This is an example of moderately large spatial data set and consists of simulated CO2 concentrations that are irregularly sampled from a lon/lat grid. Also included is the complete CO2 field (CO2.true) used to generate the synthetic observations.
The format of
CO2 is a list with two components:
lon.lat: 26633x2 matrix of the longitude/latitude locations. These are a subset of a larger lon/lat grid (see example below).
y: 26633 CO2 concentrations in parts per million.
The format of
is a list in "image" format with components:
x longitude grid values.
y latitude grid values.
z an image matrix with CO2 concentration in parts per million
mask a logical image that indicates with grid locations were
selected for the synthetic data set
This data was generously provided by Dorit Hammerling and Randy Kawa as a test example for the spatial analysis of remotely sensed (i.e. satellite) and irregular observations. The synthetic data is based on a true CO2 field simulated from a geophysical, numerical model.
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## Not run: data(CO2) # # A quick look at the observations with world map quilt.plot( CO2$lon.lat, CO2$y) world( add=TRUE) # Note high concentrations in Borneo (biomass burning), Amazonia and # ... Michigan (???). # spatial smoothing using the wendland compactly supported covariance # see help( fastTps) for details # First smooth using locations and Euclidean distances # note taper is in units of degrees out<-fastTps( CO2$lon.lat, CO2$y, theta=4, lambda=2.0) #summary of fit note about 7300 degrees of freedom # associated with fitted surface print( out) # image plot on a grid (this takes a while) surface( out, type="I", nx=300, ny=150) # smooth with respect to great circle distance out2<-fastTps( CO2$lon.lat, CO2$y, lon.lat=TRUE,lambda=1.5, theta=4*68) print(out2) #surface( out2, type="I", nx=300, ny=150) # these data are actually subsampled from a grid. # create the image object that holds the data # temp<- matrix( NA, ncol=ncol(CO2.true$z), nrow=nrow(CO2.true$z)) temp[ CO2.true$mask] <- CO2$y # look at gridded object. image.plot(CO2.true$x,CO2.true$y, temp) # to predict _exactly_ on this grid for the second fit; # (this take a while) look<- predictSurface( out2, grid.list=list( x=CO2.true$x, y=CO2.true$y)) image.plot(look) ## End(Not run)