On Fri, Jul 8, 2011 at 2:26 PM, Robert Hijmans <r.hijm...@gmail.com> wrote:
> # now there are many ways to interpolate. See, e.g., the 'gstat' and > 'automap' packages. See raster::interpolate for an example with splines. > # using gstat and inverse distrance weighted interpolation: > > library(gstat) > g <- gstat(id="level", formula = level~1, data=p, nmax=7, set=list(idp = > .5)) > x1 <- interpolate(r, g) Statistical interpolation techniques may be right for this, but thinking about it last night made me realise that contours are more than just linear estimates of height at location. There's the implication that between any two contour lines of height H1 and H2 there are no locations with height outside the bounds of (H1,H2). Otherwise there would be a contour line there. And this may not be so uncommon in elevation models. Consider a steep sided valley with a wide flood plain. You have close contours on either side with a big gap between. Would a "statistical" interpolation run down the valley side and plummet on down, then back up the other side, turning the flood plain into a deep rounded valley bottom? Sure it all depends on the parameters of the smoothing, but a method that knew it was dealing with contours would constrain the surface such that points between contour lines were always between the contour line values. I think at least one of the algorithms in GRASS-GIS does this by "drawing" the contour lines on the raster and then doing a 'flood fill' operation between them. I'll have to dig out some GIS books... Barry _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo