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="")
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