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https://github.com/nowosad/sabre

sabre: Spatial Association Between REgionalizations
https://github.com/nowosad/sabre

entropy polygons r regionalizations spatial spatial-analysis

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sabre: Spatial Association Between REgionalizations

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README 

        
---

output: github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# sabre 



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[![codecov](https://app.codecov.io/gh/Nowosad/sabre/branch/master/graph/badge.svg)](https://app.codecov.io/gh/Nowosad/sabre)

[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/sabre)](https://cran.r-project.org/package=sabre)



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`, `RasterLayer`, `SpatRaster`, and `stars` 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](https://cran.r-project.org/package=sabre) with:



``` r

install.packages("sabre")

```



You can install the development version from [GitHub](https://github.com/nowosad/sabre) with:



``` r

# 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. 



```{r, message=FALSE, warning=FALSE}

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.



```{r, echo=FALSE, fig.height=5, out.width="50%"}

plot(regions1, main = "regions1")

plot(regions2, main = "regions2")

```



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



```{r}

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 - `V-measure`, `Homogeneity`, `Completeness` - and two `sf` objects with preprocessed input maps - `$map1` and `$map2`.



```{r}

regions_vm

```



Both spatial outputs have two columns. 

The first one contains regions' names/values and the second one (`rih`) describes regions' inhomogeneities.



```{r, eval=FALSE}

plot(regions_vm$map1["rih"], main = "Map1: rih")

plot(regions_vm$map2["rih"], main = "Map2: rih")

```



```{r, warning=FALSE, fig.height=2, fig.height=5, out.width="50%", echo=FALSE}

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](https://jakubnowosad.com/sabre/articles/sabre.html) and in [the sabre: or how to compare two maps? blog post](https://jakubnowosad.com/posts/2018-09-10-sabre-bp/).



## Logo



Hex logo was created with [hexmake](https://connect.thinkr.fr/hexmake/) using icons made by Smashicons and Creaticca Creative Agency from www.flaticon.com.



## 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.