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Bibliografická citace

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BK
First published
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC, SAGE, 2015
ix, 343 stran : ilustrace (převážně barevné), mapy, plány ; 25 cm

objednat
ISBN 978-1-4462-7295-4 (brožováno)
Obsahuje bibliografie a rejstřík
001462218
PÍINTFNK // |j IJ 11 1 LI1 i  // About the Authors x // Further Resources xi // Preface xii // 1 INTRODUCTION 1 // 1.1 Objectives of This Book 1 // 1.2 Spatial Data Analysis in R 2 // 1.3 Chapters and Learning Arcs 2 // 1.4 The R Project for Statistical Computing 4 // 1.5 Obtaining and Running the R software 4 // 1.6 The R Interface 7 // 1.7 Other Resources and Accompanying Website S // References 9 // 2 DATA AND PLOTS 10 // 2.1 Introduction 10 // 2.2 The Basic Ingredients of R: Variables and Assignment 11 // 2.3 Data Types and Data Classes 15 // 2.3.1 Data types in R 15 // 2.3.2 Data’classes’in IT ’ • 19 // 2.3.3 Self-Test questions 29 // 2.4 Plots 36 // 2.4.1 Basic plot tools 36 // 2.4.2 Plot colours * 40 // 2.5 Reading, Writing/ Loading and Saving Data 44 // . 2.5.1 Text files 44 // ___ 2.5.2 R data files ... 45 // 2.5.3 Spatial data files 46 // Answers to Self-Test Questions 47 // 3 HANDLING SPATIAL DATA IN R 50 // 3.1 Overview 50 // 3.2 Introduction: GISTools 50 // 3.2.1 Installing and loading GISTools 51 // 3.2.2 Spatial data in GISTools 51 // CONTENTS // 3.2.3 Embellishing the map 55 // 3.2.4 Saving your map 56 // 3.3 Mapping Spatial Objects 59 // 3.3.1 Introduction 59 // 3.3.2 Data 59 // 3.3.3 Plotting options 59 // 3.3.4 Adding context 63 // 3.4 Mapping Spatial Data Attributes 65 // 3.4.1 Introduction 65 // 3.4.2 Attributes and data frames 66 // 3.4.3 Mapping polygons and attributes 68 // 3.4.4 Mapping points and attributes 71 // 3.4.5 Mapping lines and attributes 79
// 3.4.6 Mapping raster attributes 80 // 3.5 Simple Descriptive Statistical Analyses 83 // 3.5.1 Histograms and boxplots 83 // 3.5.2 Scatter plots and regressions 85 // 3.5.3 Mosaic plots 87 // 3.6 Self-Test Questions 89 // Answers to Self-Test Questions 93 // References 97 // 4 PROGRAMMING IN R 98 // 4.1 Overview 98 // 4.2 Introduction 99 // 4.3 Building Blocks for Programs 100 // 4.3.1 Conditional statements 100 // 4.3.2 Code blocks 103 // 4.3.3 Functions 104 // 4.3.4 Loops and repetition 106 // 4.3.5 Debugging 108 // 4.4 Writing Functions 109 // 4.4.1 Introduction 109 // 4.4.2 Data checking 111 // 4.4.3 More data checking 112 // 4.4.4 Loops revisited 114 // 4.4.5 Further activity 116 // 4.5 Writing Functions for Spatial Data 116 // 4.5.1 Drawing polygons in a list 118 // 4.5.2 Automatically choosing the bounding box 119 // 4.5.3 Shaded maps 121 // Answers to Self-Test Questions 124 // CONTENTS // 5 USING R AS A GIS 128 // 5.1 Introduction 128 // 5.2 Spatial Intersection or Clip Operations 129 // 5.3 Buffers 134 // 5.4 Merging Spatial Features 136 // 5.5 Point-in-Polygon and Area Calculations 137 // 5.5.1 Point-in-polygon 137 // 5.5.2 Area calculations 138 // 5.5.3 Point and areas analysis exercise 139 // 5.6 Creating Distance Attributes 143 // 5.6.1 Distance analysis/accessibility exercise 145 // 5.7 Combining Spatial Datasets and Their Attributes 149 // 5.8 Converting between Raster and Vector 153 // 5.8.1 Raster to vector 154 // 5.8.2 Converting to sp classes 157 // 5.8.3 Vector
to raster 159 // 5.9 Introduction to Raster Analysis 160 // 5.9.1 Raster data preparation 161 // 5.9.2 Raster reclassification 162 // 5.9.3 Other raster calculations 165 // Answers to Self-Test Questions 167 // References 172 // 6 POINT PATTERN ANALYSIS USING R 173 // 6.1 Introduction 173 // 6.2 What is Special about Spatial? 173 // 6.2.1 Point patterns 174 // 6.3 Techniques for Point Patterns Using R 175 // 6.3.1 Kernel density estimates 175 // 6.3.2 Kernel density estimation using R 176 // Self-Test Question 1 178 // 6.4 Further Uses of Kernel Density Estimation 179 // 6.4.1 Hexagonal binning using R 180 // 6.5 Second-Order Analysis of Point Patterns 184 // 6.5.1 Using the K-function in R 188 // 6.5.2 The L-function 192 // 6.5.3 The G-function 193 // 6.6 Looking at Marked Point Patterns 195 // 6.6.1 Cross-L-function analysis in R 196 // 6.7 Interpolation of Point Patterns with Continuous Attributes 199 // 6.7.1 Nearest neighbour interpolation 200 // 6.7.2 Inverse distance weighting 204 // CONTENTS // 6.8 The Kriging Approach 208 // 6.8.1 A brief introduction to kriging 209 // 6.8.2 Random functions 209 // 6.8.3 Estimating the semivariogram 211 // 6.9 Concluding Remarks 215 // Answers to Self-Test Question 215 // References 217 // 7 SPATIAL ATTRIBUTE ANALYSIS WITH R 218 // 7.1 Introduction 218 // 7.2 The Pennsylvania Lung Cancer Data 219 // 7.3 A Visual Exploration of Autocorrelation 220 // 7.3.1 Neighbours and lagged mean plots 223 // 7.4 Moran’s I: An Index of Autocorrelation 229
// 7.4.1 Moran’s I in R 231 // 7.4.2 A simulation-based approach 234 // 7.5 Spatial Autoregression 235 // 7.6 Calibrating Spatial Regression Models in R 236 // 7.6.1 Models with predictors: A bivariate example 238 // 7.6.2 Further issues 242 // 7.6.3 Troubleshooting spatial regression 243 // Answer to Self-Test Question 250 // References 251 // 8 LOCALISED SPATIAL ANALYSIS 253 // 8.1 Introduction 253 // 8.2 Setting Up The Data Used in This Chapter 253 // 8.3 Local Indicators of Spatial Association 255 // 8.4 Further Issues with the Above Analysis 259 // 8.4.1 Multiple hypothesis testing 260 // 8.4.2 Issues with the Bonferroni approach 262 // 8.4.3 The false discovery rate 265 // 8.4.4 Which method to use? 266 // 8.5 The Normality Assumption and Local Moran’s I 267 // 8.6 Getis and Ord’s G-statistic 272 // 8.7 Geographically Weighted Approaches 278 // 8.7.1 Review of summary statistics 279 // 8.7.2 Geographically weighted summary statistics in R 280 // 8.7.3 Exploring non-stationarity of relationships 288 // 8.7.4 Robust, quantile-based local summary statistics 289 // 8.7.5 Geographically weighted regression 291 // Answer to Self-Test Question 295 // References 296 // CONTENTS // 9 R AND INTERNET DATA 298 // 9.1 Introduction 298 // 9.2 Direct Access to Data 299 // 9.3 Using RCurl 302 // 9.4 Working with APIs 306 // 9.4.1 Creating a statistical ’mashup’ 312 // 9.5 Using Specific Packages 315 // 9.6 Web Scraping 323 // 9.6.1 Scraping train times 326 // Answer to Self-Test Question 328
// References 328 // 10 EPILOGUE 329 // Index // 334

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