bionic (1) v.qcount.1grass.gz

NAME

       v.qcount  - Indices for quadrat counts of vector point lists.

KEYWORDS

       vector, statistics, point pattern

SYNOPSIS

       v.qcount
       v.qcount --help
       v.qcount  [-g]  input=name  [layer=string]   [output=name]  nquadrats=integer radius=float  [--overwrite]
       [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -g
           Print results in shell script style

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

       layer=string
           Layer number or name (’-1’ for all layers)
           A single vector map can be connected to multiple database tables. This number determines which  table
           to use. When used with direct OGR access this is the layer name.
           Default: -1

       output=name
           Name for output quadrat centers map (number of points is written as category)

       nquadrats=integer [required]
           Number of quadrats

       radius=float [required]
           Quadrat radius

DESCRIPTION

       v.qcount  computes  six  different  quadrat  count  statistics that provide a measure of how much an user
       defined point pattern departs from a complete spatial random point pattern.

       Points are distributed following a complete spatial randomness (CSR) pattern if events are equally likely
       to  occur  anywhere  within an area. There are two types departure from a CSR: regularity and clustering.
       Figure 1 gives an example of a complete random, regular and a clustered pattern.
       Figure 1: Realization of two-dimensional Poisson processes of 50 points on the unit square exhibiting (a)
       complete spatial randomness, (b) regularity, and (c) clustering.

       Various indices and statistics measure departure from CSR. The v.qcount function implements six different
       quadrat count indices that are described in Cressie  (1991;  p.  590-591)[1]  and  in  Ripley  (1981;  p.
       102-106)[2] and summarized in Table 1.
       Table  1:  Indices  for  Quadrat  Count  Data.  Adapted from Cressie [1], this table shows the statistics
       computed for the quadrats in Figure 2.

       These indices are computed as follows: v.qcount chooses nquadrads circular quadrats of radius radius such
       that they are completely within the bounds of the current region and no two quadrats overlap.  The number
       of points falling within each quadrat are counted and indices are calculated to estimate the departure of
       point locations from complete spatial randomness. This is illustrated in Figure 2.
       Figure 2: Randomly placed quadrats (n = 100) with 584 sample points.

NOTES

       This  program  may  not  work  properly  with  lat-long  data.  It uses hypot() in two files: count.c and
       findquads.c.

SEE ALSO

       v.random

REFERENCES

       General references include:

       [1] Noel A. C. Cressie. Statistics for Spatial  Data.   Wiley  Series  in  Probability  and  Mathematical
       Statistics. John Wiley & Sons, New York, NY, 1st edition, 1991.

       [2] Brian D. Ripley. Spatial Statistics.  John Wiley \& Sons, New York, NY, 1981.

       References to the indices include:

       [3]  R. A. Fisher, H. G. Thornton, and W. A. Mackenzie.  The accuracy of the plating method of estimating
       the density of bacterial populations.  Annals of Applied Biology, 9:325-359, 1922.

       [4] F. N. David and P. G. Moore. Notes on  contagious  distributions  in  plant  populations.  Annals  of
       Botany, 18:47-53, 1954.

       [5] J. B. Douglas.  Clustering and aggregation.  Sankhya B, 37:398-417, 1975.

       [6] M. Lloyd. Mean crowding.  Journal of Animal Ecology, 36:1-30, 1967.

       [7]  M.  Morista. Measuring the dispersion and analysis of distribution patterns. Memoires of the Faculty
       of Science, Kyushu University, Series E.  Biology, 2:215-235, 1959.

       A more detailed background is given in the tutorial:

       [8] James Darrell McCauley 1993. Complete Spatial Randomness and Quadrat  Methods  -  GRASS  Tutorial  on
       v.qcount

KNOWN ISSUES

       Timestamp not working for header part of counts output. (2000-10-28)

AUTHORS

       James Darrell McCauley
       when he was at: Agricultural Engineering Purdue University

       Modified for GRASS 5.0 by Eric G. Miller (2000-10-28)
       Modified for GRASS 5.7 by R. Blazek (2004-10-14)

       Last changed: $Date: 2016-12-22 15:40:49 +0100 (Thu, 22 Dec 2016) $

SOURCE CODE

       Available at: v.qcount source code (history)

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       © 2003-2018 GRASS Development Team, GRASS GIS 7.4.0 Reference Manual