The dataset clmfires is a point pattern (object of class
  "ppp") containing the spatial coordinates of each fire,
  with marks containing information about each fire.  There are 4
  columns of marks:
  cause 	cause of fire (see below) 
burnt.area 	total area burned, in hectares 
date 	the date of fire as an object of class Date 
julian.date 	number of days elapsed since 1 January 1998 
}
  The cause of the fire is a factor with the levels
  lightning, accident (for accidents or negligence),
  intentional (for intentionally started fires) and
  other (for other causes including unknown cause).
  The format of date is 
  Accompanying this point pattern, there are two datasets
  clmcov100 and clmcov200 containing covariate
  information for the entire Castilla-La Mancha region. Each
  of these two datasets is a list of four images (objects of
  class "im") named elevation, orientation,
  slope and landuse.  The landuse image is
  factor-valued with the factor having levels urban,
  farm (for farms or orchards), meadow,
  denseforest (for dense forest), conifer (for conifer
  forest or plantation), mixedforest, grassland,
  bush, scrub and artifgreen for artificial
  greens such as golf courses.
  These images (effectively) provide values for the four
  covariates at every location in the study area. The images in
  clmcov100 are 100 by 100 pixels in size, while those in
  clmcov200 are 200 by 200 pixels.  For easy handling,
  clmcov100 and clmcov200 also belong to the
  class "listof" so that they can be plotted and printed
  immediately.
data(clmfires)clmfires is a marked point pattern (object of class "ppp"). 
  See ppp.object.  clmcov100 and clmcov200 are lists of pixel images
  (objects of class "im").
There is however no actual duplication of points in the 1998 to 2003
patterns due to jitter() function from R or the
rjitter.
Of course there are many sets of points which are virtually identical, being separated by distances induced by the jittering. Typically these distances are of the order of 40 metres which is unlikely to be meaningful on the scale at which forest fires are observed.
Caution should therefore be exercised in any analyses of the patterns for the years 1998 to 2003.
plot(clmfires, which.marks="cause", cols=2:5, cex=0.25)
plot(clmcov100)
# Split the clmfires pattern by year and plot the first and last years:
yr  <- factor(format(marks(clmfires)$date,format="%Y"))
X   <- split(clmfires,f=yr)
fAl <- c("1998","2007")
plot(X[fAl],use.marks=FALSE,main.panel=fAl,main="")Run the code above in your browser using DataLab