An introduction to r for spatial analysis and mapping
Chris Brunsdon
An introduction to r for spatial analysis and mapping by Chris Brunsdon - New Delhi Sage 2015 - xii, 343 pages; Illustrations; 23 cm.
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
"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."
9781446272947
300.15195 BRU
An introduction to r for spatial analysis and mapping by Chris Brunsdon - New Delhi Sage 2015 - xii, 343 pages; Illustrations; 23 cm.
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
"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."
9781446272947
300.15195 BRU