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v.qcount(1grass)            GRASS GIS User's Manual           v.qcount(1grass)

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=in-
       teger 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 com-
       plete 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) reg-
       ularity, and (c) clustering.

       Various indices and statistics measure departure from CSR. The v.qcount
       function  implements  six  different quadrat count indices that are de-
       scribed 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  cir-
       cular  quadrats  of  radius radius such that they are completely within
       the bounds of the current region and no two quadrats overlap.  The num-
       ber  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.

       The  number of points is written as category to the output map (and not
       to an attribute table).

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, v.distance, v.neighbors, v.perturb

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)

SOURCE CODE
       Available at: v.qcount source code (history)

       Accessed: unknown

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       © 2003-2022 GRASS Development Team, GRASS GIS 7.8.7 Reference Manual

GRASS 7.8.7                                                   v.qcount(1grass)

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