APPLIED SPATIAL DATA ANALYSIS WITH R BIVAND R.S. PEBESMA E.J. GOMEZ-RUBIO V. Bankowa.pl

Applied Spatial Data Analysis with R 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.

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.

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 systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science.

The book has a website where coloured figures, 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.

Table of Contents

1 Hello World: Introducing Spatial Data 1

Pt. I Handling Spatial Data in R

2 Classes for Spatial Data in R 21

3 Visualising Spatial Data 57

4 Spatial Data Import and Export 81

5 Further Methods for Handling Spatial Data 113

6 Customising Spatial Data Classes and Methods 127

Pt. II Analysing Spatial Data

7 Spatial Point Pattern Analysis 155

8 Interpolation and Geostatistics 191

9 Areal Data and Spatial Autocorrelation 237

10 Modelling Areal Data 273

11 Disease Mapping 311

References 347

Subject Index 361

Functions Index 371

378 pages, Paperback

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