Atjaunināt sīkdatņu piekrišanu

E-grāmata: Spatial Data Analysis in Ecology and Agriculture Using R

3.00/5 (1 ratings by Goodreads)
(University of California, Davis, USA)
  • Formāts: 684 pages
  • Izdošanas datums: 07-Dec-2018
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781351189897
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 50,08 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Spatial Data Analysis in Ecology and Agriculture Using RPalielināt
  • Formāts: 684 pages
  • Izdošanas datums: 07-Dec-2018
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781351189897
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

Key features:





Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages





Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions.

Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.
Preface to the First Edition xiii
Preface to the Second Edition xv
Author xvii
1 Working with Spatial Data 1(18)
1.1 Introduction
1(4)
1.2 Analysis of Spatial Data
5(4)
1.2.1 Types of Spatial Data
5(2)
1.2.2 The Components of Spatial Data
7(1)
1.2.3 Spatial Data Models
7(1)
1.2.4 Topics Covered in the Text
8(1)
1.3 The Data Sets Analyzed in This Book
9(7)
1.3.1 Data Set 1: Yellow-Billed Cuckoo Habitat
10(2)
1.3.2 Data Set 2: Environmental Characteristics of Oak Woodlands
12(1)
1.3.3 Data Set 3: Uruguayan Rice Farmers
13(1)
1.3.4 Data Set 4: Factors Underlying Yield in Two Fields
14(1)
1.3.5 Comparing the Data Sets
15(1)
1.4 Further Reading
16(3)
2 The R Programming Environment 19(48)
2.1 Introduction
19(2)
2.1.1 Introduction to R
19(1)
2.1.2 Setting Yourself Up to Use This Book
20(1)
2.2 R Basics
21(5)
2.3 Programming Concepts
26(4)
2.3.1 Looping and Branching
26(2)
2.3.2 Functional Programming
28(2)
2.4 Handling Data in R
30(13)
2.4.1 Data Structures in R
30(3)
2.4.2 Basic Data Input and Output
33(1)
2.4.3 Spatial Data Structures
34(9)
2.5 Writing Functions in R
43(3)
2.6 Graphics in R
46(15)
2.6.1 Traditional Graphics in R: Attribute Data
47(5)
2.6.2 Traditional Graphics in R: Spatial Data
52(3)
2.6.3 Trellis Graphics in R, Attribute Data
55(2)
2.6.4 Trellis Graphics in R, Spatial Data
57(2)
2.6.5 Using Color in R
59(2)
2.7 Continuing on from Here with R
61(1)
2.8 Further Reading
62(1)
Exercises
62(5)
3 Statistical Properties of Spatially Autocorrelated Data 67(38)
3.1 Introduction
67(1)
3.2 Components of a Spatial Random Process
68(9)
3.2.1 Spatial Trends in Data
68(6)
3.2.2 Stationarity
74(3)
3.3 Monte Carlo Simulation
77(2)
3.4 A Review of Hypothesis and Significance Testing
79(5)
3.5 Modeling Spatial Autocorrelation
84(14)
3.5.1 Monte Carlo Simulation of Time Series
84(4)
3.5.2 Modeling Spatial Contiguity
88(5)
3.5.3 Modeling Spatial Association in R
93(5)
3.6 Application to Field Data
98(5)
3.6.1 Setting Up the Data
98(3)
3.6.2 Checking Sequence Validity
101(1)
3.6.3 Determining Spatial Autocorrelation
102(1)
3.7 Further Reading
103(1)
Exercises
103(2)
4 Measures of Spatial Autocorrelation 105(30)
4.1 Introduction
105(1)
4.2 Preliminary Considerations
105(5)
4.2.1 Measurement Scale
105(3)
4.2.2 Resampling and Randomization Assumptions
108(1)
4.2.3 Testing the Null Hypothesis
109(1)
4.3 Join-Count Statistics
110(4)
4.4 Moran's I and Geary's c
114(3)
4.5 Measures of Autocorrelation Structure
117(10)
4.5.1 The Moran Correlogram
117(2)
4.5.2 The Moran Scatterplot
119(2)
4.5.3 Local Measures of Autocorrelation
121(3)
4.5.4 Geographically Weighted Regression
124(3)
4.6 Measuring Autocorrelation of Spatially Continuous Data
127(6)
4.6.1 The Variogram
127(5)
4.6.2 The Covariogram and the Correlogram
132(1)
4.7 Further Reading
133(1)
Exercises
133(2)
5 Sampling and Data Collection 135(32)
5.1 Introduction
135(3)
5.2 Preliminary Considerations
138(4)
5.2.1 The Artificial Population
138(3)
5.2.2 Accuracy, Bias, Precision, and Variance
141(1)
5.2.3 Comparison Procedures
142(1)
5.3 Developing the Sampling Patterns
142(12)
5.3.1 Random Sampling
142(2)
5.3.2 Geographically Stratified Sampling
144(2)
5.3.3 Sampling on a Regular Grid
146(2)
5.3.4 Stratification Based on a Covariate
148(5)
5.3.5 Cluster Sampling
153(1)
5.4 Methods for Variogram Estimation
154(3)
5.5 Estimating the Sample Size
157(1)
5.6 Sampling for Thematic Mapping
158(1)
5.7 Design-Based and Model-Based Sampling
159(5)
5.8 Further Reading
164(1)
Exercises
164(3)
6 Preparing Spatial Data for Analysis 167(32)
6.1 Introduction
167(1)
6.2 Quality of Attribute Data
168(7)
6.2.1 Dealing with Outliers and Contaminants
168(2)
6.2.2 Quality of Ecological Survey Data
170(1)
6.2.3 Quality of Automatically Recorded Data
170(5)
6.3 Spatial Interpolation Procedures
175(13)
6.3.1 Inverse Weighted Distance Interpolation
175(5)
6.3.2 Kriging Interpolation
180(3)
6.3.3 Cokriging Interpolation
183(5)
6.4 Spatial Rectification and Alignment of Data
188(8)
6.4.1 Definitions of Scale Related Processes
188(2)
6.4.2 Change of Coverage
190(3)
6.4.3 Change of Support
193(3)
6.5 Further Reading
196(1)
Exercises
197(2)
7 Preliminary Exploration of Spatial Data 199(56)
7.1 Introduction
199(2)
7.2 Data Set 1
201(13)
7.3 Data Set 2
214(15)
7.4 Data Set 3
229(11)
7.5 Data Set 4
240(11)
7.6 Further Reading
251(1)
Exercises
251(4)
8 Data Exploration Using Non-Spatial Methods: The Linear Model 255(52)
8.1 Introduction
255(1)
8.2 Multiple Linear Regression
255(15)
8.2.1 The Many Perils of Model Selection
255(6)
8.2.2 Multicollinearity, Added Variable Plots, and Partial Residual Plots
261(8)
8.2.3 A Cautious Approach Model Selection as an Exploratory Tool
269(1)
8.3 Building a Multiple Regression Model for Field 4.1
270(11)
8.4 Generalized Linear Models
281(22)
8.4.1 Introduction to Generalized Linear Models
281(7)
8.4.2 Multiple Logistic Regression Model for Data Set 2
288(7)
8.4.3 Logistic Regression Model of Count Data for Data Set 1
295(4)
8.4.4 Analysis of the Counts of Data Set 1: Zero-Inflated Poisson Data
299(4)
8.5 Further Reading
303(1)
Exercises
304(3)
9 Data Exploration Using Non-Spatial Methods: Nonparametric Methods 307(40)
9.1 Introduction
307(1)
9.2 The Generalized Additive Model
307(10)
9.3 Classification and Regression Trees (a.k.a. Recursive Partitioning)
317(22)
9.3.1 Introduction to the Method
317(3)
9.3.2 The Mathematics of Recursive Partitioning
320(1)
9.3.3 Exploratory Analysis of Data Set 2 with Regression Trees
321(7)
9.3.4 Exploratory Analysis of Data Set 3 with Recursive Partitioning
328(6)
9.3.5 Exploratory Analysis of Field 4.1 with Recursive Partitioning
334(5)
9.4 Random Forest
339(6)
9.4.1 Introduction to Random Forest
339(3)
9.4.2 Application to Data Set 2
342(3)
9.5 Further Reading
345(1)
Exercises
345(2)
10 Variance Estimation, the Effective Sample Size, and the Bootstrap 347(26)
10.1 Introduction
347(4)
10.2 Bootstrap Estimation of the Standard Error
351(4)
10.3 Bootstrapping Time Series Data
355(7)
10.3.1 The Problem with Correlated Data
355(2)
10.3.2 The Block Bootstrap
357(3)
10.3.3 The Parametric Bootstrap
360(2)
10.4 Bootstrapping Spatial Data
362(6)
10.4.1 The Spatial Block Bootstrap
362(4)
10.4.2 The Parametric Spatial Bootstrap
366(2)
10.4.3 Power of the Tests
368(1)
10.5 Application to the EM38 Data
368(3)
10.6 Further Reading
371(1)
Exercises
372(1)
11 Measures of Bivariate Association between Two Spatial Variables 373(40)
11.1 Introduction
373(3)
11.2 Estimating and Testing the Correlation Coefficient
376(10)
11.2.1 The Correlation Coefficient
376(2)
11.2.2 The Clifford et al. (1989) Correction
378(3)
11.2.3 The Bootstrap Variance Estimate
381(2)
11.2.4 Application to the Example Problem
383(3)
11.3 Contingency Tables
386(12)
11.3.1 Large Sample Size Contingency Tables
386(7)
11.3.2 Small Sample Size Contingency Tables
393(5)
11.4 The Mantel and Partial Mantel Statistics
398(6)
11.4.1 The Mantel Statistic
398(3)
11.4.2 The Partial Mantel Test
401(3)
11.5 The Modifiable Areal Unit Problem and the Ecological Fallacy
404(6)
11.5.1 The Modifiable Areal Unit Problem
404(4)
11.5.2 The Ecological Fallacy
408(2)
11.6 Further Reading
410(1)
Exercises
410(3)
12 The Mixed Model 413(32)
12.1 Introduction
413(4)
12.2 Basic Properties of the Mixed Model
417(2)
12.3 Application to Data Set 3
419(3)
12.4 Incorporating Spatial Autocorrelation
422(7)
12.5 Generalized Least Squares
429(2)
12.6 Spatial Logistic Regression
431(12)
12.6.1 Upscaling Data Set 2 in the Coast Range
431(5)
12.6.2 The Incorporation of Spatial Autocorrelation
436(7)
12.7 Further Reading
443(1)
Exercises
444(1)
13 Regression Models for Spatially Autocorrelated Data 445(22)
13.1 Introduction
445(5)
13.2 Detecting Spatial Autocorrelation in a Regression Model
450(2)
13.3 Models for Spatial Processes
452(3)
13.3.1 The Spatial Lag Model
452(2)
13.3.2 The Spatial Error Model
454(1)
13.4 Determining the Appropriate Regression Model
455(3)
13.4.1 Formulation of the Problem
455(1)
13.4.2 The Lagrange Multiplier Test
456(2)
13.5 Fitting the Spatial Lag and Spatial Error Models
458(2)
13.6 The Conditional Autoregressive Model
460(2)
13.7 Application of Simultaneous Autoregressive and Conditional Autoregressive Models to Field Data
462(4)
13.7.1 Fitting the Data
462(3)
13.7.2 Comparison of the Mixed Model and Spatial Autoregression
465(1)
13.8 Further Reading
466(1)
Exercises
466(1)
14 Bayesian Analysis of Spatially Autocorrelated Data 467(46)
14.1 Introduction
467(4)
14.2 Markov Chain Monte Carlo Methods
471(7)
14.3 Introduction to WinBUGS
478(14)
14.3.1 WinBUGS Basics
478(3)
14.3.2 WinBUGS Diagnostics
481(2)
14.3.3 Introduction to R2WinBUGS
483(7)
14.3.4 Generalized Linear Models in WinBUGS
490(2)
14.4 Hierarchical Models
492(6)
14.5 Incorporation of Spatial Effects
498(11)
14.5.1 Spatial Effects in the Linear Model
498(3)
14.5.2 Application to Data Set 3
501(4)
14.5.3 The spBayes Package
505(4)
14.6 Comparison of the Methods
509(1)
14.7 Further Reading
510(1)
Exercises
511(2)
15 Analysis of Spatiotemporal Data 513(40)
15.1 Introduction
513(1)
15.2 Spatiotemporal Data Interpolation
513(12)
15.2.1 Representing Spatiotemporal Data
513(5)
15.2.2 The Spatiotemporal Variogram
518(5)
15.2.3 Interpolating Spatiotemporal Data
523(2)
15.3 Spatiotemporal Process Models
525(4)
15.3.1 Models for Dispersing Populations
525(1)
15.3.2 A Process Model for the Yield Data
526(3)
15.4 Finite State and Time Models
529(14)
15.4.1 Determining Finite State and Time Models Using Clustering
529(9)
15.4.2 Factors Underlying Finite State and Time Models
538(5)
15.5 Bayesian Spatiotemporal Analysis
543(7)
15.5.1 Introduction to Bayesian Updating
543(3)
15.5.2 Application of Bayesian Updating to Data Set 3
546(4)
15.6 Further Reading
550(1)
Exercises
551(2)
16 Analysis of Data from Controlled Experiments 553(20)
16.1 Introduction
553(1)
16.2 Classical Analysis of Variance
554(5)
16.3 The Comparison of Methods
559(7)
16.3.1 The Comparison Statistics
559(2)
16.3.2 The Papadakis Nearest-Neighbor Method
561(1)
16.3.3 The Trend Method
562(1)
16.3.4 The "Correlated Errors" Method
563(2)
16.3.5 Published Comparisons of the Methods
565(1)
16.4 Pseudoreplicated Data and the Effective Sample Size
566(5)
16.4.1 Pseudoreplicated Comparisons
566(1)
16.4.2 Calculation of the Effective Sample Size
567(2)
16.4.3 Application to Field Data
569(2)
16.5 Further Reading
571(1)
Exercises
572(1)
17 Assembling Conclusions 573(20)
17.1 Introduction
573(1)
17.2 Data Set 1
573(5)
17.3 Data Set 2
578(5)
17.4 Data Set 3
583(3)
17.5 Data Set 4
586(4)
17.6 Conclusions
590(3)
Appendix A: Review of Mathematical Concepts 593(26)
Appendix B: The Data Sets 619(8)
Appendix C: An R Thesaurus 627(8)
References 635(22)
Index 657
Richard E. Plant received his Ph.D. in Theoretical and Applied Mechanics from Cornell University in 1975. After receiving his Ph.D., he joined the Mathematics Department at the University of California, Davis. Recognizing the opportunity to work with researchers at one of the world's leading agricultural institutions, he transferred his research effort from nerve membrane modeling to problems in agriculture.



He has received awards from the Division of Agriculture and Natural Resources of the University of California and hte American Society of Agronomy for his work in the application of academic research methods to the resolution of important agricultural problems. He was awarded a Fulbright Fellowship to carry out research in rice production in Uruguay, some of which serves as one of the case studies in his book. He is a Professor Emeritus of Biological and Agricultural Engineering and Plant Sciences at the University of California, Davis. His research interests include the application of systems analysis, geographic information systems, and statistical models to landscape level problems in crop production and resource management.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97813511898976e.html
Keywords: