r.stats
Generates area statistics for raster map.
r.stats [-acpl1gxArnNCi] input=name [,name,...] [output=name] [separator=character] [null_value=string] [nsteps=integer] [sort=string] format=name [--overwrite] [--verbose] [--quiet] [--qq] [--ui]
Example:
r.stats input=name format=plain
grass.script.parse_command("r.stats", input, output=None, separator="space", null_value="*", nsteps=255, sort=None, format="plain", flags=None, overwrite=False, verbose=False, quiet=False, superquiet=False)
Example:
gs.parse_command("r.stats", input="name", format="json")
Parameters
input=name [,name,...] [required]
Name of raster map(s) to report on
output=name
Name for output file (if omitted or "-" output to stdout)
separator=character
Field separator
Special characters: pipe, comma, space, tab, newline
Default: space
null_value=string
String representing NULL value
Default: *
nsteps=integer
Number of floating-point subranges to collect stats from
Default: 255
sort=string
Sort output statistics by cell counts
Default: sorted by categories or intervals
Allowed values: asc, desc
asc: Sort by cell counts in ascending order
desc: Sort by cell counts in descending order
format=name [required]
Output format
Allowed values: plain, json
Default: plain
plain: Plain text output
json: JSON (JavaScript Object Notation)
-a
Print area totals in square meters
-c
Print cell counts (sortable)
-p
Print approximate (total percent may not be 100%) percents
-l
Print category labels
-1
One cell (range) per line
-g
Print grid coordinates (east and north)
-x
Print x and y (column and row)
Indexing starts with 1: first column and row are 1
-A
Print averaged values instead of intervals (floating-point maps only)
-r
Print raw indexes of floating-point ranges (floating-point maps only)
-n
Do not report no data value
-N
Do not report cells where all maps have no data
-C
Report for cats floating-point ranges (floating-point maps only)
-i
Read floating-point map as integer (use map's quant rules)
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--qq
Very quiet module output
--ui
Force launching GUI dialog
input : str | list[str], required
Name of raster map(s) to report on
Used as: input, raster, name
output : str, optional
Name for output file (if omitted or "-" output to stdout)
Used as: output, file, name
separator : str, optional
Field separator
Special characters: pipe, comma, space, tab, newline
Used as: input, separator, character
Default: space
null_value : str, optional
String representing NULL value
Used as: string
Default: *
nsteps : int, optional
Number of floating-point subranges to collect stats from
Default: 255
sort : str, optional
Sort output statistics by cell counts
Default: sorted by categories or intervals
Allowed values: asc, desc
asc: Sort by cell counts in ascending order
desc: Sort by cell counts in descending order
format : str, required
Output format
Used as: name
Allowed values: plain, json
plain: Plain text output
json: JSON (JavaScript Object Notation)
Default: plain
flags : str, optional
Allowed values: a, c, p, l, 1, g, x, A, r, n, N, C, i
a
Print area totals in square meters
c
Print cell counts (sortable)
p
Print approximate (total percent may not be 100%) percents
l
Print category labels
1
One cell (range) per line
g
Print grid coordinates (east and north)
x
Print x and y (column and row)
Indexing starts with 1: first column and row are 1
A
Print averaged values instead of intervals (floating-point maps only)
r
Print raw indexes of floating-point ranges (floating-point maps only)
n
Do not report no data value
N
Do not report cells where all maps have no data
C
Report for cats floating-point ranges (floating-point maps only)
i
Read floating-point map as integer (use map's quant rules)
overwrite: bool, optional
Allow output files to overwrite existing files
Default: False
verbose: bool, optional
Verbose module output
Default: False
quiet: bool, optional
Quiet module output
Default: False
superquiet: bool, optional
Very quiet module output
Default: False
DESCRIPTION
r.stats calculates the area present in each of the categories or floating-point intervals of user-selected input raster map. Area statistics are given in units of square meters and/or cell counts. This analysis uses the current geographic region (g.region) and mask settings (r.mask). The output statistics can be saved to a output file.
Area statistics is printed in square meters for each category when -a is given. Similarly if -c flag is chosen, areas will be stated also in number of cells.
NOTES
If a single raster map is specified, a list of categories will be printed. The -x flag will print x and y (column and row) starting with 1 (both first row and first column are indexed with 1). If multiple raster maps are specified, a cross-tabulation table for each combination of categories in the raster maps will be printed.
For example, if one raster map was specified, the output would look like:
1 1350000.00
2 4940000.00
3 8870000.00
If three raster maps were specified, the output would look like:
0 0 0 8027500.00
0 1 0 1152500.00
1 0 0 164227500.00
1 0 1 2177500.00
1 1 0 140092500.00
1 1 1 3355000.00
2 0 0 31277500.00
2 0 1 2490000.00
2 1 0 24207500.00
2 1 1 1752500.00
3 0 0 17140000.00
3 1 0 11270000.00
3 1 1 2500.00
Within each grouping, the first field represents the category value of first raster map, the second represents the category values associated with second raster map, the third represents category values for third raster map, and the last field gives the area in square meters for the particular combination of these three raster maps' categories. For example, above, combination 3,1,1 covered 2500 square meters. Fields are separated by the separator option. The output from r.stats is sorted by category or category intervals (for floating-point raster maps).
Note that the user has only the option of printing out cell statistics in terms of cell counts and/or area totals. Users wishing to use different units than are available here should use r.report.
EXAMPLES
Report sorted number of cells and area for each category
Report sorted number of cells and area for each category in a single raster map:
g.region raster=geology_30m
r.stats -ac input=geology_30m sort=desc sep=tab
Output (category, area, cell count):
217 72556200.000000 80618
270 68937300.000000 76597
405 25371000.000000 28190
262 19868400.000000 22076
862 6172200.000000 6858
910 4496400.000000 4996
583 2160900.000000 2401
921 1252800.000000 1392
766 707400.000000 786
720 482400.000000 536
946 406800.000000 452
948 87300.000000 97
945 900.000000 1
import grass.script as gs
gs.run_command("g.region", raster="geology_30m")
data = gs.parse_command("r.stats", flags="a", input="geology_30m", format="json")
for record in data:
cat = record['categories'][0]['category']
print(f"Category {cat}: {record['count']} cells and {record['area'] / 1e6} km2")
Output:
Category 217: 80618 cells and 72.5562 km2
Category 270: 76597 cells and 68.9373 km2
Category 405: 28190 cells and 25.371 km2
Category 262: 22076 cells and 19.8684 km2
Category 862: 6858 cells and 6.1722 km2
Category 910: 4996 cells and 4.4964 km2
Category 583: 2401 cells and 2.1609 km2
Category 921: 1392 cells and 1.2528 km2
Category 766: 786 cells and 0.7074 km2
Category 720: 536 cells and 0.4824 km2
Category 946: 452 cells and 0.4068 km2
Category 948: 97 cells and 0.0873 km2
Category 945: 1 cells and 0.0009 km2
The JSON output looks like:
[
{
"categories": [
{
"category": 217
}
],
"area": 72556200,
"count": 80618
},
{
"categories": [
{
"category": 270
}
],
"area": 68937300,
"count": 76597
},
...
]
Report area, number of cells, and percents in multiple raster maps
Report area, number of cells, and percents (separated by tabs) for each category in multiple raster maps (suppress NULL data):
g.region raster=towns
r.stats -nacp input=towns,urban separator=tab
Output (town, urban, area, number of cells, percent):
1 55 23475900.000000 234759 11.65%
2 55 14142700.000000 141427 7.02%
3 55 1519700.000000 15197 0.75%
4 55 16051400.000000 160514 7.97%
5 55 99004400.000000 990044 49.14%
6 55 19888500.000000 198885 9.87%
import grass.script as gs
gs.run_command("g.region", raster="towns")
data = gs.parse_command("r.stats", flags="nacp", input=["towns", "urban"], format="json")
for record in data:
categories = [str(cat["category"]) for cat in record["categories"]]
print(f"Categories {' and '.join(categories)}: {record['percent']:.2f} %")
Output:
Categories 1 and 55: 11.65 %
Categories 2 and 55: 7.02 %
Categories 3 and 55: 0.75 %
Categories 4 and 55: 7.97 %
Categories 5 and 55: 49.14 %
Categories 6 and 55: 9.87 %
The JSON output looks like:
[
{
"categories": [
{
"category": 1
},
{
"category": 55
}
],
"area": 23475900,
"count": 234759,
"percent": 11.651235678463038
},
{
"categories": [
{
"category": 2
},
{
"category": 55
}
],
"area": 14142700,
"count": 141427,
"percent": 7.0191102718021128
},
...
]
Report sorted area intervals of floating-point raster map
Report sorted area for each interval of floating-point input raster map. Number of intervals are given by nsteps option.
g.region raster=elevation
r.stats -an input=elevation nsteps=5 sort=desc separator=tab
Output:
95.879221-116.029436 67133500.000000
116.029436-136.17965 54757900.000000
75.729007-95.879221 47817700.000000
136.17965-156.329865 26061600.000000
55.578793-75.729007 6729300.000000
import grass.script as gs
gs.run_command("g.region", raster="elevation")
data = gs.parse_command("r.stats", flags="an", input="elevation", nsteps=5, sort="desc", format="json")
for record in data:
from_to = record['categories'][0]['range']
print(f"Elevation {from_to['from']:.2f}-{from_to['to']:.2f}: {record['area'] / 1e6:.2f} km2")
Output:
Elevation 95.88-116.03: 67.13 km2
Elevation 116.03-136.18: 54.76 km2
Elevation 75.73-95.88: 47.82 km2
Elevation 136.18-156.33: 26.06 km2
Elevation 55.58-75.73: 6.73 km2
The JSON output looks like:
[
{
"categories": [
{
"range": {
"from": 95.879221343994146,
"to": 105.9543285369873
}
}
],
"area": 37063800
},
{
"categories": [
{
"range": {
"from": 85.804114151000974,
"to": 95.879221343994146
}
}
],
"area": 30826100
},
...
]
Report grid coordinates and category values of a raster map
Report category and east, north and row, column of a rasterized firestations map:
g.region region=wake_30m
v.to.rast input=firestations output=firestations use=cat
r.stats input=firestations -gxn
Output (east, north, column, row, category):
641835 253485 1033 155 42
644715 247785 1129 345 41
654045 246915 1440 374 38
641865 243615 1034 484 40
638055 243285 907 495 43
...
import grass.script as gs
gs.run_command("g.region", region="wake_30m")
gs.run_command("v.to.rast", input="firestations", output="firestations", use="cat")
data = gs.parse_command("r.stats", flags="gxn", input="firestations", format="json")
print(data[0])
Output:
{'east': 641835, 'north': 253485, 'col': 1033, 'row': 155, 'categories': [{'category': 42}]}
The JSON output looks like:
[
{
"east": 641835,
"north": 253485,
"col": 1033,
"row": 155,
"categories": [
{
"category": 42
}
]
},
{
"east": 644715,
"north": 247785,
"col": 1129,
"row": 345,
"categories": [
{
"category": 41
}
]
},
...
Report raster cell counts in multiple raster maps
Report raster cell counts of landuse and geological categories within zipcode areas:
g.region raster=zipcodes
# landuse/landcover, geology and zipcodes with category labels
r.stats -c input=landclass96,zipcodes,geology_30m separator=comma -l
Output:
1,developed,27511,CARY,405,CZbg,18410
1,developed,27511,CARY,583,CZve,1298
1,developed,27511,CARY,862,CZam,86
1,developed,27513,CARY,405,CZbg,2287
1,developed,27513,CARY,583,CZve,971
1,developed,27518,CARY,217,CZfg,5724
1,developed,27518,CARY,405,CZbg,3910
1,developed,27518,CARY,862,CZam,1012
1,developed,27529,GARNER,270,CZig,61497
1,developed,27539,APEX,921,Km,246
...
import grass.script as gs
gs.run_command("g.region", raster="zipcodes")
# landuse/landcover, geology and zipcodes with category labels
data = gs.parse_command("r.stats", flags="c", input=["landclass96", "zipcodes", "geology_30m"], format="json")
print(data[0])
Output:
{'categories': [{'category': 1}, {'category': 27511}, {'category': 405}], 'count': 18410}
The JSON output looks like:
[
{
"categories": [
{
"category": 1,
"label": "developed"
},
{
"category": 27511,
"label": "CARY"
},
{
"category": 405,
"label": "CZbg"
}
],
"count": 18410
},
...
]
Read r.stats results into a Pandas DataFrame
import pandas as pd
import grass.script as gs
gs.run_command("g.region", raster="zipcodes")
maps = ["landclass96", "zipcodes", "geology_30m"]
data = gs.parse_command("r.stats", flags="an", input=maps, format="json")
# Map to raster names
for item in data:
for i, name in enumerate(maps):
item[name] = item["categories"][i]["category"]
# Create DataFrame and drop the original 'categories'
df = pd.DataFrame(data).drop(columns="categories")
print(df)
Output:
area landclass96 zipcodes geology_30m
0 1841000 1 27511 405
1 129800 1 27511 583
2 8600 1 27511 862
3 228700 1 27513 405
4 97100 1 27513 583
.. ... ... ... ...
165 64400 6 27607 217
166 7000 6 27607 262
167 19200 6 27610 270
168 106600 7 27603 270
169 54400 7 27607 217
[170 rows x 4 columns]
SEE ALSO
g.region, r.report, r.coin, r.describe, r.stats.quantile, r.stats.zonal, r.statistics, r.univar
AUTHORS
Michael Shapiro, U.S. Army Construction Engineering Research
Laboratory
Sort option by Martin Landa, Czech Technical University in Prague, 2013
SOURCE CODE
Available at: r.stats source code
(history)
Latest change: Tuesday Jun 24 05:16:04 2025 in commit 84a8a6c