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
stars spatial objects, and the following methods:
- the V-measure method (Nowosad and Stepinski, 2018)
- the MapCurve method (Hargrove et al., 2006)
You can install the released version of
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("Nowosad/sabre")
We use two simple regionalization,
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.
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
sfobject containing the first regionalization
x_name- a name of the column with regions names of the first regionalization
sfobject containing the second regionalization
y_name- a name of the column with regions names of the second regionalization
regions_vm = vmeasure_calc(x = regions1, y = regions2, x_name = z, y_name = z)
The result is a list with three metrics of spatial association -
Completeness - and two
sf objects with
preprocessed input maps -
regions_vm #> The SABRE results: #> #> V-measure: 0.36 #> Homogeneity: 0.32 #> Completeness: 0.42 #> #> The spatial objects can be retrieved with: #> $map1 - the first map #> $map2 - the second map
Both spatial outputs have two columns. The first one contains regions’
names/values and the second one (
rih) describes regions’
plot(regions_vm$map1["rih"], main = "Map1: rih") plot(regions_vm$map2["rih"], main = "Map2: rih")
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/index.php?id=data-1#vmeasure.
- 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.