Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.

Spatial analysis (Statistics) --- R (Computer program language) --- Data processing. --- GNU-S (Computer program language) --- Domain-specific programming languages --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- Epidemiology. --- Ecology. --- Regional economics. --- Econometrics. --- Geography. --- Regional/Spatial Science. --- Monitoring/Environmental Analysis. --- Geography, general. --- Cosmography --- Earth sciences --- World history --- Economics, Mathematical --- Statistics --- Economics --- Regional planning --- Regionalism --- Space in economics --- Balance of nature --- Biology --- Bionomics --- Ecological processes --- Ecological science --- Ecological sciences --- Environment --- Environmental biology --- Oecology --- Environmental sciences --- Population biology --- Diseases --- Public health --- Ecology --- Ecology . --- Spatial economics. --- Environmental monitoring. --- Biomonitoring (Ecology) --- Ecological monitoring --- Environmental quality --- Monitoring, Environmental --- Applied ecology --- Environmental engineering --- Pollution --- Spatial economics --- Regional economics --- Measurement --- Monitoring --- R (Computer program language).

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.

Spatial analysis (Statistics) --- R (Computer program language) --- Data processing --- R (Computer program language). --- Data processing. --- Mathematical statistics --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- GNU-S (Computer program language) --- Domain-specific programming languages --- Spatial analysis (Statistics) - Data processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Quantitative methods (economics) --- Geology. Earth sciences --- General ecology and biosociology --- Epidemiology --- Environmental protection. Environmental technology --- epidemiologie --- ecologie --- econometrie --- gegevensanalyse --- geologie --- milieutechnologie

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)