An introduction to R for spatial analysis & mapping / Chris Brunsdon and Lex Comber

Incluye bibliografía e índice: páginas 334-343

About the Authors.. Further Resources.. Preface.. 1 Introduction.. 1.1 Objectives of this book.. 1.2 Spatial Data Analysis in R.. 1.3 Chapters and Learning Arcs.. 1.4 The R Project for Statistical Computing.. 1.5 Obtaining and Running the R software.. 1.6 The R interface.. 1.7 Other resources and accompanying website.. 2 Data and Plots.. 2.1 Introduction.. 2.2 The basic ingredients of R: variables and assignment.. 2.3 Data types and Data classes.. 2.4 Plots.. 2.5 Reading, writing, loading and saving data.. 3 Handling Spatial Data in R.. 3.1 Overview.. 3.2 Introduction: GISTools.. 3.3 Mapping spatial objects.. 3.4 Mapping spatial data attributes.. 3.5 Simple descriptive statistical analyses.. 3.6 Self-Test Questions.. 4 Programming in R.. 4.1 Overview.. 4.2 Introduction.. 4.3 Building blocks for Programs.. 4.4 Writing Functions.. 4.5. Writing Functions for Spatial Data.. 5 Using R as a GIS.. 5.1 Introduction.. 5.2 Spatial Intersection or Clip Operations.. 5.3 Buffers.. 5.4 Merging spatial features.. 5.5 Point-in-polygon and Area calculations.. 5.6 Creating distance attributes.. 5.7 Combining spatial datasets and their attributes.. 5.8 Converting between Raster and Vector.. 5.9 Introduction to Raster Analysis.. 6: Point Pattern Analysis using R.. 6.1 Introduction.. 6.2 What is Special about Spatial?.. 6.3 Techniques for Point Patterns Using R.. 6.4 Further Uses of Kernal Density Estimation.. 6.5 Second Order Analysis of Point Patterns.. 6.6 Looking at Marked Point Patterns 6.7 Interpolation of Point Patterns With Continuous Attributes.. 6.8 The Kringing approach.. 6.9 Concluding Remarks.. 7 Spatial Attribute Analysis With R.. 7.1 Introduction.. 7.2The Pennsylvania Lung Cancer Data.. 7.3 A Visual Exploration of Autocorrelation.. 7.4 Moran's I: An Index of Autocorrelation.. 7.5 Spatial Autoregression.. 7.6 Calibrating Spatial Regression Models in R

8 Localised Spatial Analysis.. 8.1 Introduction.. 8.2 Setting Up The Data Used in This Chapter.. 8.3 Local Indicators of Spatial Association.. 8.4 Further Issues with the Above Analysis.. 8.5 The Normality Assumption and Local Moran's I.. 8.6 Getis and Ord's G-statistic.. 8.7 Geographically Weighted Approaches.. 9: R and Internet Data.. 9.1 Introduction.. 9.2 Direct Access to Data.. 9.3 Using RCurl.. 9.4 Working with APIs.. 9.5 Using Specific Packages.. 9.6 Web Scraping.. 10 Epilogue.. Index

In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses. R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and 'non-geography' students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from 'zero to hero' in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring R. Scripts and coding to exemplify specific functionality. Advice for developing greater understanding - through functions such as locator, View, and alternative coding to achieve the same ends. Self-contained exercises for students to work through. Embedded code within the descriptive text. This is a definitive 'how to' that takes students - of any discipline -from coding to actual applications and uses of R. eng