An Introduction to R for Spatial Analysis & Mapping - Rilegato

Brunsdon, Chris; Comber, Lex

Rilegato

ISBN 10: 144627294X ISBN 13: 9781446272947

Casa editrice: Sage Pubns Ltd, 2015

Riassunto

"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."
- Richard Harris, Professor of Quantitative Social Science, University of Bristol

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 it
• 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.

Su questo libro

L'autore:

Chris Brunsdon is Professor of Geocomputation at the National University of Ireland, Maynooth. He studied Mathematics at the University of Durham and Medical Statistics at the University of Newcastle upon Tyne,  and has worked in a number of universities, holding the Chair in Human Geography at Liverpool University before taking up his current position.  His research interests are in health, crime and environmental data analysis,  and in the development of spatial analytical tools,  including Geographically Weighted Regression approach.  He also has interests in the software tools used to develop such approaches,  including R.

Contenuti:

Part 1: Introduction
Objectives of this book
Spatial Data Analysis in R
Chapters and Learning Arcs
The R Project for Statistical Computing
Obtaining and Running the R software
The R interface
Other resources and accompanying website
Part 2: Data and Plots
The basic ingredients of R: variables and assignment
Data types and Data classes
Plots
Reading, writing, loading and saving data
Part 3: Handling Spatial Data in R
Introduction: GISTools
Mapping spatial objects
Mapping spatial data attributes
Simple descriptive statistical analyses
Part 4: Programming in R
Building blocks for Programs
Writing Functions
Writing Functions for Spatial Data
Part 5: Using R as a GIS
Spatial Intersection or Clip Operations
Buffers
Merging spatial features
Point-in-polygon and Area calculations
Creating distance attributes
Combining spatial datasets and their attributes
Converting between Raster and Vector
Introduction to Raster Analysis
Part 6: Point Pattern Analysis using R
What is Special about Spatial?
Techniques for Point Patterns Using R
Further Uses of Kernal Density Estimation
Second Order Analysis of Point Patterns
Looking at Marked Point Patterns
Interpolation of Point Patterns With Continuous Attributes
The Kringing approach
Part 7: Spatial Attribute Analysis With R
The Pennsylvania Lung Cancer Data
A Visual Exploration of Autocorrelation
Moran's I: An Index of Autocorrelation
Spatial Autoregression
Calibrating Spatial Regression Models in R
Part 8: Localised Spatial Analysis
Setting Up The Data Used in This Chapter
Local Indicators of Spatial Association
Self Test Question
Further Issues with the Above Analysis
The Normality Assumption and Local Moran's-I
Getis and Ord's G-statistic
Geographically Weighted Approaches
Part 9: R and Internet Data
Direct Access to Data
Using RCurl
Working with APIs
Using Specific Packages
Web Scraping
Epilogue

Su questa edizione

• Editore: Sage Pubns Ltd
• Data di pubblicazione: 2015
• ISBN 10: 144627294X
• ISBN 13: 9781446272947
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 343