Data visualization in the geological sciences:
CD-ROM contains: Visual_Data -- Data files used in text -- Digital images used in text.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice Hall
2002
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | CD-ROM contains: Visual_Data -- Data files used in text -- Digital images used in text. |
| Beschreibung: | Includes bibliographical references and index. - Erscheint: November 2011 |
| Beschreibung: | XIV, 267 S. Ill., graph. Darst. CD-ROM (12 cm) |
| ISBN: | 013089706X |
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| 245 | 1 | 0 | |a Data visualization in the geological sciences |c James R. Carr |
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| 650 | 4 | |a Geologie | |
| 650 | 4 | |a Geology |x Graphic methods | |
| 650 | 4 | |a Geology |x Graphic methods |x Data processing | |
| 650 | 4 | |a Geology |x Statistical methods | |
| 650 | 4 | |a Geology |x Statistical methods |x Data processing | |
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Datensatz im Suchindex
| _version_ | 1804132705185562624 |
|---|---|
| adam_text | J A M E S R. C A R R U N I V E R S I T Y OF N E V A D A - R E N O M A C
K A Y S C H O O L OF M I N E S DATA VISUALIZATION IN THE GEOLOGICAL
SCIENCES PRENTICE HALL PRENTICE HALL UPPER SADDLE RIVER, NJ 07458 C O N
T E N T S PREFACE XIII C H A P T E R 1 INTRODUCTION C H A P T E R 2 1.1
PURPOSE 1 1.2 SCOPE 2 1.3 NEVADA_LANDSAT DATA SET 4 1.4 LITERATURE 8
EXERCISES 8 UNIVARIATE DATA ANALYSIS 8.10E+01 2.54E+02 DATA RANGE 2.1
HISTOGRAMS 9 2.1.1 VISUALIZING UNIVARIATE DATA THROUGH STATISTICAL
PARAMETERS: A SUMMARY 14 2.2 DATA DISTRIBUTIONS: PROBABILITY DENSITY
FUNCTIONS 15 2.2.1 THE NORMAL DISTRIBUTION 16 2.2.2 SELECTED COMMENTS
ABOUT THE NORMAL DISTRIBUTION 17 2.2.3 TESTING THE NORMAL PROBABILITY
DENSITY FOR GOODNESS OF FIT 17 2.3 STATISTICAL HYPOTHESIS TESTING 19
2.3.1 THE CHI-SQUARE TEST 19 2.3.1.1 DISCUSSION 22 2.3.2 HYPOTHESIS TEST
FOR THE PROBABILITY PLOT 23 2.3.3 SO, WHICH HYPOTHESIS TEST SHOULD BE
USED? 23 2.4 OTHER DISTRIBUTION FUNCTIONS AND SUGGESTED APPLICATIONS 24
2.4.1 THE POISSON DISTRIBUTION 24 2.4.2 THE EXPONENTIAL DISTRIBUTION 24
2.4.3 THE LOG-NORMAL DISTRIBUTION 26 2.5 HOW DO I REPRODUCE RESULTS IN
THIS CHAPTER, OR ANALYZE MY OWN DATA. .. 26 2.5.1 ... USING VISUALJDATA?
26 2.5.2 ... USING MICROSOFT EXCEL? 27 2.5.3 ... USING MATLAB STUDENT
VERSION 5.3? 28 2.6 LITERATURE 29 EXERCISES 29 VII VIII CONTENTS C H A P
T E R 3 32 BIVARIATE DATA ANALYSIS DATA 1 1.70E+0C 5.20E+0G X 3.1.2
3.1.3 3.1.4 3.1.5 3.1.6 3.2 VISI 3.2.1 3.1 CORRELATION 32 3.1.1 THE
METHOD OF LEAST SQUARES 32 3.1.1.1 LINEAR LEAST SQUARES REGRESSION 34
3.1.1.2 LINEAR REGRESSION, TYPE II 36 ON THE ADEQUACY OF THE LINEAR
REGRESSION MODEL 38 HYPOTHESIS TESTING OF THE SLOPE, M 40 3.1.3.1
THEF-TEST 40 CORRELATION COEFFICIENT 42 COVARIANCE 43 ON THE STATISTICAL
SIGNIFICANCE OF THE CORRELATION COEFFICIENT 43 VISUAL REGRESSION 44
LINEAR MODELS 44 3.2.1.1 LEAST ABSOLUTE DEVIATION (LAD) REGRESSION 45
3.2.1.2 COMMENT: LEAST SQUARES OR LEAST ABSOLUTE DEVIATION, WHICH SHOULD
BE USED? 46 3.2.2 ANALYSIS OF RESIDUALS TO INFER THE NEED FOR NONLINEAR
REGRESSION 47 3.3 APPLICATION OF CORRELATION ANALYSIS TO THE
NEVADA_LANDSAT DATA SET 49 3.4 HOW DO I REPRODUCE RESULTS IN THIS
CHAPTER, OR ANALYZE MY OWN DATA. .. 54 3.4.1 ... USING VISUALJDATA? 54
3.4.2 ... USING MATLAB 5.3? 55 3.4.3 ... USING MICROSOFT EXCEL? 56 3.5
WEIGHTED REGRESSION 58 3.6 A REVIEW OF THE NEVADAJLANDSAT DAT A SET 59
3.7 LITERATURE 59 EXERCISES 59 C H A P T E R 4 61 MULTIVARIATE DATA
ANALYSIS 49JK5 542 7643 63A 9 ,2934 - I 0 2 1 ^ 43P? 7)31 9 734 93I S
736 4.1 ANALYSIS OF VARIANCE (ANOVA) 61 4.1.1 APPLICATION TO THE
NEVADA_LANDSAT_6X_DATA 63 4.2 STATISTICAL HYPOTHESIS TEST FOR TWO DATA
SETS 63 4.3 PRINCIPAL COMPONENTS ANALYSIS 65 4.3.1 PRINCIPAL COMPONENTS
65 4.3.2 STANDARDIZED PRINCIPAL COMPONENTS ANALYSIS 65 4.3.2.1 PRINCIPAL
COMPONENTS ARE EIGENVECTORS 66 4.3.2.2 THE MATRIX ALGEBRA 67 4.3.2.3
EIGENDECOMPOSING THE CORRELATION MATRIX FOR THE NEVADA_LANDSAT_6X_DATA
69 4.3.3 CORRESPONDENCE ANALYSIS 71 4.3.3.1 APPLICATION TO THE
NEVADA_LANDSAT DATA SET 74 CONTENTS IX 4.3.3.2 ACCOMMODATING MISSING
DATA IN CORRESPONDENCE ANALYSIS 76 4.4 MULTIVARIATE, LINEAR REGRESSION
(MULTIPLE REGRESSION) 77 4.4.1 LOGISTIC REGRESSION: AN APPLICATION OF
MULTIVARIATE, LINEAR REGRESSION TO THE NEVADAJJMDSAT DATA SET 77 4.5 HOW
DO I REPRODUCE RESULTS IN THIS CHAPTER, OR ANALYZE MY OWN DATA. .. 78
4.5.1 ... USING VISUALJDATA? 78 4.5.2 ... USING MICROSOFT EXCEL? 79
4.5.3 ... USING MATLAB 5.3? 80 4.6 A SUMMARY OF ANALYSES THUS FAR
OBTAINED OF THE NEVADA_LANDSAT DATA SET 83 4.7 LITERATURE 83 4.8 FINAL
THOUGHTS ON THE ROBUSTNESS OF PRINCIPAL COMPONENTS METHODS 83 EXERCISES
84 C H A P T E R 5 85 UNIVARIATE SPATIAL 5.1 ANALYSIS NUGGET - 1.138E+O2
SILL- 5.92BE+02 RANGE- 2.040E+O2 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9
AUTOCORRELATION 85 5.1.1 INTRODUCTION TO TIME SERIES ANALYSIS 85 SPATIAL
AUTOCORRELATION 88 5.2.1 THEVARIOGRAM 89 5.2.1.1 APPLICATION TO THE
NEVADAJJINDSAT DATA SET 5.2.1.2 DIRECTIONAL VARIOGRAM ANALYSIS 96
FRACTAL GEOMETRY 96 KRIGING: SPATIAL INTERPOLATION AS A FUNCTION OF
SPATIAL AUTOCORRELATION 99 5.4.1 COVARIANCE IS OBTAINED FROM THE
VARIOGRAM 102 THE PRACTICE OF KRIGING 103 104 93 5.5.1 5.5.2 5.5.3 5.5.4
5.5.5 5.5.6 5.5.7 5.5.8 5.5.9 VISUALIZATIONS 108 APPLICATION TO THE
NEVADA_LANDSAT_DATA 113 M-KRIGING 117 5.8.1 CROSS-VALIDATION 119 HOW DO
I REPRODUCE RESULTS IN THIS CHAPTER, OR ANALYZE MY OWN DATA 119 ON THE
NORMALITY OF THE SPATIAL DATA ON SECOND ORDER STATIONARITY 104 ON THE
VARIOGRAM MODEL 104 ON THE DESIGN OF THE GRID 105 MORE ON THE NUMBER OF
NEAREST NEIGHBORS USED FOR ESTIMATION 105 ON THE SIZE OF THE SEARCH
WINDOW (AND THE TYPE OF SEARCH STRATEGY) 105 ON THE CONCEPT OF SAMPLE
SUPPORT: PUNCTUAL OR BLOCK 106 ON THE NEED FOR A DATA TRANSFORM 106 ON
ANISOTROPIC SPATIAL AUTOCORRELATION MODELING 108 CONTENTS 5.9.1 ...
USING VISUAL_DATA? 119 5.9.1.1 TIME SERIES ANALYSIS 119 5.9.1.2
VARIOGRAM ANALYSIS 121 5.9.1.3 KRIGING 122 5.9.2 ...MATLAB 125 5.9.2.1
TIME SERIES ANALYSIS 125 5.9.2.2 VARIOGRAM ANALYSIS 126 5.9.2.3 KRIGING
127 5.10 LITERATURE 130 EXERCISES 130 C H A P T E R 6 132 MULTIVARIATE
SPATIAL DATA ANALYSIS 6.1 THEORY 132 6.1.1 SOLVING FOR THE COKRIGING
WEIGHTS 133 6.1.1.1 THE MATRIX SYSTEM: KRIGING: 133 6.1.1.2 THE MATRIX
SYSTEM: COKRIGING: 134 6.2 ON THE PRACTICE OF COKRIGING 139 6.3
AUTOKRIGEABILITY 146 6.3.1 APPLICATION TO PRINCIPAL COMPONENTS IMAGES
146 6.3.2 EXTENSION TO INDICATOR COKRIGING 147 6.4 THE UNDERSAMPLED CASE
148 6.4.1 ACCOMMODATING UNDERSAMPLING IN COKRIGING 149 6.4.2 APPLICATION
TO THE NEVADA_LANDSAT DATA SET: IMPROVING THE RESOLUTION OF THERMAL
IMAGES 149 6.5 HOW DO I REPRODUCE RESULTS IN THIS CHAPTER USING ...
6.5.1 VISUALJDATA? 151 6.5.2 USING MATLAB? 155 6.5.3 USING MICROSOFT
EXCEL? 156 6.6 LITERATURE 157 6.7 FINAL THOUGHTS ON COKRIGING 157
EXERCISES 158 151 C H A P T E R 7 159 SPATIAL SIMULATION 7.1 RANDOM
NUMBERS AND THEIR GENERATION 159 7.2 ONE-DIMENSIONAL SPATIAL SIMULATION
162 7.3 EXTENSION TO THREE-DIMENSIONAL SPACE: THE METHOD OF RANDOM LINES
164 7.4 SIMULATION USING FRACTALS 165 7.5 NONCONDITIONAL SIMULATION: THE
NEED FOR DATA TRANSFORMATION 167 7.6 NONCONDITIONAL SIMULATION OF
NEVADA_LANDSAT_DATA 168 7.6.1 RANDOM LINES ALGORITHM 168 7.6.1.1 VISIBLE
BLUE REFLECTANCE 169 7.6.1.2 THERMAL EMISSION 170 7.6.2 THE FRACTAL
ALGORITHM 172 CONTENTS XI 7.7 CONDITIONING THE SIMULATION 173 7.7.1.
APPLICATION TO THE NEVADA_LANDSAT_DATA 173 7.7.1.1 VISIBLE BLUE
REFLECTANCE 174 7.7.1.2 THERMAL EMISSIVITY 174 7.8 WHY IS SPATIAL
SIMULATION USEFUL? 175 7.9 HOW DO I REPRODUCE RESULTS IN THIS CHAPTER,
OR EXPERIMENT WITH MY OWN DATA ... 176 7.9.1 ... USING VISUALJDATA? 176
7.10 LITERATURE 178 EXERCISES 179 C H A P T E R 8 180 DIGITAL IMAGE
PROCESSING 8.1 PIXELS 180 8.2 ADJUSTING PIXEL CONTRAST 181 8.2.1
ALGORITHMS FOR CONTRAST ADJUSTMENT 182 8.2.1.1 SIMPLE, LINEAR 183
8.2.1.2 MULTIPLE, LINEAR 185 8.2.1.3 HISTOGRAM EQUALIZATION 185 8.2.1.4
BINARY STRETCH 186 8.3 FILTERING DIGITAL IMAGES 188 8.3.1 IN A DIGITAL
IMAGE, WHAT IS A LOW FREQUENCY FEATURE? WHAT IS A HIGH FREQUENCY
FEATURE? 189 8.3.2 SPATIAL CONVOLUTION FILTERING: LOW-PASS, HIGH-PASS,
HIGH-BOOST, AND CUSTOM STRATEGIES 190 8.3.2.1 BOOSTING FACTOR OF 4 193
8.3.2.2 BOOSTING FACTOR OF 8 193 8.3.2.3 EAST-WEST CUSTOM HIGH-PASS
FILTER (3X3) 193 8.3.2.4 NORTH-SOUTH CUSTOM HIGH-PASS FILTER (3X3) 193
8.4 PRINCIPAL COMPONENTS ANALYSIS OF MULTISPECTRAL DIGITAL IMAGES 194
8.5 DUST DEVILS ON MARS 198 8.5.1 TRUE COLOR COMPOSITING: THE 24-BIT
BITMAP 199 8.6 HOW DO I REPRODUCE RESULTS IN THIS CHAPTER, OR ANALYZE MY
OWN IMAGES ... 203 8.6.1 ... USING VISUAL_DATA? 203 8.6.1.1 FILE FORMAT
CONVERSION 206 8.6.1.2 CONTRAST ADJUSTMENT 207 8.6.1.3 DIGITAL FILTERING
208 8.6.1.4 IMAGE ADDITION AND SUBTRACTION 210 8.6.2 ... USING MATLAB
5.3 (STUDENT VERSION)? 210 8.6.3 ...WITH THE HELP OF MICROSOFT EXCEL?
215 8.7 LITERATURE 217 EXERCISES 217 XII CONTENTS C H A P T E R 9 219
COMPOSITE VISUALIZATIONS 9.1 MULTISPECTRAL DIGITAL IMAGE COMPOSITING FOR
CLASSIFICATION 219 9.1.1 CLASSIFICATION OF TEXTURE 224 9.2 COMPOSITING A
CONTOUR MAP WITH A DIGITAL IMAGE 225 9.3 THREE-DIMENSIONAL PERSPECTIVES
227 9.4 ANIMATION 233 9.5 HOW DO I REPRODUCE RESULTS IN THIS CHAPTER, OR
PURSUE MY OWN IDEAS ... 234 9.5.1 ... USING VISUAL_DATA? 234 9.5.2 ...
USING MATLAB? 237 9.6 LITERATURE 239 EXERCISES 240 EPILOGUE 241 WHY BE
NORMAL? 241 HUMAN PREROGATIVE 241 IS KRIGING THE BEST SPATIAL
INTERPOLATION METHOD? 243 THE ROBUSTNESS OF KRIGING 244 APPENDICES 247 A
CRITICAL VALUES OF THE CHI-SQUARE DISTRIBUTION 248 B CRITICAL VALUES OF
SQUARED CORRELATION COEFFICIENT, P-PLOT 251 C CRITICAL VALUES OFF
DISTRIBUTION 253 D CRITICAL VALUES OFT DISTRIBUTION 257 BIBLIOGRAPHY 259
INDEX 263
|
| any_adam_object | 1 |
| author | Carr, James R. |
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| building | Verbundindex |
| bvnumber | BV015771457 |
| callnumber-first | Q - Science |
| callnumber-label | QE33 |
| callnumber-raw | QE33.2.S82 |
| callnumber-search | QE33.2.S82 |
| callnumber-sort | QE 233.2 S82 |
| callnumber-subject | QE - Geology |
| classification_rvk | RB 10103 RB 10229 RB 10239 TG 1100 |
| ctrlnum | (OCoLC)48092256 (DE-599)BVBBV015771457 |
| dewey-full | 551/.0285/66 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 551 - Geology, hydrology, meteorology |
| dewey-raw | 551/.0285/66 |
| dewey-search | 551/.0285/66 |
| dewey-sort | 3551 3285 266 |
| dewey-tens | 550 - Earth sciences |
| discipline | Geologie / Paläontologie Geographie |
| format | Book |
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| illustrated | Illustrated |
| indexdate | 2024-07-09T19:57:04Z |
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| language | English |
| lccn | 2001052353 |
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| physical | XIV, 267 S. Ill., graph. Darst. CD-ROM (12 cm) |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Prentice Hall |
| record_format | marc |
| spelling | Carr, James R. Verfasser aut Data visualization in the geological sciences James R. Carr Upper Saddle River, NJ Prentice Hall 2002 XIV, 267 S. Ill., graph. Darst. CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index. - Erscheint: November 2011 CD-ROM contains: Visual_Data -- Data files used in text -- Digital images used in text. Aardwetenschappen gtt Dataprocessing gtt Grafische methoden gtt Histogrammen gtt Datenverarbeitung Geologie Geology Graphic methods Geology Graphic methods Data processing Geology Statistical methods Geology Statistical methods Data processing Information visualization Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Geowissenschaften (DE-588)4020288-4 gnd rswk-swf Geowissenschaften (DE-588)4020288-4 s Datenverarbeitung (DE-588)4011152-0 s DE-604 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012766468&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Carr, James R. Data visualization in the geological sciences Aardwetenschappen gtt Dataprocessing gtt Grafische methoden gtt Histogrammen gtt Datenverarbeitung Geologie Geology Graphic methods Geology Graphic methods Data processing Geology Statistical methods Geology Statistical methods Data processing Information visualization Datenverarbeitung (DE-588)4011152-0 gnd Geowissenschaften (DE-588)4020288-4 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4020288-4 |
| title | Data visualization in the geological sciences |
| title_auth | Data visualization in the geological sciences |
| title_exact_search | Data visualization in the geological sciences |
| title_full | Data visualization in the geological sciences James R. Carr |
| title_fullStr | Data visualization in the geological sciences James R. Carr |
| title_full_unstemmed | Data visualization in the geological sciences James R. Carr |
| title_short | Data visualization in the geological sciences |
| title_sort | data visualization in the geological sciences |
| topic | Aardwetenschappen gtt Dataprocessing gtt Grafische methoden gtt Histogrammen gtt Datenverarbeitung Geologie Geology Graphic methods Geology Graphic methods Data processing Geology Statistical methods Geology Statistical methods Data processing Information visualization Datenverarbeitung (DE-588)4011152-0 gnd Geowissenschaften (DE-588)4020288-4 gnd |
| topic_facet | Aardwetenschappen Dataprocessing Grafische methoden Histogrammen Datenverarbeitung Geologie Geology Graphic methods Geology Graphic methods Data processing Geology Statistical methods Geology Statistical methods Data processing Information visualization Geowissenschaften |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012766468&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT carrjamesr datavisualizationinthegeologicalsciences |