Author: Paulo van Breugel
Updated on: 2011-06-11
I am looking at vegetation distribution in east Africa, using a map we have developed based on historical vegetation maps from the region, and I wanted to know the surface area per vegetation type per country. In ‘spreadsheet terms’, I wanted to create a cross- or pivot table. GRASS GIS offers several options to do this. Note that below I am using the examples provided in the help files of the functions. Check out these help files for further details.
The function r.report reports statistics for one or more raster map layers. Selecting more then one layer will give you the statistics for each unique combination of the input raster values.
A strong point of r.report is that it allows you to select one or more specific units of measure in which statistics will be reported, including miles and acres (for those few remaining non-metric persons), meters, kilometers, hectares, cell count and percentage cover. Following is the result of a r.report run on the raster map layer geology (located in the Spearfish, SD sample data base), with the units expressed in square miles and acres (example copied from the help file);
As the example shows, the output is nicely, if not a bit old fashioned (but what is wrong with that) formatted table which is easy to read. The output can be directed into a text file or printing it out on screen.
Figure 1. Output table from r.report (from the r.report help file)
Although easy on the eye, it is much more difficult to further process the output. If you want to further work on the output data, e.g., in a spreadsheet or R, you better consider the next function.
Similarly to r.report, r.stats generates area statistics for raster map layers. To quote the help file, “r.stats calculates the area present in each of the categories of user-selected raster map layer(s). Area statistics are given in units of square meters and/or cell counts”. I.e., the number of statics that can be calculated are much smaller. But really, who cares, it is easy enough to calculate the other statistics. Especially because the output is easy to read into a spreadsheet or your favorite statistical program.
For example, if three raster map layers a, b, and c, were specified, the output would look like (again, I am copying the example from the help file):
0:0:0:8027500.00 0:1:0:1152500.00 1:0:0:164227500.00 1:1:1:3355000.00 2:0:0:31277500.00 2:1:0:24207500.00 2:1:1:1752500.00 3:0:0:17140000.00 3:1:1:2500.00
Within each grouping, the first field represents the category value of map layer a, the second represents the category values associated with map layer b, the third represents category values for map layer c, and the last field gives the area in square meters for the particular combination of these three map layers’ categories. Fields are separated by colons, but you can define your own separator.
The help file furthermore states that the output lists the results in square meters, ” assuming the map’s coordinate system is in meters”. I didn’t try out, but I assume that refers to projected layers with map coordinates in alternative units such as miles. It does work for layers in a geographic Coordinate System (this is also true for r.report).
The function r.cross is different from the two functions above in that it creates an output raster map. The map represents all unique combinations of category values in the raster input layers (maximum number of layers is 10). To get statistics on the surface areas for each combination, you will need to use r.stats or r.report.
Let me again show the example from the help file. Suppose that, using two raster map layers, the following combinations occur:
map1 map2 ___________ 0 1 0 2 1 1 1 2 2 4
r.cross would produce a new raster map layer with 5 categories:
map1 map2 output ____________________ 0 1 1 0 2 2 1 1 3 1 2 4 2 4 5
The category file created for the output raster map layer describes the combinations of input map layer category values which generated each category. In the above example, the category values and corresponding labels would be:
value label ______________________________ 1 layer1(0) layer2(1) 2 layer1(0) layer2(2) 3 layer1(1) layer2(1) 4 layer1(1) layer2(2) 5 layer1(2) layer2(4)
So, if you simply want to check the surface area for each each unique combination of the input raster values, use r.report. If you want to further analyze the results in your favorite statistical package, use r.stats (you could for example run this function from within R, piping the results into a R data frame). And if you want to use the combined raster maps in further spatial analysis, use r.cross.
p.s. It would be fairly easy to do this kind of overlays in R, but you might run into trouble with larger data sets (out of the several solutions, working with a combination of GRASS and R happens to be my favorite solution).