Creating a random set of points is a standard GIS technique, used often for setting up sampling or monitoring locations. Among the tools in FOSS GIS software which offer this function are QGIS (in Vector -> Reseach Tools -> Random points) or GRASS (using the
v.random module). But suppose you need points spread randomly along a line feature? The R-project package of spatial functions called ‘sp‘ can do just that.
In this example we’ll look at a stream network, and show how to use R from within GRASS to produce a set of points located randomly along the streams. Before beginning, I downloaded some SRTM tiles, zoomed into the area of interest, and set the GRASS region (with g.region) appropriately. Then, I ran r.watershed to create a stream network, and r.water.outlet to delineate the basin boundary. Here’s the result (click on the image to view full size):
Before firing up R, we need to be aware that the sampling function
spsample works only on projected data. So we first create a new GRASS location, defined in some projected CRS, and use the
v.proj module to re-project the streams layer to the new projection. Now, with GRASS running in the new projected location, start the R interface by typing R at the GRASS command prompt:
GRASS 6.4.2 (ITM):~ > R
R version 2.14.1 (2011-12-22)
Copyright (C) 2011 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
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The packages needed for spatial analyses, and interaction with GRASS include “sp”, “spgrass6”, “rgdal”. So first install these by using the install.packages() function at the R prompt:
This process will also download and install a long list of required dependency packages. Once the process completes, add the needed packages into this session with the library() function:
Loading required package: XML
GRASS GIS interface loaded with GRASS version: GRASS 6.4.2 (2012)
and location: ITM
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 1.8.1, released 2011/07/09
Path to GDAL shared files: /usr/share/gdal
Loaded PROJ.4 runtime: Rel. 4.7.1, 23 September 2009, [PJ_VERSION: 470]
Path to PROJ.4 shared files: (autodetected)
With those preliminaries done, our R session is ready to import and work with GRASS layers. First import the streams vector into R:
> streams <- readVECT6("streams")
Now we use the spsample function from the “sp” package to create 100 sample points placed randomly along the streams:
> samp.pts <- spsample(streams, 100, type="random")
Object of class SpatialPoints
coords.x1 170389.7 229808.5
coords.x2 505654.0 548203.3
Is projected: TRUE
[+proj=tmerc +lat_0=31.73439361111111 +lon_0=35.20451694444445
+k=1.0000067 +x_0=219529.584 +y_0=626907.39 +ellps=GRS80 +units=m
Number of points: 100
Now we have our random points, let’s export the data back to GRASS. First note carefully that this R object “samp.pts” is of type SpatialPoints (see the Object of class… line in the summary output above). To export any R spatial object requires first converting it to a SpatialDataFrame object or SDF. The function which does this conversion is SpatialPointsDataFrame(). This function takes three parameters: the coordinates of the spatial points, a data.frame object (the attributes of the points) and a correct proj4 CRS string. Here’s how it’s done:
> samp.coords <- coordinates(samp.pts)
> samp.p4str <- proj4string(samp.pts)
> samp.df <- as.data.frame(samp.pts)
> samp.spdf <- SpatialPointsDataFrame(coords=samp.coords, proj4string=CRS(samp.p4str), data=samp.df)
We employed the
as.data.frame() function to return the data.frame of the sample points. Now samp.spdf contains a fully defined SDF of the sample points, ready for export to GRASS. So:
> writeVECT6(samp.spdf, "sample_pts")
Here we’ve exported the R SDF “samp.pts” to a new GRASS vector called “sample_pts”. After quitting R to return to the GRASS prompt, we add this new GRASS vector to our map to view the results: