This reply is a little late, but after much searching, I cannot find an R
package/function that fits my needs.

I am trying to perform either bilinear or  bicubic interpolation using great
circle distances (ie using a spherical model of the earth).  Here is what I
have tried:

gstat: Can only perform kriging or IDW
akima: Cannot interpolate regularly gridded data and does not support
Coordinate Reference Systems
fields: Thin plate spline function does not seem to support regularly
gridded data or Coordinate Refernec Systems
rsaga: Only supported on windows (I need unix based)

I'm now at a bit of a loss where to look.  It seems the regular R functions
are very good at bilinear and bicubic interpolation in cartesian geometry,
but I can't find a way of using great circle distances for interpolation.
Are there any more avenues I should investigate?

Thanks,

Greg

On Tue, Apr 7, 2009 at 11:06 PM, Scott W Mitchell <
smitch@connect.carleton.ca> wrote:

>
> Greg, you just need to come back here!  We just hired Murray Richardson,
> and he's a whiz at R-SAGA.
>
> ;)
>
> Cheers,
> Scott
>
>
>
> On 7-Apr-09, at 16:32 , Greg King wrote:
>
>  Thanks for your reply Roger.  I little while back I got kriging working in
>> the gstat package with my data, but found that for my purposes it was too
>> computationally expensive (read slow).  Therefore I am looking for
>> something
>> a little quicker.  I now appreciate I cannot use interp due to it treating
>> coordinates as planar.  I found a good tutorial for kriging in gstat at
>> http://casoilresource.lawr.ucdavis.edu/drupal/node/442.  From a quick
>> play
>> around I can't immediately see how functions like Tps in the field package
>> meet my needs.  Could anyone provide directions to primers on the web?.
>> Googling for R is quite tricky (even with rseek).
>>
>> Essentially I want a fast simple method for interpolating a single point
>> value from its surounding points.  It shouldn't be too difficult but I
>> must
>> admit I am struggling a little!
>>
>> Greg
>>
>> On Tue, Apr 7, 2009 at 8:26 AM, Roger Bivand <Roger.Bivand@nhh.no> wrote:
>>
>>  On Mon, 6 Apr 2009, Greg King wrote:
>>>
>>> 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.
>>>>
>>>>
>>> Have you read the Spatial Task View on your nearest CRAN? There you
>>> should
>>> find some information to help. Firstly, akima treats all coordinates as
>>> planar, so don't use interp(). Both fields and gstat can interpolate
>>> using
>>> geographical coordinates, gstat with IDW and kriging, fields with thin
>>> plate
>>> splines (I believe using rdist.earth, but have not checked) and kriging.
>>> You
>>> are trying to interpolate, so why not use a geostatistical method? You
>>> could
>>> see whether the new automap package (running gstat internally) can handle
>>> geographical coordinates - check by comparing the output with runs with
>>> the
>>> proj4string set and unset.
>>>
>>> Hope this helps,
>>>
>>> Roger
>>>
>>>
>>>
>>>  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
>>>>
>>>>
>>>>  --
>>> 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: Roger.Bivand@nhh.no
>>>
>>>
>>>
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