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Chapter 1 Working with Spatial Data
1.1 Introduction
1.2 Analysis of Spatial data
  1.2.1 Types of spatial data
  1.2.2 The components of spatial data
  1.2.3 Topics covered in the text
1.3 The data sets analyzed in this book
  1.3.1 Data Set 1: yellow-billed cuckoo habitat
  1.3.2. Data Set 2: Environmental characteristics of oak woodlands
  1.3.3 Data Set 3: Uruguayan rice farmers
  1.3.4 Data Set 4: Factors underlying yield in two fields
  1.3.5 Comparing the data sets
1.4 Further reading

Chapter 2 The R Programming Environment
2.1 Introduction
  2.1.1 Introduction to R
  2.1.2 Setting yourself up to use this book
2.2 R basics
2.3 Programming concepts
  2.3.1 Looping and branching
  2.3.2 Functional programming
2.4 Handling Data in R
  2.4.1 Data structures in R
  2.4.2 Basic data input and output
  2.4.3 Spatial data structures
2.5 Writing functions in R
2.6 Graphics in R
  2.6.1 Traditional graphics in R; attribute data
  2.6.2 Traditional graphics in R; spatial data
  2.6.3 Trellis graphics in R, attribute data
  2.6.4 Trellis graphics in R, spatial data
  2.6.5 Using color in R
2.7 Other software packages
2.8 Further reading
2.9 Exercises
Chapter 3 Statistical Properties of Spatially Autocorrelated Data
3.1 Introduction
3.2 Components of a spatial random process
  3.2.1 Spatial trends in data
  3.2.2 Stationarity
3.3 Monte Carlo simulation
3.4 A Review of hypothesis and significance testing
3.5 Modeling spatial autocorrelation
  3.5.1 Monte Carlo simulation of time series
  3.5.2 Modeling spatial contiguity
  3.5.3 Modeling spatial association in R
3.6 Application to field data
3.7 Further reading
3.8 Exercises
Chapter 4 Measures of Spatial Autocorrelation
4.1 Introduction
4.2 Preliminary considerations
  4.2.1 Measurement scale
  4.2.2 Randomization assumptions
  4.2.3 Testing the null hypothesis
4.3 Join - count statistics
4.4 Moran's I and Geary's c
4.5 Measures of autocorrelation structure
  4.5.1 The Moran correlogram
  4.5.2 The Moran Scatterplot
  4.5.3 Local measures of autocorrelation
  4.5.4 Geographically weighted regression
4.6 Measuring autocorrelation of spatially continuous data
  4.6.1 The variogram
  4.6.2 The covariogram and the correlogram
4.7 Further reading
4.8 Exercises
Chapter 5 Sampling and Data Collection
5.1 Introduction
5.2 Preliminary considerations
  5.2.1 The artificial population
  5.2.2 Accuracy, bias, and precision
  5.2.3 Comparison procedures
5.3 Developing the sampling patterns
  5.3.1 Random sampling
  5.3.2 Geographically stratified sampling
  5.3.3 Sampling on a regular grid
  5.3.4 Stratification based on a covariate
  5.3.5 Cluster sampling
5.4 Methods for variogram estimation
5.5 Estimating the sample size
5.6 Sampling for thematic mapping
5.7 Design-based and model-based sampling
5.8 Further reading
5. 9 Exercises
Chapter 6 Preparing Spatial Data for Analysis
6.1. Introduction
6.2 Quality of attribute data
  6.2.1 Dealing with outliers and contaminants
  6.2.2 Quality of ecological survey data
  6.2.3 Quality of automatically recorded data
6.3 Spatial interpolation procedures
  6.3.1 Inverse weighted distance interpolation
  6.3.2 Kriging interpolation
  6.3.3 Cokriging interpolation
6.4 Spatial rectification and alignment of data
  6.4.1 Definitions of scale related processes
  6.4.2 Change of coverage
  6.4.3 Change of support
6.5 Further reading
6.6 Exercises
Chapter 7 Preliminary Exploration of Spatial Data
7.1 Introduction
7.2 Data Set 1
7.3 Data Set 2
7.4 Data Set 3
7.5 Data Set 4
7.6 Further reading
7.7 Exercises
Chapter 8 Multivariate Methods for Spatial Data Exploration
8.1 Introduction
8.2 Principal components analysis
  8.2.1 Introduction to Principal Components Analysis
  8.2.2 Principal components analysis of Data Set 2
  8.2.3 Application to Data Set 4, Field 1
8.3 Classification and regression trees (a.k.a. recursive partitioning)
  8.3.1 Introduction to the method
  8.3.2 The mathematics of recursive partitioning
  8.3.3 Exploratory analysis of Data Set 2 with regression trees
  8.3.4 Exploratory analysis of Data Set 3 with recursive partitioning
  8.3.5 Exploratory analysis of Field 4.1 with recursive partitioning
8.4 Random forest
  8.4.1 Introduction to random forest
  8.4.2 Application to Data Set 2
8.5 Further reading
8.6 Exercises
Chapter 9 Spatial Data Exploration via Multiple Regression
9.1 Introduction
9.2 Multiple Linear Regression
  9.2.1 The many perils of model selection
  9.2.2 Multicollinearity, added variable plots and partial residual plots
  9.2.3 A cautious approach model selection as an exploratory tool
9.3 Building a multiple regression model for Field 4.1
9.4 Generalized linear models
  9.4.1 Introduction to generalized linear models
  9.4.2 Multiple logistic regression model for Data Set 2
  9.4.3 Logistic regression model of count data for Data Set 1
  9.4.4 Analysis of the counts of Data Set 1: zero-inflated Poisson data
9.5 Further reading
9.6 Exercises
Chapter 10 Variance Estimation, the Effective Sample Size, and the Bootstrap
10.1. Introduction
10.2 Bootstrap estimation of the standard error
10.3 Bootstrapping time series data
  10.3.1 The problem with correlated data
  10.3.2 The block bootstrap
  10.3.3 The parametric bootstrap
10.4 Bootstrapping spatial data
  10.4.1 The spatial block bootstrap
  10.4.2 The parametric spatial bootstrap
  10.4.3 Power of the tests
10.5 Application to the EM38 data
10.6 Further reading
10.7 Exercises
Chapter 11 Measures of Bivariate Association between Two Spatial Variables
11.1 Introduction
11.2 Estimating and testing the correlation coefficient
  11.2.1 The correlation coefficient
  11.2.2 The Clifford et al. (1989) correction
  11.2.3 The bootstrap variance estimate
  11.2.4 Application to the example problem
11.3 Contingency tables
  11.3.1 Large sample size contingency tables
  11.3.2 Small sample size contingency tables
11.4 The Mantel and partial Mantel Statistics
  11.4.1 The Mantel statistic
  11.4.2 The partial Mantel test
11.5 The modifiable areal unit problem and the ecological fallacy
  11.5.1 The modifiable areal unit problem
  11.5.2 The ecological fallacy
11.6 Further reading
11.7 Exercises
Chapter 12 The Mixed Model
12.1 Introduction
12.2 Basic properties of the mixed model
12.3 Application to Data Set 3
12.4 Incorporating spatial autocorrelation
12.5 Generalized least squares
12.6 Spatial logistic regression
  12.6.1 Upscaling Data Set 2 in the Coast Range
  12.6.2 The incorporation of spatial autocorrelation
12.7 Further Reading
12.8 Exercises
Chapter 13 Regression Models for Spatially Autocorrelated Data
13.1 Introduction
13.2 Detecting spatial autocorrelation in a regression model
13.3 Models for spatial processes
  13.3.1 The spatial lag model
  13.3.2 The spatial error model
13.4 Determining the appropriate regression model
  13.4.1 Formulation of the problem
  13.4.2 The Lagrange multiplier test
13.5 Fitting the spatial lag and spatial error models
13.6 The conditional autoregressive model
13.7 Application of SAR and CAR models to Field data
  13.7.1 Fitting the data
  13.7.2 Comparison of the mixed model and spatial autoregression
13.8 The autologistic model for binary data
13.9 Further reading
13.10 Exercises
Chapter 14 Bayesian Analysis of Spatially Autocorrelated Data
14.1 Introduction
14.2 Markov chain Monte Carlo methods
14.3 Introduction to WinBUGS
  14.3.1 WinBUGS basics
  14.3.2 WinBUGS diagnostics
  14.3.3 Introduction to R2WinBUGS
  14.3.4 Generalized linear models in WinBUGS
14.4 Hierarchical models
14.5 Incorporation of spatial effects
  14.5.1 Spatial effects in the linear model
  14.5.2 Application to Data Set 3
14.6 Further reading
14.7 Exercises
Chapter 15 Analysis of Spatiotemporal Data
15.1 Introduction
15.2 Spatiotemporal cluster analysis
15.3 Factors underlying spatiotemporal yield clusters
15.4 Bayesian spatiotemporal analysis
  15.4.1 Introduction to Bayesian updating
  15.4.2 Application of Bayesian updating to Data Set 3
15.5 Other approaches to spatiotemporal modeling
  15.5.1 Models for dispersing populations
  15.5.2 State and transition models
15.6 Further reading
15.7 Exercises
Chapter 16 Analysis of Data from Controlled Experiments
16.1 Introduction
16.2 Classical analysis of variance
16.3 The comparison of methods
  16.3.1 The comparison statistics
  16.3.2 The Papadakis nearest neighbor method
  16.3.3 The trend method
  16.3.4 The "correlated errors" method
  16.3.5 Published comparisons of the methods
16.4 Pseudoreplicated data and the effective sample size.
  16.4.1 Pseudoreplicated comparisons
  16.4.2 Calculation of the effective sample size
  16.4.3 Application to field data
16.5 Further reading
16.6 Exercises
Chapter 17 Assembling Conclusions
17.1 Introduction
17.2 Data Set 1
17.3 Data Set 2
17.4 Data Set 3
17.5 Data Set 4
17.6 Conclusions
Appendix A Review of Mathematical Concepts
A.1 Matrix theory and linear algebra
  A.1.1 Matrix algebra
  A.1.2 Random vectors and matrices
  A.1.3 Eigenvectors, eigenvalues, and projections
A.2 Linear regression
  A.2.1 Basic regression theory
  A.2.2 The matrix representation of linear regression
  A.2.3 Regression diagnostics
  A.2.4 Regression and causality
A.3 Nested models and the general linear test
A.4 The method of Lagrange multipliers
A.5 The maximum likelihood method
  A.5.1 The likelihood function
  A.5.2 The likelihood ratio test
  A.5.3. Application of maximum likelihood to linear regression
  A.5.4 Maximum likelihood and restricted maximum likelihood.
A.6 Change of variables of a probability density
Appendix B: The Data Sets
B.1 Data Set 1
  B.1.1 General description
  B.1.2 Land cover mapping from 1997 aerial photography
  B.1.4 Habitat patches
  B.1.5 The creation of the full set of explanatory variables
  B.1.6 Yellow-billed cuckoo observation points
B. 2 Data Set 2
B.3 Data Set 3
B.4 Data Set 4
Appendix C An R Thesaurus