TY  - GEN
AU  - Plant,Richard E.
TI  - Spatial data analysis in ecology and agriculture using R / 
SN  - 9780815392750 (hbk.)
U1  - 338.1072​7 PLA 23
PY  - 2019///
CY  - Boca Raton, FL : 
PB  - CRC Press, 
KW  - Agriculture
KW  - Statistical methods
KW  - Spatial analysis (Statistics)
KW  - R (Computer program language)
N1  - Includes bibliographical references and index; 0 Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface to the First Edition; Preface to the Second Edition; Author; 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 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
505 8 1.3.4 Data Set 4: Factors Underlying Yield in Two Fields1.3.5 Comparing the Data Sets; 1.4 Further Reading; 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
505 8 2.6.3 Trellis Graphics in R, Attribute Data2.6.4 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: 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
505 8 3.6 Application to Field Data3.6.1 Setting Up the Data; 3.6.2 Checking Sequence Validity; 3.6.3 Determining Spatial Autocorrelation; 3.7 Further Reading; Exercises; 4: Measures of Spatial Autocorrelation; 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
505 8 4.5.4 Geographically Weighted Regression4.6 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: Sampling and Data Collection; 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
ER  -