[R-sig-Geo] colour mapping tesselated irregular xy data

[baptiste auguie]

Thanks, it is cleaner indeed. However, it's still painfully slow on
large data sets (typically a couple hundred thousand points). Looking
at the source of various functions, it looks like the place to look at
would be deldir::tile.list(). To my naive eyes it doesn't look
particularly efficient, given that the "meat" of the calculation that
I expected more costly was performed by deldir() in much less time,

N <- 1000
set.seed(123)
d <- data.frame(x=rnorm(N, 15, 4), y=rnorm(N, 15, 4))

library(spatstat)
library(maptools)

W <- owin(c(0, 30), c(0, 30))
X <- as.ppp(d, W=W)

system.time(dd <- deldir(d$x, d$y) )
   user  system elapsed
  0.079   0.009   0.088

system.time(Y <- dirichlet(X))
   user  system elapsed
  5.494   0.074   5.756

tile.list() would probably benefit greatly from being ported to C++,
and currently returns a list where I really want a data.frame to avoid
subsequent looping. Further coercion steps that I attempted earlier
are unnecessary as I wouldn't use the tess or SpatialPolygons classes.

Suggestions and comments most welcome.

baptiste

On 13 April 2011 14:36, Hadley Wickham <hadley at rice.edu> wrote:
> Did you try fortify on the SpatialPolygons?
> Hadley
>
> On Tue, Apr 12, 2011 at 4:24 PM, baptiste auguie
> <baptiste.auguie at googlemail.com> wrote:
>> Dear list,
>>
>> I have x,y,z data originating from an electromagnetic simulation on an
>> irregular mesh. I wish to visualise a 2D map of these (N~1e6) data
>> points with colour-coded z intensity.
>>
>> On way is to perform an interpolation onto a regular grid. This works
>> (using akima's interp for example), but is not what I'm after. The xy
>> mesh is very fine at places, and rough at others, and interpolation
>> loses this spatial information. Thus, I wish to display coloured tiles
>> of irregular size. Voronoi and delaunay tesselations seem quite suited
>> to the task; however I am stuck with two technical difficulties.
>>
>> 1- while voronoi/dirichlet gives me N polygons, delaunay obviously
>> returns more triangular tiles than there are points initially. How
>> could I perform the interpolation for the assignment of colours?
>>
>> 2- spatstat and maptools seem to work with special|spatial| classes,
>> from which I have been unable to efficiently extract the polygon
>> outlines in a data.frame. This is necessary because I wish to use
>> lattice or ggplot2 for plotting, rather than the default plot method.
>>
>> Below's a minimal illustration,
>>
>> N <- 100
>> set.seed(123)
>> d <- data.frame(x=rnorm(N, 15, 4), y=rnorm(N, 15, 4))
>> ## a circular intensity pattern
>> d$z = (d$x - 15)^2 + (d$y - 15)^2
>>
>> library(spatstat)
>> library(maptools)
>>
>> W <- ripras(df, shape="rectangle")
>> W <- owin(c(0, 30), c(0, 30))
>> X <- as.ppp(d, W=W)
>> Y <- dirichlet(X)
>> Z <- as(Y, "SpatialPolygons")
>> ## default plot
>> ## plot(Z, col=grey(d$z/max(d$z)))
>>
>> ## awfully inefficient extraction of the polygons...
>>
>> extract.tiles <- function(x){
>>  data.frame(x$bdry[[1]][1:2])
>> }
>>
>> all <- lapply(Y$tiles, extract.tiles )
>>
>> library(ggplot2)
>> ## convert list to long format data.frame with ids
>> names(all) <- seq_along(all)
>> m <- melt(all, id=1:2)
>> m$fill <- d$z[as.integer(m$L1)]
>>
>> ggplot(m)+
>>  geom_polygon(aes(x,y,fill=fill, group=fill))+
>>  scale_fill_gradient(low="black", high="white")
>>
>> library(latticeExtra)
>> ## result similar (except for the edges) to latticeExtra's
>> panel.voronoi, as intended
>> levelplot(z~x*y, data=d,
>>         panel = function(...) panel.voronoi(..., points=FALSE),
>>         col.regions = colorRampPalette(grey(0:1))(1e3), cut=1e3)
>>
>>
>> ## now trying the same with delaunay tiles
>> Y1 <- delaunay(X)
>> Z1 <- as(Y1, "SpatialPolygons")
>> ## no link between original z-values and tiles
>> plot(Z1, col=grey(d$z/max(d$z)))
>>
>>
>> Best regards,
>>
>> baptiste
>>
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>
>
>
> --
> Assistant Professor / Dobelman Family Junior Chair
> Department of Statistics / Rice University
> http://had.co.nz/
>