sabre
Spatial Association Between Regionalizations
Calculates a degree of spatial association between regionalizations
or categorical maps using the information-theoretical V-measure
(Nowosad and Stepinski (2018)
The sabre (Spatial Association Between
REgionalizations) is an R package for calculating a degree of
spatial association between regionalizations or categorical maps. This
package offers support for sf spatial objects, and the following
methods:
- the V-measure method (Nowosad and Stepinski, 2018)
- the MapCurve method (Hargrove et al., 2006)
Installation
You can install the released version of sabre from
CRAN with:
install.packages("sabre")
You can install the development version from GitHub with:
# install.packages("devtools")devtools::install_github("Nowosad/sabre")
Example
We use two simple regionalization, regions1 and regions2 to show the
basic concept of calculating a degree of spatial association.
library(sabre)library(sf)data("regions1")data("regions2")
The first map, regions1 consists of four regions of the same shape and
size, while the second one, regions2 has three irregular
regions.
The vmeasure_calc() function allows for calculation of a degree of
spatial association between regionalizations or categorical maps using
the information-theoretical V-measure. It requires, at least, four
arguments:
x- ansfobject containing the first regionalizationx_name- a name of the column with regions names of the first regionalizationy- ansfobject containing the second regionalizationy_name- a name of the column with regions names of the second regionalization
regions_vm = vmeasure_calc(regions1, z, regions2, z)
The result is a list with three metrics of spatial association -
V-measure, Homogeneity, Completeness - and two sf objects with
preprocessed input maps - $map1 and $map2.
regions_vm#> The SABRE results:#>#> V-measure: 0.36#> Homogeneity: 0.32#> Completeness: 0.42#>#> The spatial objects could be retrived with:#> $map1 - the first map#> $map2 - the second map
Both spatial outputs have two columns. The first one contains regions’
names/values and the second one (rih) describes regions’
inhomogeneities.
plot(regions_vm$map1["rih"], main = "Map1: rih")plot(regions_vm$map2["rih"], main = "Map2: rih")
More examples can be found in the package vignette and in the sabre: or how to compare two maps? blog post.
Additionally, examples presented in the Spatial association between regionalizations using the information-theoretical V-measure article can be reproduced using data available at http://sil.uc.edu/cms/index.php?id=data-1#vmeasure.
References
- Nowosad, Jakub, and Tomasz F. Stepinski. “Spatial association between regionalizations using the information-theoretical V-measure.” International Journal of Geographical Information Science (2018). https://doi.org/10.1080/13658816.2018.1511794
- Rosenberg, Andrew, and Julia Hirschberg. “V-measure: A conditional entropy-based external cluster evaluation measure.” Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL). 2007.
- Hargrove, William W., Forrest M. Hoffman, and Paul F. Hessburg. “Mapcurves: a quantitative method for comparing categorical maps.” Journal of Geographical Systems 8.2 (2006): 187.