[R-sig-Geo] Find nearest downstream value of a river network

[Edzer Pebesma]

Hi Jon (thanks for reminding off-list!), I haven't looked into finding
the line with an ID, but in general finding the nearest SpatialLines
index (which of course might be ambiguous) might be done by:

library(rgeos)
d = gDistance(nres, nres, byid=TRUE)
diag(d)=1e9
apply(d,2,function(x)which(x==min(x))[1])

Does that get you any further? Or are there more clever solutions?
--
Edzer

On 04/13/2011 09:59 AM, Jon Olav Skoien wrote:
> Hi,
> 
> I have a SpatialLinesDataFrame with predictions on different locations
> of a river network that I would like to plot. However, I have many more
> line segments in the network than I have predictions, so most of the
> data.frame has NA-values. Does anyone know a simple way of finding the
> nearest downstream value for a line segment with an NA-value, so that I
> can get a continuous river network with predictions?
> 
> Below is a simple example of what I want to do, based on a shapefile
> ("nres") that can be downloaded from:
> http://intamap.geo.uu.nl/~jon/sarp/tempaacf/
> 
> nres = readOGR(".","nres")
> nin = nres
> nin$pred[!(nin$OBJECTID %in% c(1015, 1020, 1366, 1369, 4981))] = NA
> 
> "nres" is the SpatialLinesDataFrame that I would like to have as result
> after associating all NA-values to the nearest downstream value. "nin"
> is a simplified version of the SpatialLinesDataFrame that I have after
> predicting, and that I would like to use for creating "nres".  "pred" is
> the prediction column in these SLDFs. The following plot shows the
> results (with colors) and thick black lines for the segments where I
> have predictions.
> 
> spplot(nres, "pred", col.regions = bpy.colors(),
>    panel = function(x,y, ...) {
>     panel.polygonsplot(x,y, ...)
>     sp.lines(nin[!is.na(nin$pred),], col = "black", lwd = 2)
>    })
> 
> So far I have got the result in this plot from an iterative procedure
> using the FROMJCT and TOJCT (from and to junction) IDs of the data.frame:
> 
> ichange = 1
> while (ichange > 0) {
>   ichange = 0
>   for (i in 1:dim(nin)[1]) {
>     if (!is.na(nin$pred[i])) {
>       tt = which(nin$TOJCT == nin$FROMJCT[i])
>       if (length(tt) > 0 && is.na(nin$pred[tt])) {
>         nin$pred[tt] = nin$pred[i]
>         ichange = ichange + 1
>       }
>     }
>   }
> }
> 
> But I think it should be either possible to simplify this (quite slow
> for a large river network), or preferably take advantage of topology of
> SpatialLines objects? Any clues?
> 
> Thanks,
> Jon
> 
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-- 
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics      e.pebesma at wwu.de