsat Data Set
74(2)
Accommodating Missing Data in Correspondence Analysis
76(1)
Multivariate, Linear Regression (Multiple Regression)
77(1)
Logistic Regression: An Application of Multivariate, Linear Regression to the Nevada_Landsat Data Set
77(1)
How Do I Reproduce Results in This
Chapter, or Analyze My Own Data...
78(5)
...Using Visual_Data?
78(1)
...Using Microsoft Excel?
79(1)
...Using Matlab 5.3?
80(3)
A Summary of Analyses Thus Far Obtained of the Nevada_Landsat Data Set
83(1)
Literature
83(1)
Final Thoughts on the Robustness of Principal Components Methods
83(2)
Exercises
84(1)
Univariate Spatial Analysis
85(47)
Autocorrelation
85(3)
Introduction to Time Series Analysis
85(3)
Spatial Autocorrelation
88(8)
The Variogram
89(4)
Application to the Nevada_Landsat Data Set
93(3)
Directional Variogram Analysis
96(1)
Fractal Geometry
96(3)
Kriging: Spatial Interpolation as a Function of Spatial Autocorrelation
99(4)
Covariance is Obtained from the Variogram
102(1)
The Practice of Kriging
103(5)
On the Normality of the Spatial Data
104(1)
On Second Order Stationarity
104(1)
On the Variogram Model
104(1)
On the Design of the Grid
105(1)
More on the Number of Nearest Neighbors used for Estimation
105(1)
On the Size of the Search Window (And the Type of Search Strategy)
105(1)
On the Concept of Sample Support: Punctual or Block
106(1)
On the Need for a Data Transform
106(2)
On Anisotropic Spatial Autocorrelation Modeling
108(1)
Visualizations
108(5)
Application to the Nevada_Landsat_Data
113(4)
M-Kriging
117(2)
Cross-Validation
119(1)
How Do I Reproduce Results in This
Chapter, or Analyze My Own Data
119(11)
...Using Visual_Data?
119(1)
Time Series Analysis
119(2)
Variogram Analysis
121(1)
Kriging
122(3)
...Matlab
125(1)
Time Series Analysis
125(1)
Variogram Analysis
126(1)
Kriging
127(3)
Literature
130(2)
Exercises
130(2)
Multivariate Spatial Data Analysis
132(27)
Theory
132(7)
Solving for the Cokriging Weights
133(1)
The Matrix System: Kriging:
133(1)
The Matrix System: Cokriging:
134(5)
On the Practice of Cokriging
139(7)
Autokrigeability
146(2)
Application to Principal Components Images
146(1)
Extension to Indicator Cokriging
147(1)
The Undersampled Case
148(3)
Accommodating Undersampling in Cokriging
149(1)
Application to the Nevada_Landsat Data Set: Improving the Resolution of Thermal Images
149(2)
How Do I Reproduce Results in This
Chapter Using...
151(6)
Visual_Data?
151(4)
Using Matlab?
155(1)
Using Microsoft Excel?
156(1)
Literature
157(1)
Final Thoughts on Cokriging
157(2)
Exercises
158(1)
Spatial Simulation
159(21)
Random Numbers and Their Generation
159(3)
One-Dimensional Spatial Simulation
162(2)
Extension to Three-Dimensional Space: The Method of Random Lines
164(1)
Simulation Using Fractals
165(2)
Nonconditional Simulation: The Need for Data Transformation
167(1)
Nonconditional Simulation of Nevada_Landsat_Data
168(5)
Random Lines Algorithm
168(1)
Visible Blue Reflectance
169(1)
Thermal Emission
170(2)
The Fractal Algorithm
172(1)
Conditioning the Simulation
173(2)
Application to the Nevada_Landsat_Data
173(1)
Visible Blue Reflectance
174(1)
Thermal Emissivity
174(1)
Why Is Spatial Simulation Useful?
175(1)
How Do I Reproduce Results in This
Chapter, or Experiment With My Own Data...
176(2)
...Using Visual_Data?
176(2)
Literature
178(2)
Exercises
179(1)
Digital Image Processing
180(39)
Pixels
180(1)
Adjusting Pixel Contrast
181(7)
Algorithms for Contrast Adjustment
182(1)
Simple, Linear
183(2)
Multiple, Linear
185(1)
Histogram Equalization
185(1)
Binary Stretch
186(2)
Filtering Digital Images
188(6)
In a Digital Image, What Is a Low Frequency Feature? What Is a High Frequency Feature?
189(1)
Spatial Convolution Filtering: Low-Pass, High-Pass, High-Boost, and Custom Strategies
190(3)
Boosting Factor of 4
193(1)
Boosting Factor of 8
193(1)
East-West Custom High-Pass Filter (3x3)
193(1)
North-South Custom High-Pass Filter (3x3)
193(1)
Principal Components Analysis of Multispectral Digital Images
194(4)
Dust Devils on Mars
198(5)
True Color Compositing: The 24-Bit Bitmap
199(4)
How Do I Reproduce Results in This
Chapter, or Analyze My Own Images...
203(14)
...Using Visual_Data?
203(3)
File Format Conversion
206(1)
Contrast Adjustment
207(1)
Digital Filtering
208(2)
Image Addition and Subtraction
210(1)
...Using Matlab 5.3 (Student Version)?
210(5)
...With the Help of Microsoft Excel?
215(2)
Literature
217(2)
Exercises
217(2)
Composite Visualizations
219(22)
Multispectral Digital Image Compositing for Classification
219(6)
Classification of Texture
224(1)
Compositing a Contour Map With a Digital Image
225(2)
Three-Dimensional Perspectives
227(6)
Animation
233(1)
How Do I Reproduce Results in This
Chapter, or Pursue My Own Ideas...
234(5)
...Using Visual_Data?
234(3)
...Using Matlab?
237(2)
Literature
239(2)
Exercises
240(1)
Epilogue 241(6)
Why Be Normal?
241(1)
Human Prerogative
241(2)
Is Kriging the Best Spatial Interpolation Method?
243(1)
The Robustness of Kriging
244(3)
Appendices 247(12)
A Critical Values of the Chi-Square Distribution
248(3)
B Critical Values of Squared Correlation Coefficient, p-plot
251(2)
C Critical Values of F Distribution
253(4)
D Critical Values of t Distribution
257(2)
Bibliography 259(4)
Index 263