NAME
v.nnstat - Indicates clusters, separations or random distribution of point set in 2D or 3D space.KEYWORDS
vector, nearest neighbour analysisSYNOPSIS
v.nnstat
v.nnstat --helpv.nnstat [-2] input=name [area=float] [layer=string] [zcolumn=name] [--help] [--verbose] [--quiet] [--ui]
Flags:
- -2
- Force 2D NNA even if input is 3D
- --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
- area=float
- 2D: Area. If not specified, area of Minimum Enclosing Rectangle will be used.
3D: Volume. If not specified, volume of Minimum Enclosing Box will be used. - 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
- zcolumn=name
- Column with z coordinate (set for 2D vectors only if 3D NNA is required to be performed)
Table of contents
DESCRIPTION
v.nnstat indicates clusters, separations or random distribution of point dataset in 2D or 3D space using Nearest Neighbour Analysis (NNA). The method is based on comparison of observed average distance between the nearest neighbours and the distance which would be expected if points in the dataset are distributed randomly. More detailed information about theoretical background is provided in (Clark and Evans, 1954), (Chandrasekhar, 1943, p. 86-87). Details about the module and testing are summarized in (Stopkova, 2013).EXAMPLES
Comparison of 2D and 3D NNA
On the example of dataset that contains 2000 randomly distributed points, basic settings of analysis dimension (2D or 3D) will be examined:- 2D NNA may be performed using 2D vector layer. If 2D NNA is required to be
performed using 3D vector layer, flag -2 should be marked. The results of
both cases can be seen below.
Output in the command line:
v.nnstat input=rand_2000_2d
Input coordinates have been read... Computing average distance between nearest neighbors... 100% *** Nearest Neighbour Analysis results *** Input settings .. 3D layer: 0 3D NNA: 0 Number of points .......... 2000 Area ...................... 398645718.651701 [units^2] Density of points ......... 0.000005 Average distance between the nearest neighbours ........... 225.859 [units] Average expected distance between the nearest neighbours .. 223.228 [units] Ratio rA/rE ............... 1.011785 *** Results of two-tailed test of the mean *** Null hypothesis: Point set is randomly distributed within the region. Standard variate of the normal curve> c = 1.008239 Null hypothesis IS NOT REJECTED at the significance level alpha = 0.05
Output in the command line:v.nnstat input=rand_2000_3d -2
Input coordinates have been read... Computing average distance between nearest neighbors... 100% *** Nearest Neighbour Analysis results *** Input settings .. 3D layer: 1 3D NNA: 0 Number of points .......... 2000 Area ...................... 398645718.651701 [units^2] Density of points ......... 0.000005 Average distance between the nearest neighbours ........... 225.859 [units] Average expected distance between the nearest neighbours .. 223.228 [units] Ratio rA/rE ............... 1.011785 *** Results of two-tailed test of the mean *** Null hypothesis: Point set is randomly distributed within the region. Standard variate of the normal curve> c = 1.008239 Null hypothesis IS NOT REJECTED at the significance level alpha = 0.05
NOTE: Comparing the results of 2D NNA with results summarized in (Stopkova, 2013), there can be seen small difference between the values of area. It is assumed to be caused by differences in transformed coordinates of the convex hull that have been computed using two versions of the module.
- 3D NNA can be performed just using 3D vector layer. If 3D NNA is required to be performed using
2D vector layer, name of the column in attribute table that contains elevation values must be set.
The results of both cases can be seen below.
Output in the command line:
v.nnstat input=rand_2000_3d
Input coordinates have been read... Computing average distance between nearest neighbors... 100% Reading 3D vertices... 100% Constructing 3D hull... 99% *** Nearest Neighbour Analysis results *** Input settings .. 3D layer: 1 3D NNA: 1 Number of points .......... 2000 Volume .................... 398423031180.489197 [units^3] Density of points ......... 0.000000 Average distance between the nearest neighbours ........... 346.072 [units] Average expected distance between the nearest neighbours .. 323.531 [units] Ratio rA/rE ............... 1.069670 *** Results of two-tailed test of the mean *** Null hypothesis: Point set is randomly distributed within the region. Standard variate of the normal curve> c = 0.191691 Null hypothesis IS NOT REJECTED at the significance level alpha = 0.05
Output in the command line:v.nnstat input=rand_2000_2d zcolumn=z
Reading elevations from attribute table: 2000 records selected Input coordinates have been read... Computing average distance between nearest neighbors... 100% Reading 3D vertices... 100% Constructing 3D hull... 99% *** Nearest Neighbour Analysis results *** Input settings .. 3D layer: 0 .. 3D NNA: 1 .. zcolumn: z Number of points .......... 2000 Volume .................... 398423031180.489197 [units^3] Density of points ......... 0.000000 Average distance between the nearest neighbours ........... 346.072 [units] Average expected distance between the nearest neighbours .. 323.531 [units] Ratio rA/rE ............... 1.069670 *** Results of two-tailed test of the mean *** Null hypothesis: Point set is randomly distributed within the region. Standard variate of the normal curve> c = 0.191691 Null hypothesis IS NOT REJECTED at the significance level alpha = 0.05
- Warning: If flag -2 is set up together with zcolumn, the flag will have higher priority and 2D NNA will be performed.
Comparison of various datasets
In (Stopkova, 2013), there might be seen other examples (also clustered and dispersed datasets).
TODO
- add graphical output
SEE ALSO
v.hullREFERENCES:
Stopkova, 2013: Extension of mathematical background for Nearest Neighbour Analysis in three-dimensional space, https://doi.org/10.14311/gi.11.2, https://ojs.cvut.cz/ojs/index.php/gi/article/view/gi.11.2/2396REQUIREMENTS
- LAPACK / BLAS (libraries for numerical computing) for
GMATH library (GRASS Numerical Library)
https://www.netlib.org/lapack (usually available on Linux distros)
AUTHOR
Eva Stopkovafunctions for computation of Minimum Bounding Box volume (Minimum Bounding Rectangle area) are based on functions for computing convex hull from the module v.hull (Aime, A., Neteler, M., Ducke, B., Landa, M.)
SOURCE CODE
Available at: v.nnstat source code (history)
Latest change: Monday Nov 11 18:04:48 2024 in commit: 59e289fdb093de6dd98d5827973e41128196887d
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