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Bibliografická citace

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0 (hodnocen0 x )
BK
Second edition
Los Angeles : SAGE, 2019
ix, 325 stran : ilustrace, mapy ; 25 cm

objednat
ISBN 978-1-5264-2850-9 (brožováno)
GIS & spatial analytics
Obsahuje bibliografie a rejstřík
001480928
About the authors x // 1 INTRODUCTION 1 // 1.1 Introduction to the Second Edition 1 // 1.2 Objectives of This Book 2 // 1.3 Spatial Data Analysis in R 3 // 1.4 Chapters and Learning Arcs 4 // 1.5 Specific Changes to the Second Edition 5 // 1.6 The R Project for Statistical Computing 7 // 1.7 Obtaining and Running the R Software 7 // 1.8 The R Interface 10 // 1.9 Other Resources and Accompanying Website 11 // References 12 // 2 DATA AND PLOTS 13 // 2.1 Introduction 13 // 2.2 The Basic Ingredients of R: Variables and Assignment 14 // 2.3 Data Types and Data Classes 16 // 2.4 Plots 34 // 2.5 Another Plot Option: ggplot 43 // 2.6 Reading, Writing, Loading and Saving Data 50 // 2.7 Answers to Self-Test Questions 52 // Reference 54 // 3 BASICS OF HANDLING SPATIAL DATA IN R 55 // 3.1 Overview 55 // 3.2 Introduction to sp and sf: The sf Revolution 57 // 3.3 Reading and Writing Spatial Data 63 // 3.4 Mapping: An Introduction to tmap 66 // 3.5 Mapping Spatial Data Attributes 81 // 3.6 Simple Descriptive Statistical Analyses 98 // 3.7 Self-Test Questions 107 // 3.8 Answers to Self-Test Questions 110 // References 117 // 4 SCRIPTING AND WRITING FUNCTIONS IN R 118 // 4.1 Overview 118 // 4.2 Introduction 119 // 4.3 Building Blocks for Programs 120 // 4.4 Writing Functions 127 // 4.5 Spatial Data Structures 135 // 4.6 apply Functions 137 // 4.7 Manipulating Data with dplyr 140 // 4.8 Answers to Self-Test Questions 143 // 5 USING R AS A GIS 148 // 5.1 Introduction 148 // 5.2 Spatial Intersection and Clip Operations 150 // 5.3 Buffers 153 // 5.4 Merging Spatial Features 155 // 5.5 Point-in-Polygon and Area Calculations 157 // 5.6 Creating Distance Attributes 163 // 5.7 Combining Spatial Datasets and Their Attributes 169 // 5.8 Converting between Raster and Vector 175 // 5.9 Introduction to Raster Analysis 182 // 5.10 Answers to Self-Test Questions 190 // References 192 //
6 POINT PATTERN ANALYSIS USING R 194 // 6.1 Introduction 194 // 6.2 What is Special about Spatial? 194 // 6.3 Techniques for Point Patterns Using R 196 // 6.4 Further Uses of Kernel Density Estimation 202 // 6.5 Second-Order Analysis of Point Patterns 207 // 6.6 Looking at Marked Point Patterns 218 // 6.7 Interpolation of Point Patterns with Continuous Attributes 222 // 6.8 The Kriging Approach 235 // 6.9 Concluding Remarks 242 // 6.10 Answers to Self-Test Questions 243 // References 244 // 7 SPATIAL ATTRIBUTE ANALYSIS WITH R 245 // 7.1 Introduction 245 // 7.2 The Pennsylvania Lung Cancer Data 246 // 7.3 A Visual Exploration of Autocorrelation 248 // 7.4 Moran’s I: An Index of Autocorrelation 257 // 7.5 Spatial Autoregression 262 // 7.6 Calibrating Spatial Regression Models in R 263 // 7.7 Answer to Self-Test Question 277 // References 279 // LOCALISED SPATIAL ANALYSIS 281 // 8.1 Introduction 281 // 8.2 Setting Up the Data Used in This Chapter 282 // 8.3 Local Indicators of Spatial Association 283 // 8.4 Further Issues with the Above Analysis 286 // References 289 // R AND INTERNET DATA 290 // 9.1 Introduction 290 // 9.2 Direct Access to Data 291 // 9.3 Using RCurl 295 // 9.4 Working with APIs 297 // 9.5 Creating a Statistical ’Mashup’ 303 // 9.6 Using Specific Packages 306 // 9.7 Web Scraping 310 // References 315 // EPILOGUE 316 // 10.1 The Future of R as a Tool for Geocomputation 316 // 10.2 Extensions of R as a Language 316 // 10.3 Improvements ’Under the Bonnet’ 318 // 10.4 Coexistence with Other Software 319 // 10.5 Finally... 320 // References 321 // Index 322

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