The **sabre** (**S**patial **A**ssociation **B**etween **RE**gionalizations) 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)

You can install the released version of `sabre`

from CRAN with:

You can install the development version from GitHub with:

We use two simple regionalization, `regions1`

and `regions2`

to show the basic concept of calculating a degree of spatial association.

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`

- an`sf`

object containing the first regionalization`x_name`

- a name of the column with regions names of the first regionalization`y`

- an`sf`

object containing the second regionalization`y_name`

- a name of the column with regions names of the second regionalization

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.

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.

- 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.