## 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 detmined 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 existing. If they do already exist, the count_column must be of type integer and the stats_column of type double precision.

## 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.distance, r.distance, v.rast.stats, v.what.vect, v.mkgrid

## AUTHOR

Markus Metz

Last changed: $Date: 2016-08-23 04:00:20 +0200 (Tue, 23 Aug 2016) $