Spatial data analysis in ecology and agriculture using R:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton, Fla. [u.a.]
CRC Press, Taylor & Francis Group
2012
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024821017&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XV, 631 S. Ill., graph. Darst. |
| ISBN: | 9781439819135 |
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| 245 | 1 | 0 | |a Spatial data analysis in ecology and agriculture using R |c Richard E. Plant |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press, Taylor & Francis Group |c 2012 | |
| 300 | |a XV, 631 S. |b Ill., graph. Darst. | ||
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| 650 | 4 | |a Landwirtschaft | |
| 650 | 4 | |a Agriculture |x Statistical methods | |
| 650 | 4 | |a Spatial analysis (Statistics) | |
| 650 | 4 | |a R (Computer program language) | |
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| adam_text | Titel: Spatial data analysis in ecology and agriculture using R
Autor: Plant, Richard E
Jahr: 2012
Contents
Preface..............................................................................................................................................xi
Acknowledgments......................................................................................................................xiii
Author.............................................................................................................................................xv
1. Working with Spatial Data...................................................................................................1
1.1 Introduction...................................................................................................................1
1.2 Analysis of Spatial Data...............................................................................................5
1.2.1 Types of Spatial Data.......................................................................................5
1.2.2 Components of Spatial Data...........................................................................7
1.2.3 Topics Covered in the Text..............................................................................7
1.3 Data Sets Analyzed in This Book...............................................................................9
1.3.1 Data Set 1: Yellow-Billed Cuckoo Habitat.....................................................9
1.3.2 Data Set 2: Environmental Characteristics of Oak Woodlands..............11
1.3.3 Data Set 3: Uruguayan Rice Farmers..........................................................12
1.3.4 Data Set 4: Factors Underlying Yield in Two Fields..................................13
1.3.5 Comparing the Data Sets..............................................................................14
1.4 Further Reading..........................................................................................................15
2. R Programming Environment...........................................................................................17
2.1 Introduction.................................................................................................................17
2.1.1 Introduction to R............................................................................................17
2.1.2 Setting Up Yourself to Use This Book........................................................18
2.2 R Basics.........................................................................................................................19
2.3 Programming Concepts.............................................................................................23
2.3.1 Looping and Branching................................................................................23
2.3.2 Functional Programming.............................................................................25
2.4 Handling Data in R.....................................................................................................27
2.4.1 Data Structures in R......................................................................................27
2.4.2 Basic Data Input and Output.......................................................................30
2.4.3 Spatial Data Structures.................................................................................31
2.5 Writing Functions in R...............................................................................................37
2.6 Graphics in R...............................................................................................................39
2.6.1 Traditional Graphics in R: Attribute Data..................................................40
2.6.2 Traditional Graphics in R: Spatial Data......................................................44
2.6.3 Trellis Graphics in R: Attribute Data..........................................................47
2.6.4 Trellis Graphics in R: Spatial Data...............................................................49
2.6.5 Using Color in R.............................................................................................50
2.7 Other Software Packages...........................................................................................53
2.8 Further Reading..........................................................................................................53
Exercises..................................................................................................................................54
Contents
3. Statistical Properties of Spatially Autocorrelated Data...............................................59
3.1 Introduction.................................................................................................................59
3.2 Components of a Spatial Random Process..............................................................60
3.2.1 Spatial Trends in Data...................................................................................60
3.2.2 Stationarity......................................................................................................65
3.3 Monte Carlo Simulation.............................................................................................68
3.4 Review of Hypothesis and Significance Testing....................................................70
3.5 Modeling Spatial Autocorrelation............................................................................75
3.5.1 Monte Carlo Simulation of Time Series......................................................75
3.5.2 Modeling Spatial Contiguity........................................................................80
3.5.3 Modeling Spatial Association in R..............................................................84
3.6 Application to Field Data...........................................................................................90
3.7 Further Reading..........................................................................................................92
Exercises..................................................................................................................................93
4. Measures of Spatial Autocorrelation................................................................................95
4.1 Introduction.................................................................................................................95
4.2 Preliminary Considerations......................................................................................95
4.2.1 Measurement Scale........................................................................................95
4.2.1.1 Nominal Scale Data.......................................................................97
4.2.1.2 Ordinal Scale Data.........................................................................97
4.2.1.3 Interval Scale Data.........................................................................97
4.2.1.4 Ratio Scale Data..............................................................................98
4.2.2 Resampling and Randomization Assumptions........................................98
4.2.2.1 Resampling Assumption...............................................................98
4.2.2.2 Randomization Assumption........................................................99
4.2.3 Testing the Null Hypothesis........................................................................99
4.3 Join-Count Statistics..................................................................................................100
4.4 Moran s I and Geary s c............................................................................................104
4.5 Measures of Autocorrelation Structure.................................................................107
4.5.1 Moran Correlogram.....................................................................................107
4.5.2 Moran Scatterplot.........................................................................................108
4.5.3 Local Measures of Autocorrelation...........................................................Ill
4.5.4 Geographically Weighted Regression.......................................................114
4.6 Measuring Autocorrelation of Spatially Continuous Data.................................116
4.6.1 Variogram.....................................................................................................116
4.6.2 Covariogram and Correlogram.................................................................121
4.7 Further Reading........................................................................................................122
Exercises................................................................................................................................123
5. Sampling and Data Collection.........................................................................................125
5.1 Introduction...............................................................................................................125
5.2 Preliminary Considerations....................................................................................128
5.2.1 Artificial Population....................................................................................128
5.2.2 Accuracy, Bias, Precision, and Variance...................................................131
5.2.3 Comparison Procedures.............................................................................131
5.3 Developing the Sampling Patterns.........................................................................132
5.3.1 Random Sampling.......................................................................................132
5.3.2 Geographically Stratified Sampling..........................................................134
Contents vu
5.3.3 Sampling on a Regular Grid......................................................................136
5.3.4 Stratification Based on a Covariate............................................................137
5.3.5 Cluster Sampling.........................................................................................143
5.4 Methods for Variogram Estimation........................................................................144
5.5 Estimating the Sample Size.....................................................................................146
5.6 Sampling for Thematic Mapping............................................................................147
5.7 Design-Based and Model-Based Sampling...........................................................148
5.8 Further Reading........................................................................................................152
Exercises................................................................................................................................154
6. Preparing Spatial Data for Analysis...............................................................................155
6.1 Introduction...............................................................................................................155
6.2 Quality of Attribute Data.........................................................................................156
6.2.1 Dealing with Outliers and Contaminants...............................................156
6.2.2 Quality of Ecological Survey Data............................................................158
6.2.3 Quality of Automatically Recorded Data.................................................158
6.3 Spatial Interpolation Procedures............................................................................162
6.3.1 Inverse Weighted Distance Interpolation.................................................162
6.3.2 Kriging Interpolation..................................................................................167
6.3.3 Cokriging Interpolation..............................................................................170
6.4 Spatial Rectification and Alignment of Data........................................................175
6.4.1 Definitions of Scale-Related Processes.....................................................175
6.4.2 Change of Coverage.....................................................................................176
6.4.3 Change of Support.......................................................................................180
6.5 Further Reading........................................................................................................183
Exercises................................................................................................................................184
7. Preliminary Exploration of Spatial Data.......................................................................187
7.1 Introduction...............................................................................................................187
7.2 Data Set 1....................................................................................................................188
7.3 Data Set 2....................................................................................................................201
7.4 Data Set 3....................................................................................................................213
7.5 Data Set 4....................................................................................................................224
7.6 Further Reading........................................................................................................235
Exercises................................................................................................................................235
8. Multivariate Methods for Spatial Data Exploration...................................................237
8.1 Introduction...............................................................................................................237
8.2 Principal Components Analysis.............................................................................237
8.2.1 Introduction to Principal Components Analysis....................................237
8.2.2 Principal Components Analysis of Data Set 2.........................................245
8.2.3 Application to Data Set 4, Field 1...............................................................246
8.3 Classification and Regression Trees (a.k.a. Recursive Partitioning)..................248
8.3.1 Introduction to the Method........................................................................248
8.3.2 Mathematics of Recursive Partitioning....................................................250
8.3.3 Exploratory Analysis of Data Set 2 with Regression Trees....................251
8.3.4 Exploratory Analysis of Data Set 3 with Recursive Partitioning..........258
8.3.5 Exploratory Analysis of Field 4.1 with Recursive Partitioning.............264
viii Contents
8.4 Random Forest...........................................................................................................268
8.4.1 Introduction to Random Forest.................................................................268
8.4.2 Application to Data Set 2............................................................................271
8.5 Further Reading........................................................................................................274
Exercises................................................................................................................................275
9. Spatial Data Exploration via Multiple Regression......................................................277
9.1 Introduction...............................................................................................................277
9.2 Multiple Linear Regression.....................................................................................277
9.2.1 Many Perils of Model Selection.................................................................277
9.2.2 Multicollinearity, Added Variable Plots, and Partial Residual Plots..........282
9.2.2.1 Added Variable Plots...................................................................286
9.2.2.2 Partial Residual Plots...................................................................288
9.2.3 Cautious Approach to Model Selection as an Exploratory Tool...........290
9.3 Building a Multiple Regression Model for Field 4.1.............................................291
9.4 Generalized Linear Models.....................................................................................301
9.4.1 Introduction to Generalized Linear Models............................................301
9.4.2 Multiple Logistic Regression Model for Data Set 2.................................306
9.4.3 Logistic Regression Model of Count Data for Data Set 1.......................314
9.4.4 Analysis of the Counts of Data Set 1: Zero-Inflated Poisson Data.......318
9.5 Further Reading........................................................................................................322
Exercises................................................................................................................................323
10. Variance Estimation, the Effective Sample Size, and the Bootstrap........................325
10.1 Introduction...............................................................................................................325
10.2 Bootstrap Estimation of the Standard Error.........................................................329
10.3 Bootstrapping Time Series Data.............................................................................332
10.3.1 Problem with Correlated Data...................................................................332
10.3.2 Block Bootstrap............................................................................................335
10.3.3 Parametric Bootstrap...................................................................................338
10.4 Bootstrapping Spatial Data......................................................................................339
10.4.1 Spatial Block Bootstrap...............................................................................339
10.4.2 Parametric Spatial Bootstrap......................................................................344
10.4.3 Power of the Tests........................................................................................345
10.5 Application to the EM38 Data.................................................................................346
10.6 Further Reading........................................................................................................349
Exercises................................................................................................................................349
11. Measures of Bivariate Association between Two Spatial Variables........................351
11.1 Introduction...............................................................................................................351
11.2 Estimating and Testing the Correlation Coefficient............................................354
11.2.1 Correlation Coefficient................................................................................354
11.2.2 Clifford et al. Correction.............................................................................356
11.2.3 Bootstrap Variance Estimate......................................................................359
11.2.4 Application to the Example Problem........................................................361
11.3 Contingency Tables...................................................................................................364
11.3.1 Large Sample Size Contingency Tables....................................................364
11.3.2 Small Sample Size Contingency Tables....................................................371
Contents ix
11.4 Mantel and Partial Mantel Statistics......................................................................375
11.4.1 Mantel Statistic.............................................................................................375
11.4.2 Partial Mantel Test.......................................................................................379
11.5 Modifiable Areal Unit Problem and Ecological Fallacy......................................382
11.5.1 Modifiable Areal Unit Problem..................................................................382
11.5.2 Ecological Fallacy.........................................................................................386
11.6 Further Reading........................................................................................................388
Exercises................................................................................................................................388
12. Mixed Model.......................................................................................................................391
12.1 Introduction...............................................................................................................391
12.2 Basic Properties of the Mixed Model.....................................................................395
12.3 Application to Data Set 3..........................................................................................396
12.4 Incorporating Spatial Autocorrelation...................................................................401
12.5 Generalized Least Squares......................................................................................406
12.6 Spatial Logistic Regression......................................................................................409
12.6.1 Upscaling Data Set 2 in the Coast Range.................................................409
12.6.2 Incorporation of Spatial Autocorrelation.................................................413
12.7 Further Reading........................................................................................................420
Exercises................................................................................................................................420
13. Regression Models for Spatially Autocorrelated Data...............................................423
13.1 Introduction...............................................................................................................423
13.2 Detecting Spatial Autocorrelation in a Regression Model..................................428
13.3 Models for Spatial Processes...................................................................................430
13.3.1 Spatial Lag Model........................................................................................430
13.3.2 Spatial Error Model.....................................................................................432
13.4 Determining the Appropriate Regression Model................................................433
13.4.1 Formulation of the Problem.......................................................................433
13.4.2 Lagrange Multiplier Test.............................................................................434
13.5 Fitting the Spatial Lag and Spatial Error Models.................................................436
13.6 Conditional Autoregressive Model........................................................................438
13.7 Application of SAR and CAR Models to Field Data............................................439
13.7.1 Fitting the Data.............................................................................................439
13.7.2 Comparison of the Mixed and Autoregressive Models.........................443
13.8 Autologistic Model for Binary Data.......................................................................443
13.9 Further Reading........................................................................................................447
Exercises................................................................................................................................448
14. Bayesian Analysis of Spatially Autocorrelated Data..................................................449
14.1 Introduction...............................................................................................................449
14.2 Markov Chain Monte Carlo Methods....................................................................453
14.3 Introduction to WinBUGS........................................................................................459
14.3.1 WinBUGS Basics...........................................................................................459
14.3.2 WinBUGS Diagnostics.................................................................................463
14.3.3 Introduction to R2WinBUGS......................................................................464
14.3.4 Generalized Linear Models in WinBUGS................................................472
14.4 Hierarchical Models.................................................................................................474
Contents
14.5 Incorporation of Spatial Effects...............................................................................479
14.5.1 Spatial Effects in the Linear Model...........................................................479
14.5.2 Application to Data Set 3............................................................................483
14.6 Further Reading........................................................................................................487
Exercises................................................................................................................................488
15. Analysis of Spatiotemporal Data....................................................................................489
15.1 Introduction...............................................................................................................489
15.2 Spatiotemporal Cluster Analysis............................................................................489
15.3 Factors Underlying Spatiotemporal Yield Clusters..............................................500
15.4 Bayesian Spatiotemporal Analysis.........................................................................505
15.4.1 Introduction to Bayesian Updating...........................................................505
15.4.2 Application of Bayesian Updating to Data Set 3.....................................508
15.5 Other Approaches to Spatiotemporal Modeling..................................................513
15.5.1 Models for Dispersing Populations...........................................................513
15.5.2 State and Transition Models.......................................................................514
15.6 Further Reading........................................................................................................515
Exercises................................................................................................................................515
16. Analysis of Data from Controlled Experiments..........................................................517
16.1 Introduction...............................................................................................................517
16.2 Classical Analysis of Variance................................................................................518
16.3 Comparison of Methods..........................................................................................522
16.3.1 Comparison Statistics..................................................................................522
16.3.2 Papadakis Nearest Neighbor Method......................................................525
16.3.3 Trend Method...............................................................................................526
16.3.4 Correlated Errors Method.......................................................................527
16.3.5 Published Comparisons of the Methods..................................................529
16.4 Pseudoreplicated Data and the Effective Sample Size.........................................529
16.4.1 Pseudoreplicated Comparisons.................................................................529
16.4.2 Calculation of the Effective Sample Size..................................................530
16.4.3 Application to Field Data............................................................................533
16.5 Further Reading........................................................................................................535
Exercises................................................................................................................................535
17. Assembling Conclusions...................................................................................................537
17.1 Introduction...............................................................................................................537
17.2 Data Set 1....................................................................................................................537
17.3 Data Set 2....................................................................................................................542
17.4 Data Set 3....................................................................................................................547
17.5 Data Set 4....................................................................................................................550
17.6 Conclusions................................................................................................................554
Appendix A: Review of Mathematical Concepts................................................................555
Appendix B:The Data Sets......................................................................................................581
Appendix C: An R Thesaurus.................................................................................................589
References...................................................................................................................................599
Index.............................................................................................................................................619
|
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| id | DE-604.BV039963325 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:06:15Z |
| institution | BVB |
| isbn | 9781439819135 |
| language | English |
| lccn | 2012000491 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024821017 |
| oclc_num | 785852922 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM DE-188 DE-20 DE-83 |
| owner_facet | DE-M49 DE-BY-TUM DE-188 DE-20 DE-83 |
| physical | XV, 631 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| spellingShingle | Plant, Richard E. Spatial data analysis in ecology and agriculture using R Landwirtschaft Agriculture Statistical methods Spatial analysis (Statistics) R (Computer program language) Raumdaten (DE-588)4206012-6 gnd Statistik (DE-588)4056995-0 gnd Umweltdaten (DE-588)4452653-2 gnd Landwirtschaft (DE-588)4034402-2 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4206012-6 (DE-588)4056995-0 (DE-588)4452653-2 (DE-588)4034402-2 (DE-588)4705956-4 |
| title | Spatial data analysis in ecology and agriculture using R |
| title_auth | Spatial data analysis in ecology and agriculture using R |
| title_exact_search | Spatial data analysis in ecology and agriculture using R |
| title_full | Spatial data analysis in ecology and agriculture using R Richard E. Plant |
| title_fullStr | Spatial data analysis in ecology and agriculture using R Richard E. Plant |
| title_full_unstemmed | Spatial data analysis in ecology and agriculture using R Richard E. Plant |
| title_short | Spatial data analysis in ecology and agriculture using R |
| title_sort | spatial data analysis in ecology and agriculture using r |
| topic | Landwirtschaft Agriculture Statistical methods Spatial analysis (Statistics) R (Computer program language) Raumdaten (DE-588)4206012-6 gnd Statistik (DE-588)4056995-0 gnd Umweltdaten (DE-588)4452653-2 gnd Landwirtschaft (DE-588)4034402-2 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Landwirtschaft Agriculture Statistical methods Spatial analysis (Statistics) R (Computer program language) Raumdaten Statistik Umweltdaten R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024821017&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT plantricharde spatialdataanalysisinecologyandagricultureusingr |
Inhaltsverzeichnis
Paper/Kapitel scannen lassen
Paper/Kapitel scannen lassen
Teilbibliothek Weihenstephan
| Signatur: |
1002 LAN 048f 2012 B 1321 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |