On Tue, 26 Aug 2008, Michael Friendly wrote:
Edzer Pebesma wrote:
Michael Friendly wrote:
> Two short questions about working with maps:
>
> 1. I'm reading a shapefile with character labels for the regions (FSA).
> I can add the labels using plot(),
> but when I try the same thing using spplot(), the labels are in the
> wrong positions -- they all seem to be
> shrunk somewhat in toward the center of the map. What am I doing wrong?
>
> # this doesn't work-- labels in wrong position
> spplot(toronto,"FSA_NAME", colorkey=FALSE)
> text(coordinates(toronto), labels=as.character(toronto$FSA), cex=0.4)
Right: text() works with base graphics, not with lattice on which spplot
is built.
Something like this should work:
spplot(toronto,"FSA_NAME", colorkey=FALSE,
sp.layout = list("sp.text", coordinates(toronto),
as.character(toronto$FSA), cex=0.4))
Great! Now I also know where to look to generalize this.
>
> 2. I have a bunch of attribute variables for the geographic regions, all
> on different scales. Id like to
> produce a set of comparative maps in the same figure (say with spplot())
> with each attribute shaded
> by its quantiles, e.g., 5 classes each. Do I have to precompute these
> first, or is there something I can do in the call
> to spplot() to have this done, using the variables in the
> SpatialPolygonsDataFrame?
What exactly did you mean by "all on different scales"? They have
different polygon structures?
No - some of the attribute values are percents, some are quantitative &
positively skewed, like Income. If I do
spplot(toronto, c("Household.Income","Unemployed","University"))
a single scale is applied to all three, so the two % variables are shaded
uniformly in the lowest range.
What I'd like is to apply a function to take each of these and recode into
quantiles for that variable.
After a little digging around, it looks as though the plot() method for
trellis objects (pp. 202-206 in the Lattice book) provides a way to
generate a single graphic from multiple calls to spplot, something like:
p1 <- spplot(toronto, c("Household.Income"))
p2 <- spplot(toronto, c("Unemployed"))
p3 <- spplot(toronto, c("University"))
plot(p1, split=c(1,1,2,2), more=TRUE)
plot(p2, split=c(1,2,2,2), more=TRUE)
plot(p3, split=c(2,1,2,2), more=FALSE)
using at=, col.regions=, main=, etc. in each of the spplot calls as
appropriate for the selected variables. With the same col.regions= and at=
based on quantiles (perhaps floor() for the first and ceiling() for the
last), this should be pretty close visually, but with a key for each
variable.
It's partly that my data variables are now in the map object and, from the
help, I only know how to refer to
zcol= names of these, rather than some transformations on the underlying
data.
The alternative might be to assign new derived variables to the
Spatial*DatatFrame object, which for all intents and purposes "is" a data
frame, and spplot() them.
Roger
--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: [EMAIL PROTECTED]
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