Applied Spatial Data Analysis with R

By Roger S. Bivand, Edzer Pebesma and Virgilio Gómez-Rubio

Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition.

 

This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, publicadministration and political science.

 

The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org.

 

The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

• Language: English
• Publisher: Springer
• Release date: Jun 21, 2013
• ISBN: 9781461476184

Book preview

Part 1

Handling Spatial Data in R

Roger S. Bivand, Edzer Pebesma and Virgilio Gómez-RubioUse R!Applied Spatial Data Analysis with R2nd ed. 201310.1007/978-1-4614-7618-4© Springer Science+Business Media New York 2013

Handling Spatial Data

The key intuition underlying the development of the classes and methods in the sp package, and its closer dependent packages, is that users approaching R with experience of GIS will want to see ‘layers’, ‘coverages’, ‘rasters’, or ‘geometries’. Seen from this point of view, sp classes should be reasonably familiar, appearing to be well-known data models. On the other hand, for statistician users of R , ‘everything’ is a data.frame, a rectangular table with rows of observations on columns of variables. To permit the two disparate groups of users to play together happily, classes have grown that look like GIS data models to GIS and other spatial data people, and look and behave like data frames from the point of view of applied statisticians and other data analysts.

This part of the book describes the classes and methods of the sp package, and in doing so also provides a practical guide to the internal structure of many GIS data models, as R permits the user to get as close as desired to the data. However, users will not often need to know more than that of Chap. 4 to read in their data and start work. Visualisation is covered in Chap. 3, and so a statistician receiving a well-organised set of data from a collaborator may even be able to start making maps in two lines of code, one to read the data and one to plot the variable of interest using lattice graphics. Note that complete code examples, data sets, and other support material may be found on the book website.

If life was always so convenient, this part of the book could be much shorter than it is. But combining spatial data from different sources often means that much more insight is needed into the data models involved. The data models themselves are described in Chap. 2, and methods for handling and combining them are covered in Chap. 5, with substantial discussion of functions and operations provided in the rgeos package. Keeping track of which observation belongs to which geometry is also discussed here, seen from the GIS side as feature identifiers, and row names from the data frame side. In addition to data import and export, Chap. 4 also describes the use and transformation of coordinate reference systems for sp classes, and integration of the open source GRASS GIS and R . Finally, Chap. 6 explains how the methods and classes introduced in Chap. 2 can be extended to spatio-temporal data.

Roger S. Bivand, Edzer Pebesma and Virgilio Gómez-RubioUse R!Applied Spatial Data Analysis with R2nd ed. 201310.1007/978-1-4614-7618-4_2© Springer Science+Business Media New York 2013

2. Classes for Spatial Data in R

Roger S. Bivand¹ , Edzer Pebesma² and Virgilio Gómez-Rubio³

(1)

Norwegian School of Economics, Bergen, Norway

(2)

Westfälische Wilhelms-Universität, Münster, Germany

(3)

Department of Mathematics, Universidad de Castilla-La Mancha, Albacete, Spain

Abstract

Many disciplines have influenced the representation of spatial data, both in analogue and digital forms. Surveyors, navigators, and military and civil engineers refined the fundamental concepts of mathematical geography, established often centuries ago by some of the founders of science, for example by al-Khwārizmı̄. Digital representations came into being for practical reasons in computational geometry, in computer graphics and hardware-supported gaming, and in computer-assisted design and virtual reality. The use of spatial data as a business vehicle has been spurred early in the present century by consumer wired and mobile broadband penetration and distributed server farms, with examples being Google Earth™, Google Maps™, and others. There are often interactions between the graphics hardware required and the services offered, in particular for the fast rendering of scene views.

2.1 Introduction

Many disciplines have influenced the representation of spatial data, both in analogue and digital forms. Surveyors, navigators, and military and civil engineers refined the fundamental concepts of mathematical geography, established often centuries ago by some of the founders of science, for example by al-Khwārizmı̄. Digital representations came into being for practical reasons in computational geometry, in computer graphics and hardware-supported gaming, and in computer-assisted design and virtual reality. The use of spatial data as a business vehicle has been spurred early in the present century by consumer wired and mobile broadband penetration and distributed server farms, with examples being Google Earth™, Google Maps™, and others. There are often interactions between the graphics hardware required and the services offered, in particular for the fast rendering of scene