README.md

In alsarna/declusteringr: Spatial Data Declustering

declusteringr

The goal of declusteringr is to declustering spatial data. This package offers support for the sf spatial objects.

Installation

You can install the development version of declusteringr with:

# install.packages("remotes")
remotes::install_github("alsarna/declusteringr")

Examples

Let’s start with reading the data:

library(sf)
#> Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3
points4 = sf::st_read(system.file("points/punkty4.shp", package = "declusteringr"))
#> Reading layer `punkty4' from data source `C:\Users\Lenovo\Documents\R\win-library\3.6\declusteringr\points\punkty4.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 200 features and 1 field
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: -1775590 ymin: -5316527 xmax: -1642585 ymax: -5237789
#> epsg (SRID):    NA
#> proj4string:    +proj=tmerc +lat_0=0 +lon_0=19 +k=0.9993 +x_0=500000 +y_0=-5300000 +ellps=GRS80 +units=m +no_defs

Cell declustering

This is a basic example of cell declustering:

library(declusteringr)
x = declstr_weighted(spatial_object = points4, cellsize = 10000)
x
#> Simple feature collection with 200 features and 3 fields
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: -1775590 ymin: -5316527 xmax: -1642585 ymax: -5237789
#> epsg (SRID):    NA
#> proj4string:    +proj=tmerc +lat_0=0 +lon_0=19 +k=0.9993 +x_0=500000 +y_0=-5300000 +ellps=GRS80 +units=m +no_defs
#> First 10 features:
#>     id received_weights lengths                  geometry
#> 172  0        13.333333       1 POINT (-1652995 -5254057)
#> 121  1         3.333333       4 POINT (-1655337 -5267243)
#> 139  2         3.333333       4 POINT (-1711804 -5262422)
#> 3    3         6.666667       2 POINT (-1748079 -5310699)
#> 187  4         6.666667       2 POINT (-1724664 -5244599)
#> 140  5         3.333333       4 POINT (-1712603 -5260028)
#> 179  6         6.666667       2 POINT (-1748891 -5241387)
#> 99   7         3.333333       4 POINT (-1767435 -5271677)
#> 150  8         6.666667       2 POINT (-1670705 -5260691)
#> 41   9         4.444444       3 POINT (-1649904 -5302388)

Plot input data and weighted points:

par(mfrow = c(1, 2))
plot(st_geometry(points4), main = "original data")
plot(x["received_weights"], main = "cell declustering",
     key.pos = NULL, reset = FALSE)

declstr_weighted() is adding the following columns: received_weights - the weight of each cell, and lengths - a number of points in cell. declstr_weighted() does not change the number of features. Weights are calculated according to the equation:

, where ni is the number of samples in the cell in which sample j is located and n is the total number cells with samples.

Random declustering

This is basic example of random declustering:

y = declstr_random(spatial_object = points4, cellsize = 10000, numpoint = 2)
y
#> Simple feature collection with 159 features and 1 field
#> geometry type:  POINT
#> dimension:      XY
#> bbox:           xmin: -1775590 ymin: -5316527 xmax: -1642585 ymax: -5237910
#> epsg (SRID):    NA
#> proj4string:    +proj=tmerc +lat_0=0 +lon_0=19 +k=0.9993 +x_0=500000 +y_0=-5300000 +ellps=GRS80 +units=m +no_defs
#> First 10 features:
#>      id                  geometry
#> 50   49 POINT (-1757793 -5311478)
#> 68   67 POINT (-1764673 -5310663)
#> 4     3 POINT (-1748079 -5310699)
#> 177 176 POINT (-1748461 -5307808)
#> 56   55 POINT (-1745143 -5315420)
#> 64   63 POINT (-1733773 -5308707)
#> 16   15 POINT (-1725205 -5311317)
#> 59   58 POINT (-1705916 -5313721)
#> 28   27 POINT (-1708887 -5310775)
#> 184 183 POINT (-1704856 -5313243)

Plot input data and data after random declustering:

par(mfrow = c(1, 2))
plot(st_geometry(points4), main = "original data")
plot(y, main = "random declustering",
     key.pos = NULL, reset = FALSE)

declstr_random() is choosing random features by specifying some parameters. declstr_random() can change the number of features.

References

1. Declustering and Debiasing - M. J. Pyrcz ([e-mail]) and C. V. Deutsch ([e-mail]), Centre for Computational Geostatistics University of Alberta, Edmonton, Alberta, CANADA
2. Cell Declustering Parameter Selection - C. V. Deutsch
3. Nowosad, J., (2019). Geostatystyka w R. Poznań: Space A. ISBN 978-83-953296-0-9. Online: https://bookdown.org/nowosad/Geostatystyka/
4. Nowosad, J., (2019). Elementarz programisty: wstęp do programowania używając R. Poznań: Space A. Online: https://nowosad.github.io/elp/
5. Lovelace, Robin & Nowosad, Jakub & Muenchow, Jannes. (2019). Geocomputation with R. 10.1201/9780203730058.
6. R Packages, H. Wickham, J. Bryan. Online: https://r-pkgs.org/ (2015)

alsarna/declusteringr documentation built on Sept. 29, 2020, 11:59 p.m.