pred.strk: Spatio-temporal regression kriging

Description Usage Arguments Value Author(s) References See Also Examples

Description

Function for spatio-temporal regression kriging prediction based on krigeST. The prediction is made for raster objects, i.e. for STFDF-class objects.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
pred.strk(temp,zcol = 1, newdata, pred.id = "tempPred", zero.tol = 0, 
  dynamic.cov = c(1, 2), static.cov = c(1, 2),
reg.coef=list(
tmean=c(-0.126504415,0.4051734447,0.4943247727,0.0001837527,-0.0189207588),  
tmin = c(-0.9825601517,0.5672140021,0.3344561638, 0.0003119777,-0.0243629638),
tmax = c(1.7873573081,0.350228076, 0.5569091092, 0.0002571338,-0.0012988123)
)[['tmean']],
vgm.model=list( tmean=vgmST("sumMetric",
                                space=vgm( 14.13, "Sph", 5903, 1.933),
                                time =vgm(0, "Sph",  0.1, 0),
                                joint=vgm(9.06, "Sph", 2054, 0.474),
                                 stAni=497.9),
                   tmin = vgmST("sumMetric",
                                space=vgm( 22.682, "Sph", 5725, 3.695),
                                time =vgm(0, "Sph",  0.1, 0),
                                joint=vgm(9.457, "Sph",1888, 1.67),
                                 stAni=485),
                    tmax = vgmST("sumMetric",
                                 space=vgm( 8.31, "Sph", 4930, 2.872),
                                 time =vgm(0, "Sph",  0.1, 0),
                                 joint=vgm(11.175, "Sph", 2117, 1.75),
                                 stAni=527) ) [['tmean']] ,
tiling = FALSE, ntiles = 64, parallel.processing = FALSE, cpus = 3, 
sp.nmax = 18, time.nmax = 2,fast = FALSE, computeVar = FALSE, 
do.cv = FALSE, only.cv = FALSE, out.remove = FALSE, threshold.res = 15,progress=TRUE)

Arguments

temp

object of STFDF-class containing dependent variable (observations) in space and time.

zcol

variable column name or number showing position of dependent variable in [email protected]

newdata

dynamic and static covariates as STFDF-class object; spatial and temporal overlay with temp object must be possible

pred.id

identifier of new variable

zero.tol

distance values less than or equal to this threshold value locations are considered as duplicates, see rm.dupl, duplicates are removed to avoid singular covariance matrices in kriging.

dynamic.cov

vector of variable column names or numbers showing position of dynamic covariates in [email protected]; dynamic covariates are spatio-temporal explanatory variables, changing in space and time domain

static.cov

vector of variable column names or numbers showing position of static covariates in [email protected]@sp; static covariates are spatial explanatory variables changing just in space; static in time dimension

reg.coef

linear regression coefficients; order is assumed as intercept, dynamic.cov, static.cov. Coefficients can be specified by user; depending on type, number and order of dynamic and static covariates. At the moment the function contains regression coefficient for mean, minimum and maximum temperature calculated globally for GSOD and ECA&D data set on geometrical temperature trend, MODIS LST-8 day, elevation and TWI, see regdata. Coefficients for mean temperature are defined by default.

vgm.model

spatio-temporal variogram of regression residuals, see vgmST. At the moment the function contains spatio-temporal variogram model on residuals for mean, minimum and maximum temperature calculated globally for GSOD and ECA&D data set. Regression residuals on geometrical temperature trend, MODIS LST-8 day, elevation and TWI, see regdata. Ranges are in km. Spatio-temporal variogram for mean temperatures is defined by default. User can specified own variogram model as vgmST object.

tiling

for simplified local kriging. Area is divided in tiles and kriging calculation is done for each tile separately, number of observation used per tile is defined with sp.nmax and time.nmax. Default is TRUE. If FALSE just temporal local kriging will be applied defined with time.nmax, sp.nmax will be ignored.

ntiles

number of tiles. Default is 64. Each tile at minimum should contain less observations than sp.nmax, ideally each tile should contain observations falling in neighboring tiles.

parallel.processing

if TRUE parallel processing is performed via sfLapply

cpus

number of processing units

sp.nmax

number of nearest spatial observations that should be used for a kriging prediction for each tile

time.nmax

number of nearest time observations that should be used for a kriging prediction

fast

if TRUE tiling, tiling is done twice to avoid edge effect

computeVar

if TRUE, just variance is computed

do.cv

if TRUE, cross validation leave-one-station-out is performed

only.cv

if TRUE, only cross validation leave-one-station-out is performed without prediction

out.remove

if TRUE, potential outliers are removed. Removing procedure is iterative, all location with residual higher than defined threshold (treshold.res) are selected. Only location with highest cross validation residual is removed, than cross validation is done again, the procedure removing one by one location run until all locations have residuals under defined threshold.

threshold.res

critical threshold for removing potential outliers

progress

if FALSE remove progress bar

Value

An list object containing:

pred

an object of STFDF-class with column contains prediction or variance

cv

cross validation information for points used in prediction, as object of STFDF-class

out

potential outliers, returned as vector of row names of [email protected], only returned if out.remove=FALSE

remst

removed locations as an object of Spatial-class, if out.remove=TRUE

remobs

removed locations with observations as an object of STFDF-class, if out.remove=TRUE

Author(s)

Milan Kilibarda [email protected]

References

Kilibarda, M., T. Hengl, G. B. M. Heuvelink, B. Graeler, E. Pebesma, M. Percec Tadic, and B. Bajat (2014), Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution, J. Geophys. Res. Atmos., 119, 2294-2313, doi:10.1002/2013JD020803.

See Also

regdata meteo2STFDF tgeom2STFDF

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
# prepare data
# load observation - data.frame of mean temperatures
data(dtempc) 
# str(dtempc)
data(stations)
library(sp)
library(spacetime)
library(gstat)

# str(stations)
## lonmin,lonmax,lonmax, lonmin   latmin, latnmin,latmax,latmax
serbia= point.in.polygon(stations$lon, stations$lat, c(18,22.5,22.5,18), c(40,40,46,46))
st= stations[ serbia!=0, ]
# create STFDF
temp <- meteo2STFDF(dtempc,st)
rm(dtempc)
# str(temp)
# Adding CRS
temp@sp@proj4string <- CRS('+proj=longlat +datum=WGS84')

# load covariates for mean temperatures
data(regdata)
# str(regdata)
regdata@sp@proj4string <- CRS('+proj=longlat +datum=WGS84')

# Calculate prediction of mean temperatures for "2011-07-05" 
# global model is used for regression and variogram
# load precalculated variograms
data(tvgms)
data(tregcoef)
res= pred.strk(temp,zcol=1, newdata= regdata[,1,drop=FALSE], 
              reg.coef=tregcoef[[1]] ,vgm.model=tvgms[[1]], progress=FALSE )

## plot prediction
# stplot(res$pred, col.regions=bpy.colors())


# t1= temp[[email protected],]
# # create fake observations
# [email protected]$tempc[seq(1,120,by=8)] =35
# 
# 
# res= pred.strk(t1,zcol=1, newdata= regdata[,1:2], 
#                reg.coef=tregcoef[[1]], vgm.model=tvgms[[1]] , 
#                threshold.res=5, do.cv=T, out.remove = T)
# # plot cross validation residuals 
# stplot(res$cv[,,'resid.cv'] , col.regions=bpy.colors())
# 
# # plot locations of removed stations
# spplot(res$remst, zcol='station_name' , col.regions=bpy.colors())
# #plot removed stations as time-series
# row.names([email protected]) = res$remst$station_name
# res$remobs[,1:2,c('tempc','pred.cv')]
# stplot(res$remobs[,1:2,c('tempc','pred.cv')], mode='tp')

## Calculate prediction of mean temperature for "2011-07-05" "2011-07-06"
## only MODIS is used as covariate

# modisVGM =vgmST("sumMetric",space=vgm( 18.27, "Sph", 6000, 3.22),
#                                           time =vgm(0, "Sph",  0.1, 0),
#                                           joint=vgm(8.34, "Sph", 2349, 1.80),
#                                           stAni=583)
# attr(modisVGM,"temporal unit") = "days"                                           

# rkmod <-  pred.strk(temp,zcol=1, newdata= STFDF([email protected],
#                time=as.POSIXct("2011-07-05"), endTime=as.POSIXct("2011-07-06"), 
#                data=regdata[,1]@data) , threshold.res=10, 
#              dynamic.cov='modis', static.cov=NULL,
#               reg.coef= c(-0.23,0.7303284),
#               vgm.model= modisVGM  )
                                           
## coefficients and variogram is calculated globally for GSOD and ECA&D obs. for 2011 year 

# stplot(rkmod$pred, col.regions=bpy.colors())

## parallel processing
# library(snowfall)
# rkmod <-  pred.strk(temp,zcol=1, 
#                   newdata= STFDF([email protected],
#                    time=as.POSIXct("2011-07-05"), endTime=as.POSIXct("2011-07-06"), 
#                    data=regdata[,1]@data) ,
#                     threshold.res=10, 
#                     dynamic.cov='modis', static.cov=NULL,
#                     reg.coef= c(-0.23,0.7303284),
#                     vgm.model= modisVGM, parallel.processing=TRUE)

meteo documentation built on May 2, 2019, 3:29 p.m.