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)

Two simple regionalizations would be used to show the basic concept
of **sabre**.

```
library(sabre)
library(sf)
data("regions1")
data("regions2")
```

The first map, `regions1`

, has four regions (“r1”, “r2”,
“r3”, “r4”) of the same size and shape. The second one,
`regions2`

, contains three irregular regions where “z1” is
the smallest and “z3” being the largest. Our goal is to compare these
two regionalizations and calculate a degree of spatial association
between them.

It can be done with `vmeasure_calc()`

, which calculates
“V-measure”, “Homogeneity”, and “Completeness” and returns two
preprocessed input maps. This function requires, at least, four
arguments:

`x`

- an`sf`

object containing the first regionalization`y`

- an`sf`

object containing the second regionalization`x_name`

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

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

Importantly, both `x`

and `y`

must contain
`POLYGON`

s or `MULTIPOLYGON`

s and have the same
coordinate reference system.

There are also two additional arguments - `B`

and
`precision`

. If `B`

> 1 then completeness is
weighted more strongly than homogeneity, and if `B`

< 1
then homogeneity is weighted more strongly than completeness. By default
this value is 1. The `vmeasure_calc()`

function calculates
intersections of the input geometries internally using
`sf::st_intersection()`

, which depends on the coordinates
values precision. The `precision`

argument can be used when
`vmeasure_calc`

produces an error. For example,
`precision = 1000`

rounds values to the third decimal places
and `precision = 0.001`

uses values rounded to the nearest
1000.

```
= vmeasure_calc(x = regions1, y = regions2, x_name = z, y_name = z)
regions_vm
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
```

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`

. All
of the above metrics are between 0 and 1, where larger values are
desired. `V-measure`

is a measure of an overall spatial
correspondence between input maps. `Homogeneity`

shows an
average homogeneity of the regions in the second map with respect to the
regions in the first map. `Completeness`

is a function of
homogeneity of the regions in the first map with respect to the regions
in the second map. The spatial outputs, `$map1`

and
`$map2`

, have two columns. The first one contains regions’
names and the second one (`rih`

) describes regions’
inhomogeneities. Geometries of these spatial outputs show intersections
of the two input regionalizations.

For example, “Map1” shows that three regions have the same inhomogeneity of 0.48. This is due a fact that all of these three have two regions from the second map. The upper left region has a larger inhomogeneity of 0.86 as its area “belongs” to three different regions in the second map. More information about this method and its applications can be found in Nowosad and Stepinski (2018).

The **sabre** also allows for calculating a degree of
spatial association between regionalizations using the MapCurve method
(Hargrove et al., 2006). The `mapcurves_calc()`

function also
requires four arguments, `x`

, `x_name`

,
`y`

, and `y_name`

. It also accepts an additional
argument - `precision`

. All of these arguments are explained
in the previous section.

```
= mapcurves_calc(x = regions1, y = regions2, x_name = z, y_name = z)
regions_mc
regions_mc#> The MapCurves results:
#>
#> The goodness of fit: 0.61
#> Reference map: x
#>
#> The spatial objects can be retrieved with:
#> $map1 - the first map
#> $map2 - the second map
```

The `mapcurves_calc()`

returns a list with a value of the
goodness of fit (GOF), the map used as a reference, and two
`sf`

objects with preprocessed input maps -
`$map1`

and `$map2`

. Read Hargrove et al. (2006)
to learn more about this method.

- 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.