Provided by: grass-doc_8.3.0-1_all 
NAME
r.univar - Calculates univariate statistics from the non-null cells of a raster map.
Statistics include number of cells counted, minimum and maximum cell values, range,
arithmetic mean, population variance, standard deviation, coefficient of variation, and
sum.
KEYWORDS
raster, statistics, univariate statistics, zonal statistics, parallel
SYNOPSIS
r.univar
r.univar --help
r.univar [-getr] map=name[,name,...] [zones=name] [output=name]
[percentile=float[,float,...]] [nprocs=integer] [separator=character] [--overwrite]
[--help] [--verbose] [--quiet] [--ui]
Flags:
-g
Print the stats in shell script style
-e
Calculate extended statistics
-t
Table output format instead of standard output format
-r
Use the native resolution and extent of the raster map, instead of the current region
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
map=name[,name,...] [required]
Name of raster map(s)
zones=name
Raster map used for zoning, must be of type CELL
output=name
Name for output file (if omitted or "-" output to stdout)
percentile=float[,float,...]
Percentile to calculate (requires extended statistics flag)
Options: 0-100
Default: 90
nprocs=integer
Number of threads for parallel computing
Default: 1
separator=character
Field separator
Special characters: pipe, comma, space, tab, newline
Default: pipe
DESCRIPTION
r.univar calculates the univariate statistics of one or several raster map(s). This
includes the number of cells counted, minimum and maximum cell values, range, arithmetic
mean, population variance, standard deviation, coefficient of variation, and sum.
Statistics are calculated separately for every category/zone found in the zones input map
if given. If the -e extended statistics flag is given the 1st quartile, median, 3rd
quartile, and given percentile are calculated. If the -g flag is given the results are
presented in a format suitable for use in a shell script. If the -t flag is given the
results are presented in tabular format with the given field separator. The table can
immediately be converted to a vector attribute table which can then be linked to a vector,
e.g. the vector that was rasterized to create the zones input raster.
When multiple input maps are given to r.univar, the overall statistics are calculated.
This is useful for a time series of the same variable, as well as for the case of a
segmented/tiled dataset. Allowing multiple raster maps to be specified saves the user from
using a temporary raster map for the result of r.series or r.patch.
NOTES
As with most GRASS raster modules, r.univar operates on the raster array defined by the
current region settings, not the original extent and resolution of the input map. See
g.region, but also the wiki page on the computational region to understand the impact of
the region settings on the calculations.
This module can use large amounts of system memory when the -e extended statistics flag is
used with a very large region setting. If the region is too large the module should exit
gracefully with a memory allocation error. Basic statistics can be calculated using any
size input region. Extended statistics can be calculated using r.stats.quantile.
Without a zones input raster, the r.quantile module will be significantly more efficient
for calculating percentiles with large maps.
For calculating univariate statistics from a raster map based on vector polygon map and
uploads statistics to new attribute columns, see v.rast.stats.
PERFORMANCE
r.univar supports parallel processing using OpenMP. The user can specify the number of
threads to be used with the nprocs parameter. However, parallelization is disabled when
the -e extended statistics flag is used.
Due to the differences in summation order, users may encounter small floating points
discrepancies when r.univar is run on very large raster files when different nprocs
parameters are used. However, since the work allocation among threads is static, users
should expect to have the same results when run with the same number of threads.
Figure: Benchmark shows execution time for different number of cells and cores. See
benchmark scripts in source code.
EXAMPLES
Univariate statistics
In this example, the raster map elevation in the North Carolina sample dataset is used to
calculate univariate statistics:
g.region raster=elevation -p
# standard output, along with extended statistics
r.univar -e elevation percentile=98
total null and non-null cells: 2025000
total null cells: 0
Of the non-null cells:
----------------------
n: 2025000
minimum: 55.5788
maximum: 156.33
range: 100.751
mean: 110.375
mean of absolute values: 110.375
standard deviation: 20.3153
variance: 412.712
variation coefficient: 18.4057 %
sum: 223510266.558102
1st quartile: 94.79
median (even number of cells): 108.88
3rd quartile: 126.792
98th percentile: 147.727
# script style output, along with extended statistics
r.univar -ge elevation percentile=98
n=2025000
null_cells=0
cells=2025000
min=55.5787925720215
max=156.329864501953
range=100.751071929932
mean=110.375440275606
mean_of_abs=110.375440275606
stddev=20.3153233205981
variance=412.712361620436
coeff_var=18.4056555243368
sum=223510266.558102
first_quartile=94.79
median=108.88
third_quartile=126.792
percentile_98=147.727
Zonal statistics
In this example, the raster polygon map basins in the North Carolina sample dataset is
used to calculate raster statistics for zones for elevation raster map:
g.region raster=basins -p
This will set and print computational region in the format:
projection: 99 (Lambert Conformal Conic)
zone: 0
datum: nad83
ellipsoid: a=6378137 es=0.006694380022900787
north: 228500
south: 215000
west: 630000
east: 645000
nsres: 10
ewres: 10
rows: 1350
cols: 1500
cells: 2025000
Check basin’s IDs using:
r.category basins
This will print them in the format:
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
Visualization of them underlying elevation map can be created as:
d.mon wx0
d.rast map=elevation
r.colors map=elevation color=grey
d.rast map=basins
r.colors map=basins color=bgyr
d.legend raster=basins use=2,4,6,8,10,12,14,16,18,20,22,24,26,28,30
d.barscale
Figure: Zones (basins, opacity: 60%) with underlying elevation map for North Carolina
sample dataset.
Then statistics for elevation can be calculated separately for every zone, i.e. basin
found in the zones parameter:
r.univar -t map=elevation zones=basins separator=comma \
output=basin_elev_zonal.csv
This will print information in the format:
zone,label,non_null_cells,null_cells,min,max,range,mean,mean_of_abs,
stddev,variance,coeff_var,sum,sum_abs2,,116975,0,55.5787925720215,
133.147018432617,77.5682258605957,92.1196971445722,92.1196971445722,
15.1475301152556,229.447668592576,16.4433129773355,10775701.5734863,
10775701.57348634,,75480,0,61.7890930175781,110.348838806152,
48.5597457885742,83.7808205765268,83.7808205765268,11.6451777476995,
135.610164775515,13.8995747088232,6323776.33711624,6323776.33711624
6,,1137,0,66.9641571044922,83.2070922851562,16.2429351806641,
73.1900814395257,73.1900814395257,4.15733292896409,17.2834170822492,
5.68018623179036,83217.1225967407,83217.12259674078,,80506,
0,67.4670791625977,147.161514282227, ...
Comma Separated Values (CSV) file is best viewed through a spreadsheet program such as
Microsoft Excel, Libre/Open Office Calc or Google Docs:
Figure: Raster statistics for zones (basins, North Carolina sample dataset) viewed through
Libre/Open Office Calc.
TODO
To be implemented mode, skewness, kurtosis.
SEE ALSO
g.region, r3.univar, r.mode, r.quantile, r.series, r.stats, r.stats.quantile,
r.stats.zonal, r.statistics, v.rast.stats, v.univar
AUTHORS
Hamish Bowman, Otago University, New Zealand
Extended statistics by Martin Landa
Multiple input map support by Ivan Shmakov
Zonal loop by Markus Metz
SOURCE CODE
Available at: r.univar source code (history)
Accessed: Tuesday Jun 27 11:13:20 2023
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