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NAME

       v.univar  - Calculates univariate statistics of vector map features.
       Variance and standard deviation is calculated only for points if specified.

KEYWORDS

       vector, statistics, univariate statistics, attribute table, geometry

SYNOPSIS

       v.univar
       v.univar --help
       v.univar  [-gewd]  map=name   [layer=string]    [type=string[,string,...]]   [column=name]
       [where=sql_query]   [percentile=integer]   [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -g
           Print the stats in shell script style

       -e
           Calculate extended statistics

       -w
           Weigh by line length or area size

       -d
           Calculate geometric distances instead of attribute statistics

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       map=name [required]
           Name of vector map
           Or data source for direct OGR access

       layer=string
           Layer number or name
           Vector features can have category values in different layers. This  number  determines
           which layer to use. When used with direct OGR access this is the layer name.
           Default: 1

       type=string[,string,...]
           Input feature type
           Options: point, line, boundary, centroid, area
           Default: point,line,area

       column=name
           Name of attribute column

       where=sql_query
           WHERE conditions of SQL statement without ’where’ keyword
           Example: income < 1000 and population >= 10000

       percentile=integer
           Percentile to calculate (requires extended statistics flag)
           Options: 0-100
           Default: 90

DESCRIPTION

       v.univar calculates univariate statistics on (by default) an attribute of, or, through the
       -d flag on distance between, vector map features.  Attributes are read per feature and per
       category  value.  This  means  that  if  the  map  contains several features with the same
       category value, the attribute is read as many times as there are features.  On  the  other
       hand,  if  a feature has more than one category value, each attribute value linked to each
       of the category values of the feature  is  read.  For  statistics  on  one  attribute  per
       category value, instead of one attribute per feature and per category, see v.db.univar.

       Extended  statistics  (-e)  adds median, 1st and 3rd quartiles, and 90th percentile to the
       output.

NOTES

       When using the -d flag, univariate statistics of distances  between  vector  features  are
       calculated.  The  distances  from  all  features to all other features are used. Since the
       distance from feature A to feature B is the same like  the  distance  from  feature  B  to
       feature  A,  that  distance  is  considered only once, i.e. all pairwise distances between
       features are used. Depending on the selected vector  type,  distances  are  calculated  as
       follows:

           •   type=point: point distances are considered;

           •   type=line: line to line distances are considered;

           •   type=area:  not  supported,  use  type=centroid  instead  (and  see v.distance for
               calculating distances between areas)

EXAMPLES

       The examples are based on the North Carolina sample dataset.

   Example dataset preparation
       g.region raster=elevation -p
       v.random output=samples npoints=100
       v.db.addtable map=samples columns="heights double precision"
       v.what.rast map=samples rast=elevation column=heights
       v.db.select map=samples

   Calculate height attribute statistics
       v.univar -e samples column=heights type=point
       number of features with non NULL attribute: 100
       number of missing attributes: 0
       number of NULL attributes: 0
       minimum: 57.2799
       maximum: 148.903
       range: 91.6235
       sum: 10825.6
       mean: 108.256
       mean of absolute values: 108.256
       population standard deviation: 20.2572
       population variance: 410.356
       population coefficient of variation: 0.187123
       sample standard deviation: 20.3593
       sample variance: 414.501
       kurtosis: -0.856767
       skewness: 0.162093
       1st quartile: 90.531
       median (even number of cells): 106.518
       3rd quartile: 126.274
       90th percentile: 135.023

   Compare to statistics of original raster map
       r.univar -e elevation
       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
       90th percentile: 138.66

   Calculate statistic of distance between sampling points
       v.univar -d samples type=point
       number of primitives: 100
       number of non zero distances: 4851
       number of zero distances: 0
       minimum: 69.9038
       maximum: 18727.7
       range: 18657.8
       sum: 3.51907e+07
       mean: 7254.33
       mean of absolute values: 7254.33
       population standard deviation: 3468.53
       population variance: 1.20307e+07
       population coefficient of variation: 0.478132
       sample standard deviation: 3468.89
       sample variance: 1.20332e+07
       kurtosis: -0.605406
       skewness: 0.238688

SEE ALSO

        db.univar, r.univar, v.db.univar, v.distance, v.neighbors, v.qcount

AUTHORS

       Radim Blazek, ITC-irst

       extended by:
       Hamish Bowman, University of Otago, New Zealand
       Martin Landa

SOURCE CODE

       Available at: v.univar source code (history)

       Accessed: unknown

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