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Applied Spatial Data Analysis with R
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, public administration 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.
- GenresProgrammingGeography
414 pages, ebook
First published January 1, 2008
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Displaying 1 - 7 of 7 reviews
February 3, 2018
Re-read in order to refresh memory. Methods will be used when analysing the spatial component of the location of museums in Florence.
July 13, 2009
Applied Spatial Data Analysis with R (ASDAR) is written by the same people who wrote and maintain the spatial sp class in R. The book is not a statistician's text on mathematical geo-statistics, rather is focuses on taking geospatial (e.g. GIS) data and applying analysis within R. Not being a statistician, I used the book to learn how to manipulate geospatial data for my own analytical purposes. The mark of a good technical book, not only did I learn about how to work with the standard geospatial data types, I was able to implement analyses using the material in the book.
The book has three parts. First is an introduction to spatial data. Much of it is orienting the reader to the vocabulary of geospatial data such as point, line, polygon, grid, coordinate system, projection. It also motivates why using R for spatial analysis. (The other options would be within a GIS such as GRASS or ARCInfo, custom functions using C++ or Java, or Python, which has been incorporated into many GIS environments). In particular, it looks at the many packages and analysis built up that uses the sp package and data structure, allowing many developed analytical methods to be used together to build a complex analysis. (this is similar to my purpose, taking advantage of the fact that R provides a standard entry point to several computational toolkits that I use.)
The second part discusses accessing and using geospatial data in R, which fulfilled my purpose. It is detailed documentation on the various spatial classes and the methods that are applicable. There are descriptions and examples of how to visually display geospatial data. The chapter on data import and export covers GDAL/OGR, coordinate reference systems, projections and transformations, and what you would need to work with formats such as shapefiles, PostGIS, KML, image files such as tiff files and Google Earth overlays (PNG), or directly with GRASS, TerraLib, or Python interfaces with ArcGIS, RPyGeo.
The last part is on implementing geostatistical methods such as for pattern analysis, geostatistics, areal analysis (geographic aggregation), or epidemiology. I cannot comment too much on this as this is not an area that I have expertise, but the methods look both adequate as well as practical to use.
While the intended audience of this book are statisticians working with geospatial data, I would also recommend this to those who do data analysis or modeling with geospatial data. Most of the analytical texts I've seen discuss algorithms. This text gets into the practicalities of working with real data formats and real data issues that are the inevitable first step in a project. And it does so at a more analytical level then the point and click interface instructions that are enabled by standard GIS systems alone.
The book has three parts. First is an introduction to spatial data. Much of it is orienting the reader to the vocabulary of geospatial data such as point, line, polygon, grid, coordinate system, projection. It also motivates why using R for spatial analysis. (The other options would be within a GIS such as GRASS or ARCInfo, custom functions using C++ or Java, or Python, which has been incorporated into many GIS environments). In particular, it looks at the many packages and analysis built up that uses the sp package and data structure, allowing many developed analytical methods to be used together to build a complex analysis. (this is similar to my purpose, taking advantage of the fact that R provides a standard entry point to several computational toolkits that I use.)
The second part discusses accessing and using geospatial data in R, which fulfilled my purpose. It is detailed documentation on the various spatial classes and the methods that are applicable. There are descriptions and examples of how to visually display geospatial data. The chapter on data import and export covers GDAL/OGR, coordinate reference systems, projections and transformations, and what you would need to work with formats such as shapefiles, PostGIS, KML, image files such as tiff files and Google Earth overlays (PNG), or directly with GRASS, TerraLib, or Python interfaces with ArcGIS, RPyGeo.
The last part is on implementing geostatistical methods such as for pattern analysis, geostatistics, areal analysis (geographic aggregation), or epidemiology. I cannot comment too much on this as this is not an area that I have expertise, but the methods look both adequate as well as practical to use.
While the intended audience of this book are statisticians working with geospatial data, I would also recommend this to those who do data analysis or modeling with geospatial data. Most of the analytical texts I've seen discuss algorithms. This text gets into the practicalities of working with real data formats and real data issues that are the inevitable first step in a project. And it does so at a more analytical level then the point and click interface instructions that are enabled by standard GIS systems alone.
August 21, 2021
It is slightly outdated but still the best of its kind. The main drawback is that everything is presented by using the older class of `sp` objects instead of the much more versatile and revolutionary `sf` spatial data frames (invented by Edzer Pebesma, one of the co-authors). They are also more intuitive for beginners.
Other than that this is a stunning applied text that contains just almost anything that you could think of. From spatial inference to spatial prediction....
Other than that this is a stunning applied text that contains just almost anything that you could think of. From spatial inference to spatial prediction....
February 17, 2020
Today this book is a bit old, despite of that still is a great source for spatial data analysis and modeling. Everyone working with spatial data must read it.
One of the best sources on the field.
One of the best sources on the field.
October 2, 2021
Very useful to start using R for spatial stats.
May 1, 2010
Useful stuff! Haven't properly gone through it all the way, but seems like a good start to doing spatial stats in R (with other open-source tools like GRASS).
May 17, 2017
Although most of the examples were not very close to my discipline (ecology), I was easily able to work through the examples in R using the provided data and then substitute in some of my own data/shapefiles to see how things worked. That made this overview of the field really useful!
Displaying 1 - 7 of 7 reviews