v.vect.stats(1)

NAME
v.vect.stats - Count points in areas, calculate statistics from point attributes.

KEYWORDS
vector, attribute table, database, univariate statistics, zonal statistics

SYNOPSIS
v.vect.stats
v.vect.stats --help
v.vect.stats [-p] points=name areas=name [type=string[,string,...]] [points_layer=string] [points_cats=range] [points_where=sql_query] [areas_layer=string] [areas_cats=range] [areas_where=sql_query] [method=string] [points_column=name] [count_column=name] [stats_column=name] [separator=character] [--help] [--verbose] [--quiet] [--ui]

  Flags:

-p
    Print output to stdout, do not update attribute table
    First column is always area category

--help
    Print usage summary

--verbose
    Verbose module output

--quiet
    Quiet module output

--ui
    Force launching GUI dialog

  Parameters:

points=name [required]
    Name of existing vector map with points
    Or data source for direct OGR access

areas=name [required]
    Name of existing vector map with areas
    Or data source for direct OGR access

type=string[,string,...]
    Feature type
    Input feature type
    Options: point, centroid
    Default: point

points_layer=string
    Layer number for points map
    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

points_cats=range
    Category values for points map
    Example: 1,3,7-9,13

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

areas_layer=string
    Layer number for area map
    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

areas_cats=range
    Category values for area map
    Example: 1,3,7-9,13

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

method=string
    Method for aggregate statistics
    Options: sum, average, median, mode, minimum, min_cat, maximum, max_cat, range, stddev, variance, diversity

points_column=name
    Column name of points map to use for statistics
    Column of points map must be numeric

count_column=name
    Column name to upload points count
    Column to hold points count, must be of type integer, will be created if not existing

stats_column=name
    Column name to upload statistics
    Column to hold statistics, must be of type double, will be created if not existing

separator=character
    Field separator
    Special characters: pipe, comma, space, tab, newline
    Default: pipe

DESCRIPTION
v.vect.stats counts the number of points in vector map points falling into each area in vector map areas. Optionally statistics on point attributes in points are calculated for each area. The results are either uploaded to the attribute table of the vector map areas or printed to stdout.

  Statistical methods
Using numeric attribute values of all points falling into a given area, a new value is determined with the selected method. v.vect.stats can perform the following operations:

sum
    The sum of values.

average
    The average value of all point attributes (sum / count).

median
    The value found half-way through a list of the attribute values, when these are ranged in numerical order.

mode
    The most frequently occurring value.

minimum
    The minimum observed value.

min_cat
    The point category corresponding to the minimum observed value.

maximum
    The maximum observed value.

max_cat
    The point category corresponding to the maximum observed value.

range
    The range of the observed values.

stddev
    The statistical standard deviation of the attribute values.

variance
    The statistical variance of the attribute values.

diversity
    The number of different attribute values.

NOTES
Points not falling into any area are ignored. Areas without category (no centroid attached or centroid without category) are ignored. If no points are falling into a given area, the point count is set to 0 (zero) and the statistics result to "null".

The columns count_column and stats_column are created if not yet existing. If they do already exist, the count_column must be of type integer and the stats_column of type double precision.

In case that v.vect.stats complains about the points_column of the input points vector map not being numeric, the module v.db.update can be used to perform type casting, i.e. adding and populating an additional numeric column with the values type converted from string attributes to floating point numbers.

EXAMPLES

  Preparation for examples
The subsequent examples are based on randomly sampled elevation data (North Carolina sample database):

# work on map copy for attribute editing
g.copy vector=zipcodes_wake,myzipcodes_wake
# set computational region: extent of ZIP code map, raster pixels
# aligned to raster map
g.region vector=myzipcodes_wake align=elev_state_500m -p
#  generate random elevation points
r.random elev_state_500m vector=rand5000 n=5000
v.colors rand5000 color=elevation
# visualization
d.mon wx0
d.vect myzipcodes_wake -c
d.vect rand5000

These vector maps are used for the examples below.

  Count points per polygon with printed output
See above for the creation of the input maps.

Counting points per polygon, print results to terminal:

v.vect.stats points=rand5000 area=myzipcodes_wake -p

  Count points per polygon with column update
See above for the creation of the input maps.

Counting of points per polygon, with update of "num_points" column (will be automatically created):

v.vect.stats points=rand5000 area=myzipcodes_wake count_column=num_points
# verify result
v.db.select myzipcodes_wake column=ZIPCODE_,ZIPNAME,num_points

  Average values of points in polygon with printed output
See above for the creation of the input maps.

Calculation of average point elevation per ZIP code polygon, printed to terminal in comma separated style:

# check name of point map column:
v.info -c rand5000
v.vect.stats points=rand5000 area=myzipcodes_wake \
  method=average points_column=value separator=comma -p

  Average values of points in polygon with column update
See above for the creation of the input maps.

Calculation of average point elevation per ZIP code polygon, with update of "avg_elev" column and counting of points per polygon, with update of "num_points" column (new columns will be automatically created):

# check name of point map column:
v.info -c rand5000
v.vect.stats points=rand5000 area=myzipcodes_wake count_column=num_points \
  method=average points_column=value stats_column=avg_elev
# verify result
v.db.select myzipcodes_wake column=ZIPCODE_,ZIPNAME,avg_elev

  Point statistics in a hexagonal grid
The grid extent and size is influenced by the current computational region. The extent is based on the vector map points_of_interest from the basic North Carolina sample dataset.

g.region vector=points_of_interest res=2000 -pa

The hexagonal grid is created using the v.mkgrid module as a vector map based on the previously selected extent and size of the grid.

v.mkgrid map=hexagons -h

The v.vect.stats module counts the number of points and does one statistics on a selected column (here: elev_m).

v.vect.stats points=points_of_interest areas=hexagons method=average \
  points_column=elev_m count_column=count stats_column=average

User should note that some of the points may be outside the grid since the hexagons cannot cover all the area around the edges (the computational region extent needs to be enlarged if all points should be considered). The last command sets the vector map color table to viridis based on the count column.

v.colors map=hexagons use=attr column=average color=viridis

Point statistics in a hexagonal grid (count of points, average of values associated with point, standard deviation)

SEE ALSO

    •   v.rast.stats for zonal statistics of raster maps using vector zones (univariate statistics of a raster map),

    •   r.stats.zonal for zonal statistics of raster map using raster zones (univariate statistics using two raster maps),

    •   v.what.vect for querying one vector map by another,

    •   v.distance for finding nearest features,

    •   r.distance for computing distances between objects in raster maps,

    •   v.mkgrid for creating vector grids to aggregate point data.

AUTHOR
Markus Metz

SOURCE CODE

Available at: v.vect.stats source code (history)

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