Spatial Interpolation of Regularly Gridded Data

library(RSAGA)
library(RCurl)
out.url <-

write(out.url, "out.csv")
out <- read.csv("out.csv", header=T, sep=",")
str(out)
coordinates(out) <- ~lon+lat
proj4string(out) <- CRS("+proj=longlat +datum=WGS84")
writeOGR(out, ".", "out", "ESRI Shapefile")

rsaga.geoprocessor(lib="grid_gridding", module=3, param=list(GRID="out5deg.sgrd", INPUT="out.shp",

rsaga.geoprocessor(lib="grid_tools", module=0, param=list(INPUT="out5deg.sgrd",

-----Original Message-----
From: r-sig-geo-bounces at stat.math.ethz.ch [mailto:r-sig-geo-bounces at stat.math.ethz.ch] On Behalf
Of Greg King
Sent: Tuesday, April 07, 2009 12:01 AM
To: r-sig-geo at stat.math.ethz.ch
Subject: [R-sig-Geo] Spatial Interpolation of Regularly Gridded Data

Hi,

I'm fairly new to R, but finding the experience a good one.  However I am a little overwhelmed by
the number of packages and not sure I am necessarily using the most appropriate ones.

Here is the background to what I am trying to achieve:  I have a CSV file which contains weather
forecast data for latitude and longitude points (see attached out.csv, the data I understand is on
WGS84 http://www.ready.noaa.gov/faq/geodatums.html).  The sample points are at half degree
intervals.  My objective is to work out what the forecast data is at any specific given
latitude/longitude by interpolating data from the 0.5x0.5 degree grid.  I am doing this for a
number of different time points using the following functions:


library(akima)
library(rgdal)

gks <- function(inLat,inLon,dframe,variab) {
    wind.grid <- expand.grid(lat=inLat, lon=inLon)
    coordinates(wind.grid) <- ~ lat+lon
    proj4string(wind.grid) <- CRS("+init=epsg:4326")
    pnt<-interpp(coordinates(dframe)[,1], coordinates(dframe)[,2], z=as.data.frame(dframe)[,1],
coordinates(wind.grid)[,1],coordinates(wind.grid)[,2],linear=TRUE)
    return(pnt$z)
}

interp_gk <- function(lat, lon) {
    wind<-read.csv(file="/Users/greg/Out.csv",head=TRUE,sep=",",
colClasses=c("POSIXct","numeric","numeric","numeric","numeric"))
    coordinates(wind)=~lat+lon
    proj4string(wind) <- CRS("+init=epsg:4326")

    times<-unique(wind$vt)
    columns<-names(wind)[2:length(names(wind))]

    dOut<-data.frame(dateTime=times[1])

    for (i in 1:length(times)) {
    dOut[i,"dateTime"]<-times[i]
        for (j in 1:length(columns)) {
            sset<-subset(wind, wind$vt==times[i], select=c(columns[j]))
            dOut[i,columns[j]]<-gks(lat,lon,sset,columns[j])
        }
    }
    dOut<-cbind(dOut, mag=sqrt(dOut$ugrd_0^2+dOut$vgrd_0^2))
    return(dOut)
}


However, I have the following concerns:


*	Should I really be using akima?  The documentation states it is not for use on regularly
spaced grids - what are my alternatives?
*	The interp funcrtion will not work for cubic spline "linear=FALSE" interpolation (is it
because my data is regularly gridded?).  How can I achieve cubic spline interpolation?
*	Is my function really using the Cordinate reference system specified?  When I comment out
the CRS lines, my functions return the same values?


Lots of questions I appreciate, but I am curious!  It seems R can achieve what I am trying to
do... but I may just be missing some vital bits of information.

Thanks,

Greg