Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
R
 
 
 
 
 
 
doc
 
 
 
 
man
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Overview

sppt is an R package that implements several Spatial Point Pattern Tests. This package started with Martin Andresen’s original ‘sppt’ that is published here and elsewhere.

The Spatial Point Pattern Tests in this package measure the degree of similarity at the local level between two spatial pointpatterns and are area-based tests.

These tests are not for the purpose of testing point patterns with the null hypotheses of random, uniform, or clustered distributions, but may be used to compare a particular point pattern with these distributions. One advantage is that it can be performed for a number of different area boundaries using the same original point datasets.

Version

The most recent version of the package is:

Steenbeek, W., Vandeviver, C. Andresen, M.A., Malleson, N., Wheeler, A. sppt: Spatial Point Pattern Test. R package version 0.2.3. URL: https://github.com/wsteenbeek/sppt

An archive of older versions of the package can be found here: https://github.com/wsteenbeek/sppt-archive.

Installation

You can install the package from this GitHub repository. You first need to install the remotes package.

install.packages("remotes")

Then install sppt using the install_github function in the remotes package.

remotes::install_github("wsteenbeek/sppt")

Example

Spatial objects areas.sp, points1.sp, and points2.sp, are included in the package. For example, you can think of these as neighborhoods and the locations where crimes occur in two different years.

library(sppt)
plot(areas.sp)
points(points1.sp, col="blue", pch = 19)
points(points2.sp, col="red", pch = 15)

The original function within the sppt package is also called sppt:

set.seed(93255) # set seed for reproducibility
output <- sppt(points1.sp, points2.sp, areas.sp)

Two other functions were added in February 2018. sppt_boot():

set.seed(93255) # set seed for reproducibility
output2 <- sppt_boot(points1.sp, points2.sp, areas.sp)

and sppt_diff():

set.seed(93255) # set seed for reproducibility
output3 <- sppt_diff(points1.sp, points2.sp, areas.sp)

You can see the results of the test by inspecting the SpatialPolygonsDataFrame, for example:

output@data
#>   ID uoa_id SIndex NumBsePts NumTstPts PctBsePts PctTstPts SumBseTstPts
#> 0  1      1      0         2         2      25.0  22.22222            4
#> 1  4      2      1         2         1      25.0  11.11111            3
#> 2  5      3      0         2         2      25.0  22.22222            4
#> 3  6      4      0         1         2      12.5  22.22222            3
#> 4 11      5      0         0         1       0.0  11.11111            1
#> 5 15      6      0         1         1      12.5  11.11111            2
#>   ConfLowP ConfUppP localS similarity   globalS SIndex.robust localS.robust
#> 0     12.5     25.0      0          1 0.8333333             0             0
#> 1      0.0     12.5     -1          0 0.8333333             1            -1
#> 2     12.5     25.0      0          1 0.8333333             0             0
#> 3     12.5     25.0      0          1 0.8333333             0             0
#> 4      0.0     12.5      0          1 0.8333333             0             0
#> 5      0.0     12.5      0          1 0.8333333             0             0
#>   similarity.robust globalS.robust
#> 0                 1      0.8333333
#> 1                 0      0.8333333
#> 2                 1      0.8333333
#> 3                 1      0.8333333
#> 4                 1      0.8333333
#> 5                 1      0.8333333

The global S-values can be outputted directly using summary_sppt():

summary_sppt(output)
#> $globalS.standard
#> [1] 0.8333333
#> 
#> $globalS.robust
#> [1] 0.8333333

Vignettes

The package includes vignettes explaining the sppt procedure in more detail; a worked example of a toy dataset and actual crime data; a comparison between this R package and an existing Java application that has been written by Nick Malleson; and the new functions sppt_boot() and sppt_diff().

By far the easiest way to view the vignettes are these direct links, courtesy of the GitHub HTML Preview service:

  1. Introduction to Spatial Point Pattern Test

  2. Comparing R vs Java

  3. Proportion difference tests

If instead you want to access the vignettes from R itself you need to take a few additional steps, because remotes::install_github() does not build vignettes by default to save time and because it may require additional packages.

  1. Install the rmarkdown package with install.packages("rmarkdown")

  2. Install pandoc (and afterwards restart your computer)

  3. Then, install the package again but force building of the vignettes using remotes::install_github("wsteenbeek/sppt", build_vignettes = TRUE, force = TRUE). This will take a few minutes.

Afterwards, you should be able to view which vignettes are available using:

browseVignettes("sppt")

To directly read the vignettes rather than going through browseVignettes("sppt") you can use:

vignette("sppt_intro", package = "sppt")
vignette("sppt_comparison", package = "sppt")
vignette("sppt_diff", package = "sppt")

License

This package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License, version 3, as published by the Free Software Foundation.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.

A copy of the GNU General Public License, version 3, is available at https://www.r-project.org/Licenses/GPL-3

About

An R package to run Spatial Point Pattern Tests

Resources

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.