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 a value of class Date |
julian.date |
number of days elapsed since 1 January 1998 |
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 Year-month-day, e.g.
2005-07-14 means 14 July, 2005.
The accompanying dataset clmfires.extra
is a list
of two items clmcov100
and clmcov200
containing covariate
information for the entire Castilla-La Mancha region. Each
of these two elements 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
. clmfires.extra
is a list with two components, named
clmcov100
and clmcov200
, which are lists of pixel images
(objects of class "im"
).jitter()
function from R
or the
spatstat function 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(clmfires.extra$clmcov100)
# Split the clmfires pattern by year and plot the first and last years:
yr <- factor(format(marks(clmfires)$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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