Hi
On 11/23/2017 10:04 AM, Stefano Sofia wrote:
Thank you Sarah and Mike for your explanations. My final objective is
to produce maps (png image or any kind of extension I can import in
LaTeX) where rainfall data are interpolated, using the Inverse
Distance method or Kriging. My input file (pointfile.csv in the
reported example) reports the station code, lat and long of the
meteorological station and the rainfall value (which might be the
cumulate of a week or ten days or the period I need to investigate).
Here a small example: Station_Code, Init_Year, Init_Month, Init_Day,
Init_Hour, Init_Minute, Fin_Year, Fin_Month, Fin_Day, Fin_Hour,
Fin_Minute, Rainfall_Cumulate, Long, Lat 1056, 2017 , 11 , 1 , 0 , 0 ,
2017 , 11 , 11 , 0 , 0 , 28.40, 12.786904, 43.851849 1064, 2017 , 11 ,
1 , 0 , 0 , 2017 , 11 , 11 , 0 , 0 , 27.20, 12.967556, 43.762669 1072,
2017 , 11 , 1 , 0 , 0 , 2017 , 11 , 11 , 0 , 0 , 21.80, 12.897710,
43.907555 As far as I can understand (as you can see, I am not an
expert on GIS or any spatial topic) - my input file pointfile.csv is
in "observation-per-row form"; - I need a grid (file.asc) where I can
interpolate my rainfall data (I can get it, a resolution of 1km will
be enough for me)
If I understand, in order to interpolate point data to a grid, the steps
you need to do in R are:
1. import the CSV of rain data and convert to a SpatialPointsDataFrame
2. Convert that SPDF to a projected coordinate system, such as UTM, for
kriging
3. create an empty grid as the target for kriging, probably based on
the extent of the rain data
4. Run kriging using the point rain data and target grid
Here's a basic workflow for the above
#-----------------------------------
# Required libraries
library(gstat)
library(automap)
library(rgdal)
# Read in CSV file and convert to SPDF
rain_data <- read.csv("pointfile.csv")
str(rain_data)
# Check what you have so far
point_coords <- rain_data[c("Long","Lat")]
coordinates(rain_data) <- point_coords
p4str <- CRS("+init=epsg:4326") # Since the coordinates are in
Long/Lat, first declare this CRS
proj4string(rain_data) <- p4str
# Now Convert to UTM
p4str_UTM <- CRS("+init=epsg:32633")
rain_data_UTM <- spTransform(rain_data, p4str_UTM)
str(rain_data_UTM) # Check that this is a SPDF in the UTM coordinate
system
# Create Grid for kriging output, using the extent of the rain data SPDF
minx <- rain_data_UTM@bbox[1,1]
maxx <- rain_data_UTM@bbox[1,2]
miny <- rain_data_UTM@bbox[2,1]
maxy <- rain_data_UTM@bbox[2,2]
pixel <- 1000 # Each pixel will be 1000 meters
grd <- expand.grid(x=seq(minx, maxx, by=pixel), y=seq(miny, maxy, by=pixel))
coordinates(grd) <- ~x+y
gridded(grd) <- TRUE
proj4string(grd) <- p4str_UTM
# Kriging, using autoKrige which creates a best guess variogram
# The formula for ordinary kriging is "<data_column> ~ 1"
OK_rain <- autoKrige(Rainfall_Cumulate ~ 1, rain_data_UTM, grd)
#-----------------------------------
The kriging result contains a component "prediction" which you can
either plot directly, convert to an R raster object for plotting with
ggplot2, or export to a Geotiff.
HTH,
Micha
Dear R users,
I need to produce rainfall maps using R. I know that this is
possible, I looked though the web, I found the example below reported
(the author is Andrew Tredennick). I would ask you if this is the
most performing way to make rainfall maps; if yes would someone be
able to give me an example of how file.asc and pointfile.csv should
be? If no would somebody please show me another way providing a small
example? Thank you for your help Stefano
-- Micha Silver Ben Gurion Univ. Sde Boker, Remote Sensing Lab cell:
+972-523-665918
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
