[R-sig-Geo] Example of universal kriging with R/gstat in GRASS
Thomas Adams
Thomas.Adams at noaa.gov
Sat May 13 02:34:04 CEST 2006
Roger,
I have made considerable progress in what I was trying to do with gstat
& Universal Kriging using R. I was fortunate enough to find a detailed
example of what I was trying to do: http://spatial-analyst.net/RKguide.php.
I exported from GRASS my elevation grid, "gtopo30.dem", and temperature
point file, "temps.txt", to ascii files to assure I followed a parallel
path with my data.
So, with my data, following Tomislav Hengl's example, I have:
> temps<-read.delim("temps.txt",sep=" ")
> summary(temps)
cat lon lat z T name
Min. : 1.00 Min. :-90.05 Min. :35.82 Min. : 99.0 Min. :61.00 AGC : 1
1st Qu.: 31.75 1st Qu.:-86.63 1st Qu.:38.27 1st Qu.: 197.0 1st Qu.:64.00
AID : 1
Median : 60.50 Median :-84.06 Median :40.09 Median : 255.5 Median :66.00
ALN : 1
Mean : 60.37 Mean :-84.14 Mean :39.86 Mean : 314.2 Mean :66.83 AOH : 1
3rd Qu.: 89.25 3rd Qu.:-81.47 3rd Qu.:41.58 3rd Qu.: 360.0 3rd Qu.:69.00
AOO : 1
Max. :118.00 Max. :-78.32 Max. :42.49 Max. :1155.0 Max. :73.00 ARB : 1
(Other):110
X Y
Min. :-341460 Min. :-342057
1st Qu.: -53847 1st Qu.: -71919
Median : 163999 Median : 119784
Mean : 154474 Mean : 99916
3rd Qu.: 371950 3rd Qu.: 282632
Max. : 632335 Max. : 398186
> library(sp)
> dem<-read.asciigrid("gtopo30.dem")
> class(dem)
[1] "SpatialGridDataFrame"
attr(,"package")
[1] "sp"
> image(dem)
> points(Y ~ X, data=temps)
> class(temps)
[1] "data.frame"
> coordinates(temps)=~X+Y
> dem.ov=overlay(dem,temps)
> summary(dem.ov)
Object of class SpatialPointsDataFrame
Coordinates:
min max
X -341459.8 632334.6
Y -342056.9 398185.9
Is projected: NA
proj4string : [NA]
Number of points: 116
Data attributes:
gtopo30.dem
Min. : 115.3
1st Qu.: 196.9
Median : 245.4
Mean : 306.9
3rd Qu.: 331.0
Max. :1064.5
> temps$gtopo30.dem=dem.ov$gtopo30.dem
> library(lattice)
> plot(T~gtopo30.dem, as.data.frame(temps))
> abline(lm(T~gtopo30.dem, as.data.frame(temps)))
> library(gstat)
> vgm <- vgm(psill=8,model="Exp",range=600000,nugget=3.8)
> vgm_temps_r<-fit.variogram(variogram(T~gtopo30.dem,temps), model=vgm)
> plot(variogram(T~gtopo30.dem,temps),main = "fitted by gstat")
> temps_uk<-krige(T~gtopo30.dem,temps,dem, vgm_temps_r)
[using universal kriging]
> library(lattice)
> trellis.par.set(sp.theme())
> spplot(temps_uk,"var1.pred", main="Universal kriging predictions
TEMPERATURE")
Which works perfectly on my Macintosh running Mac OS X 10.4 and using R
2.2.1. (see attachment, temperatures in deg. F) However, following the
*identical* steps with the identical data on Linux, at the step:
temps_uk<-krige(T~gtopo30.dem,temps,dem, vgm_temps_r)
I get the error:
Error in eval(expr, envir, enclos) : object "gtopo30.dem" not found
This has me baffled; any thoughts? I could send you my files if you
would like to see what happens for you…
Regards,
Tom
BTW, the grid spacing on my DEM is coarse (9 km) and I will probably do
my final analyses at 1-km.
Roger Bivand wrote:
> On Fri, 28 Apr 2006, Thomas Adams wrote:
>
>
>> Roger,
>>
>> Your suggestion:
>>
>> fullgrid(dem) <- FALSE
>>
>> did turn dem into class type SpatialGridDataFrame, but when I tried:
>>
>> z <- predict(UK_fit,newdata=dem)
>>
>> I got an error:
>>
>> Error in model.frame(... :
>> invalid variable type.
>>
>> I think I should restate the problem:
>>
>> I have a file 'temps' which has class SpatialPointsDataFrame read from
>> GRASS 6.1, that looks like:
>>
>> coordinates cat x y z temp name
>> 1 (-341460, -2154.42) 1 -90.05 38.90 166 63 ALN
>> 2 (-198769, 301388) 2 -88.47 41.77 215 67 ARR
>> 3 (-334899, -40321) 3 -89.95 38.55 140 66 BLV
>> 4 (-240028, 163910) 4 -88.92 40.48 268 69 BMI
>> 5 (-187957, 114806) 5 -88.27 40.04 229 64 CMI
>> 6 (-351730, -37305.9) 6 -90.15 38.57 126 65 CPS
>> 7 (-242424, 98244.7) 7 -88.92 39.87 204 66 DEC
>> 8 (-179844, 315889) 8 -88.24 41.91 232 69 DPA
>> 9 (-136093, -24538.2) 9 -87.61 38.76 131 68 LWV
>> 10 (-278964, -126152) 10 -89.25 37.78 125 66 MDH
>> 11 (-140792, 302011) 11 -87.75 41.79 187 73 MDW
>> 12 (-364737, 274189) 12 -90.51 41.45 180 73 MLI
>> 13 (-190503, 54493.9) 13 -88.28 39.48 219 64 MTO
>>
>> and I have a a file 'dem' which has class SpatialGridDataFrame which
>> just consists of grid of elevation values read from GRASS 6.1 using
>> dem<-readFLOAT6sp(). (Sorry, I know I'm repeating myself).
>>
>> What I want to do is to use the grid of elevation values ('dem') as a
>> proxy in the spatial interpolation of the 'temp' values in my 'temps'
>> file that are located at the coordinates in parentheses(). Notice that
>> the temps file also has 'z' values of elevations. So, is this what you
>> already understood? Converting 'dem' to a SpatialPixelsDataFrame seemed
>> to only leave me with the grid locations and not the elevation values —
>> is this right.
>>
>
> What does:
>
> summary(dem)
>
> say before and after doing
>
> fullgrid(dem) <- FALSE?
>
> Afterwards it should be a SpatialPixelsDataFrame with
>
> names(dem)
>
> being "z". Saying summary(dem) will give you an idea of what is inside,
> str() should too.
>
> Roger
>
> PS. This is usually a one-off thing, once it works, you note down how, and
> then it just does from then on.
>
>
>
>> Thanks again for your help!
>>
>> Regards,
>> Tom
>>
>>
>> Roger Bivand wrote:
>>
>>> On Fri, 28 Apr 2006, Thomas Adams wrote:
>>>
>>>
>>>
>>>> Roger,
>>>>
>>>> This got me further along, but I am encountering a problem with:
>>>>
>>>> z <- predict(UK_fit, newdata=BMcD_SPx)
>>>>
>>>> The gstat step works for me, where I have:
>>>>
>>>> UK_fit<-gstat(formula=temps$temp~dem,data=temps,model=efitted)
>>>>
>>>> temps has class SpatialPointsDataFrame:
>>>>
>>>> coordinates cat x y z temp name
>>>> 1 (-341460, -2154.42) 1 -90.05 38.90 166 63 ALN
>>>> 2 (-198769, 301388) 2 -88.47 41.77 215 67 ARR
>>>> 3 (-334899, -40321) 3 -89.95 38.55 140 66 BLV
>>>> 4 (-240028, 163910) 4 -88.92 40.48 268 69 BMI
>>>> 5 (-187957, 114806) 5 -88.27 40.04 229 64 CMI
>>>> 6 (-351730, -37305.9) 6 -90.15 38.57 126 65 CPS
>>>> 7 (-242424, 98244.7) 7 -88.92 39.87 204 66 DEC
>>>> 8 (-179844, 315889) 8 -88.24 41.91 232 69 DPA
>>>> 9 (-136093, -24538.2) 9 -87.61 38.76 131 68 LWV
>>>> 10 (-278964, -126152) 10 -89.25 37.78 125 66 MDH
>>>> 11 (-140792, 302011) 11 -87.75 41.79 187 73 MDW
>>>> 12 (-364737, 274189) 12 -90.51 41.45 180 73 MLI
>>>> 13 (-190503, 54493.9) 13 -88.28 39.48 219 64 MTO
>>>>
>>>> and dem has class SpatialGridDataFrame and just consists of grid values.
>>>>
>>>>
>>> I think
>>>
>>> fullgrid(dem) <- FALSE
>>>
>>> should make a SpatialPixelsDataFrame, but you'll have to make sure the
>>> name of the dem variable is the same as in the formula.
>>>
>>> Roger
>>>
>>>
>>>
>>>> I tried to create a SpatialPixelsDataFrame for predict(), but with (for
>>>> example):
>>>>
>>>> m = SpatialPixelsDataFrame(points=meuse.grid[c("x","y")],data=meuse.grid)
>>>>
>>>> I have nothing like meuse.grid, so this does not work. I can use
>>>> image(dem), which produces a plot of elevation values. My point is that
>>>> meuse.grid and my dem files have very different structures.
>>>>
>>>> I'm not sure where to go to from here.
>>>>
>>>> Regards,
>>>> Tom
>>>>
>>>>
>>>> Roger Bivand wrote:
>>>>
>>>>
>>>>> On Thu, 27 Apr 2006, Thomas Adams wrote:
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>> List:
>>>>>>
>>>>>> I can not seem to work out the syntax for using R/gstat within a GRASS
>>>>>> 6.1 session to do universal kriging. I have a DEM (elevation data on a
>>>>>> grid) and point data for temperature; theoretically, the temperatures
>>>>>> should relate to elevation. So, I am trying to spatially interpolate the
>>>>>> temperature data based on the elevations at the grid points. How do I
>>>>>> setup the gstat command in R/gstat (and using spgrass6, of course)? I
>>>>>> have no trouble reading in my elevation data (DEM) from GRASS and I have
>>>>>> no problem doing ordinary kriging of my temperature data using
>>>>>> GRASS/R/gstat.
>>>>>>
>>>>>>
>>>>>>
>>>>> What do the data look like? Do you have temperature and elevation at the
>>>>> observation points and elevation over the grid? If temperature is the
>>>>> variable for which you want to interpolate, then the formula argument in
>>>>> the gstat() function would be temp ~ elev, data=pointsdata (if a
>>>>> SpatialPointsDataFrame no need for location= ~ x + y). Then the predict()
>>>>> step would need a SpatialGridDataFrame object as newdata, with elev as
>>>>> (one of) the columns in the data slot.
>>>>>
>>>>> An example for the Meuse bank data in Burrough and McDonnell:
>>>>>
>>>>> cvgm <- variogram(Zn ~ Fldf, data=BMcD, width=100, cutoff=1000)
>>>>> uefitted <- fit.variogram(cvgm, vgm(psill=1, model="Exp", range=100,
>>>>> nugget=1))
>>>>> UK_fit <- gstat(id="UK_fit", formula = Zn ~ Fldf, data = BMcD,
>>>>> model=uefitted)
>>>>> z <- predict(UK_fit, newdata=BMcD_SPx)
>>>>>
>>>>> where BMcD_SPx is a SpatialPixelsDataFrame (the grid has ragged edges)
>>>>> with flood frequencies in Fldf (actually a factor, but works neatly).
>>>>>
>>>>> Hope this helps,
>>>>>
>>>>> Roger
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>> Regards,
>>>>>> Tom
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
--
Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177
EMAIL: thomas.adams at noaa.gov
VOICE: 937-383-0528
FAX: 937-383-0033
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