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https://github.com/nowosad/acspatial

Algorithmic Complexity of Spatial Data
https://github.com/nowosad/acspatial

r r-package rspatial rstats spatial

Last synced: 3 months ago
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Algorithmic Complexity of Spatial Data

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README

        

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```

# acspatial

[![R-CMD-check](https://github.com/Nowosad/spatcomplexity/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/Nowosad/spatcomplexity/actions/workflows/R-CMD-check.yaml)

The goal of **acspatial** is to provide a function to calculate the layered block decomposition method (layered BDM) for spatial data.
The layered BDM is a method to estimate the algorithmic complexity of spatial data, a measure of the amount of information in a data set.
The algorithmic complexity is calculated using the Kolmogorov complexity, which is the length of the shortest computer program that can generate the provided data set.

## Installation

You can install the development version of **acspatial** from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("Nowosad/acspatial")
```

## Examples

```{r}
library(acspatial)
# 1
data(mini_array)
mini_array
layered_bdm(mini_array)

# 2
mini_array2 = matrix(1, nrow = 8, ncol = 8)
mini_array2
layered_bdm(mini_array2)
```

## References

- Rueda-Toicen, Antonio, Image Analysis with Estimations of Kolmogorov Complexity, (2018), GitHub repository, https://github.com/andandandand/ImageAnalysisWithAlgorithmicInformation, DOI: /10.5281/zenodo.1291510
- Hector Zenil, Santiago Hernández-Orozco, Narsis A.Kiani, Fernando Soler-Toscano, Antonio Rueda-Toicen, and Jesper Tegner "A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity", https://arxiv.org/abs/1609.00110
- https://www.complexity-calculator.com/