Analysing spatial point patterns in R - CSIRO

32.1 Numeric marks: distribution and trend 201
To assess spatial trend in the marks, one way is to form a kernel regression smoother. The
smoothed mark value at location u ∈ R 2 is
∑
i
̂m(u) =
m iκ(u − x i )
∑
i κ(u − x i)
where k is the smoothing kernel, and m i is the mark value at data point x i . This is computed
by smooth.ppp:
> plot(smooth.ppp(longleaf))
smooth.ppp(longleaf)
15 20 25 30 35 40
The plot shows that there is a region of younger trees in the northeast of the study region.
If the marks are a data frame, the result of smooth.ppp will be a list of pixel images, one for
each mark variable.
You can also use cut.ppp followed by split.ppp to look for spatial inhomogeneity of the
marks:
> data(spruces)
> plot(split(cut(spruces, breaks = 3)))
split(cut(spruces, breaks = 3))
(0.16,0.23] (0.23,0.3] (0.3,0.37]
Other facilities include markvar which calculates a smoothed estimate of the local variance
of the mark values.
CopyrightCSIRO 2010