Actions for Spatial data analysis in ecology and agriculture using R
Spatial data analysis in ecology and agriculture using R / Richard E. Plant
- Author
- Plant, Richard E.
- Published
- Boca Raton, Florida : CRC Press, [2019]
- Copyright Date
- ©2019
- Edition
- Second edition.
- Physical Description
- xvii, 666 pages : illustrations ; 26 cm
- Contents
- Machine generated contents note: 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.Spatial Data Models -- 1.2.4.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 -- 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.Continuing on from Here with R -- 2.8.Further Reading -- Exercises -- 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 -- 3.6.1.Setting Up the Data -- 3.6.2.Checking Sequence Validity -- 3.6.3.Determining Spatial Autocorrelation -- 3.7.Further Reading -- Exercises -- 4.1.Introduction -- 4.2.Preliminary Considerations -- 4.2.1.Measurement Scale -- 4.2.2.Resampling and 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 -- Exercises -- 5.1.Introduction -- 5.2.Preliminary Considerations -- 5.2.1.The Artificial Population -- 5.2.2.Accuracy, Bias, Precision, and Variance -- 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 -- Exercises -- 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 -- Exercises -- 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 -- Exercises -- 8.1.Introduction -- 8.2.Multiple Linear Regression -- 8.2.1.The Many Perils of Model Selection -- 8.2.2.Multicollinearity, Added Variable Plots, and Partial Residual Plots -- 8.2.3.A Cautious Approach Model Selection as an Exploratory Tool -- 8.3.Building a Multiple Regression Model for Field 4.1 -- 8.4.Generalized Linear Models -- 8.4.1.Introduction to Generalized Linear Models -- 8.4.2.Multiple Logistic Regression Model for Data Set 2 -- 8.4.3.Logistic Regression Model of Count Data for Data Set 1 -- 8.4.4.Analysis of the Counts of Data Set 1: Zero-Inflated Poisson Data -- 8.5.Further Reading -- Exercises -- 9.1.Introduction -- 9.2.The Generalized Additive Model -- 9.3.Classification and Regression Trees (a.k.a. Recursive Partitioning) -- 9.3.1.Introduction to the Method -- 9.3.2.The Mathematics of Recursive Partitioning -- 9.3.3.Exploratory Analysis of Data Set 2 with Regression Trees -- 9.3.4.Exploratory Analysis of Data Set 3 with Recursive Partitioning -- 9.3.5.Exploratory Analysis of Field 4.1 with Recursive Partitioning -- 9.4.Random Forest -- 9.4.1.Introduction to Random Forest -- 9.4.2.Application to Data Set 2 -- 9.5.Further Reading -- Exercises -- 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 -- Exercises -- 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 -- Exercises -- 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 -- Exercises -- 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 Simultaneous Autoregressive and Conditional Autoregressive Models to Field Data -- 13.7.1.Fitting the Data -- 13.7.2.Comparison of the Mixed Model and Spatial Autoregression -- 13.8.Further Reading -- Exercises -- 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 -- 14.5.3.The spBayes Package -- 14.6.Comparison of the Methods -- 14.7.Further Reading -- Exercises -- 15.1.Introduction -- 15.2.Spatiotemporal Data Interpolation -- 15.2.1.Representing Spatiotemporal Data -- 15.2.2.The Spatiotemporal Variogram -- 15.2.3.Interpolating Spatiotemporal Data -- 15.3.Spatiotemporal Process Models -- 15.3.1.Models for Dispersing Populations -- 15.3.2.A Process Model for the Yield Data -- 15.4.Finite State and Time Models -- 15.4.1.Determining Finite State and Time Models Using Clustering -- 15.4.2.Factors Underlying Finite State and Time Models -- 15.5.Bayesian Spatiotemporal Analysis -- 15.5.1.Introduction to Bayesian Updating -- 15.5.2.Application of Bayesian Updating to Data Set 3 -- 15.6.Further Reading -- Exercises -- 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 -- Exercises -- 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.
- Subject(s)
- ISBN
- 9780815392750 (hardback : alk. paper)
0815392753 - Bibliography Note
- Includes bibliographical references (pages 635-656) and index.
- Endowment Note
- George Blight Agricultural Library Fund Endowment
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