Applied Spatial Data Analysis with RSpringer Science & Business Media, 24 août 2008 - 376 pages We began writing this book in parallel with developing software for handling and analysing spatial data withR (R Development Core Team, 2008). - though the book is now complete, software development will continue, in the R community fashion, of rich and satisfying interaction with users around the world, of rapid releases to resolve problems, and of the usual joys and frust- tions of getting things done. There is little doubt that without pressure from users, the development ofR would not have reached its present scale, and the same applies to analysing spatial data analysis withR. It would, however, not be su?cient to describe the development of the R project mainly in terms of narrowly de?ned utility. In addition to being a communityprojectconcernedwiththedevelopmentofworld-classdataana- sis software implementations, it promotes speci?c choices with regard to how data analysis is carried out.R is open source not only because open source software development, including the dynamics of broad and inclusive user and developer communities, is arguably an attractive and successful development model. |
Table des matières
3 | |
Classes for Spatial Data in R 21 | 18 |
Visualising Spatial Data | 57 |
Spatial Data Import and Export | 81 |
Further Methods for Handling Spatial Data | 112 |
6 | 119 |
Customising Spatial Data Classes and Methods | 127 |
7 | 152 |
Interpolation and Geostatistics | 191 |
Areal Data and Spatial Autocorrelation | 237 |
Modelling Areal Data | 273 |
Disease Mapping 311 | 310 |
Afterword | 343 |
361 | |
362 | |
Autres éditions - Tout afficher
Applied Spatial Data Analysis with R Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio Aperçu limité - 2013 |
Applied Spatial Data Analysis with R Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio Aucun aperçu disponible - 2013 |
Applied Spatial Data Analysis with R Roger S. Bivand,Edzer J. Pebesma,Virgilio Gómez-Rubio Aucun aperçu disponible - 2008 |
Expressions et termes fréquents
areal entities argument bandwidth bbox cells Chap clusters coefficient computed coords covariates credible intervals Cressie data frame data set data.frame default Diggle disease mapping distance distance bands distribution error example FALSE fitting function function(x GDAL geographical geographical coordinates geostatistics global grid gstat interpolation K-function kriging likelihood linear model lines listw log(zinc matrix Mean 3rd Median meuse meuse.grid Moran's Neighbour list Number of points NYlistw Object of class ordinary kriging p-value parameters PCTAGE65P PCTOWNHOME PEXPOSURE point patterns point process Poisson polygons prediction proj4string random raster region regression relative risks residuals row.names S-PLUS sample variogram semivariance shapefiles shown in Fig simulation slot spatial autocorrelation spatial correlation spatial data analysis Spatial Epidemiology spatial lag spatial objects spatial weights SpatialLines SpatialPixels SpatialPoints SpatialPointsDataFrame SpatialPolygons specific spplot study area tracts users v.fit values variable variance variogram model vector Waller and Gotway WinBUGS