Dear Peter, The spatial taskview lists a number of interpolation methods [1]. Some of those support spatio-temporal interpolation. For example gstat supports spatio-temporal kriging [2,3,4]. regards, Paul [1] http://cran.r-project.org/web/views/Spatial.html [2] http://en.wikipedia.org/wiki/Kriging [3] http://www.google.nl/search?q=space+time+kriging [4] http://cran.r-project.org/web/packages/gstat/index.html
On 07/30/2011 08:35 PM, Peter Maclean wrote:
Dear GIS people
What is the best way of implemeting spatial data interpolation (from large to small grids)-especially for dummies. I searched the internet and could not get concrete answer. Here is an example with simulated data.
#Example of spatial data interpolation
require(utils)
#I need to interpolate the temp and rain data (from its surounding points)
#for the same period and accoubting for elevation
#New coordinates and elevation
lat <-seq(-1, -5, by=-0.1)
lon <-seq(28, 30, by=0.1)
year <- seq(2000, 2005, by=1)
period <- c("Mar", "Apr","May")
ndata <- list(year=year,period=period,lat=lat, lon=lon)
ndata <- expand.grid(ndata)
ndata$elev <-sample(1000: 8000,nrow(ndata),replace=T)
ndata <- ndata[order(ndata$year,ndata$period) , ]
fix(ndata)
#Original data with elevation-same period
lat <- seq(-1, -5, by=-0.5)
lon <- seq(28, 30, by=0.5)
data <- list(year=year,period=period,lat=lat, lon=lon)
data <- expand.grid(data)
data$temp <- sample(15:100, nrow(data),replace=T)
data$rain <- sample(0: 1000,nrow(data),replace=T)
data <- data[order(data$year,data$period) , ]
data <- na.omit(merge(data,ndata, by=c("year", "period", "lat","lon")))
fix(data)
##########
#Spatial-Temporal Interpolation from original data (temp & rain) to new data
Peter Maclean
Department of Economics
UDSM
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Paul Hiemstra, Ph.D. Global Climate Division Royal Netherlands Meteorological Institute (KNMI) Wilhelminalaan 10 | 3732 GK | De Bilt | Kamer B 3.39 P.O. Box 201 | 3730 AE | De Bilt tel: +31 30 2206 494 http://intamap.geo.uu.nl/~paul http://nl.linkedin.com/pub/paul-hiemstra/20/30b/770