Title: Ising Model for Spatial Data
Version: 0.6.0
Description: Performs simulations of binary spatial raster data using the Ising model (Ising (1925) <doi:10.1007/BF02980577>; Onsager (1944) <doi:10.1103/PhysRev.65.117>). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: comat, Rcpp, terra
URL: https://github.com/Nowosad/spatialising
BugReports: https://github.com/Nowosad/spatialising/issues
Suggests: covr, knitr, rmarkdown, optimization, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Depends: R (≥ 2.10)
LazyData: false
LinkingTo: Rcpp
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2023-11-26 20:07:45 UTC; jn
Author: Jakub Nowosad ORCID iD [aut, cre]
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>
Repository: CRAN
Date/Publication: 2023-11-27 17:10:02 UTC

spatialising: Ising Model for Spatial Data

Description

Performs simulations of binary spatial raster data using the Ising model (Ising (1925) doi:10.1007/BF02980577; Onsager (1944) doi:10.1103/PhysRev.65.117). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) doi:10.1098/rsos.231005).

Author(s)

Maintainer: Jakub Nowosad nowosad.jakub@gmail.com (ORCID)

See Also

Useful links:

Composition imbalance index

Description

Calculates composition imbalance index (also known as the m index) – a sum of cell’s values over the entire site divided by the number of cell in the site. m has a range from -1 (site completely dominated by the -1 values) to 1 (site completely dominated by the 1 values).

Usage

composition_index(x)

Arguments

x

SpatRaster or matrix containing two values: -1 and 1

Value

A numeric vector

See Also

kinetic_ising()

Examples

data(r_start, package = "spatialising")
composition_index(r_start)
ts1 = kinetic_ising(r_start, B = -0.3, J = 0.7)
composition_index(ts1)
ts2 = kinetic_ising(r_start, B = -0.3, J = 0.7, updates = 2)
composition_index(ts2)


  library(terra)
  r1 = rast(system.file("raster/r_start.tif", package = "spatialising"))
  composition_index(r1)
  r2 = kinetic_ising(r1, B = -0.3, J = 0.7)
  composition_index(r2)

Ising model for spatial data

Description

Performs simulations based on the given parameters of the Ising model

Usage

kinetic_ising(
  x,
  B,
  J,
  updates = 1,
  iter,
  rule = "glauber",
  inertia = 0,
  version = 1,
  progress = FALSE
)

Arguments

x

SpatRaster or matrix containing two values: -1 and 1

B

External pressure (positive or negative): it tries to align cells' values with its sign

J

Strength of the local autocorrelation tendency (always positive): it tries to align signs of neighboring cells

updates

Specifies how many sets of iterations are performed on the input object. The output of this function has as many layers as the updates value.

iter

Specifies how many iterations are performed on the input object. By default it equals to the number of values in the input object.

rule

IM temporal evolution rule: either "glauber" (default) or "metropolis"

inertia

Represents the modification of the algorithm aimed at suppressing the salt-and-pepper noise of the focus category present when simulating evolution of a coarse-textured pattern. With Q > 0, small patches of the focus category are not generated, thus eliminating the salt-and-pepper noise of the focus category

version

By default, 1, the x object is converted into a matrix (fast, but can be memory consuming); version = 2 has a lower RAM impact, but is much slower

progress

TRUE/FALSE

Value

Object of the same class as x with the number of layers specified by updates

References

Ising, E., 1924. Beitrag zur theorie des ferro-und paramagnetismus. Ph.D. thesis, Grefe & Tiedemann.

Onsager, L., 1944. Crystal statistics. I. A two-dimensional model with an order-disorder transition. Physical Review 65 (3-4), 117.

Brush, S. G., 1967. History of the Lenz-Ising model. Reviews of modern physics 39 (4), 883.

Cipra, B. A., 1987. An introduction to the Ising model. The American Mathematical Monthly 94 (10), 937–959.

Examples

data(r_start, package = "spatialising")
ts1 = kinetic_ising(r_start, B = -0.3, J = 0.7)
ts10 = kinetic_ising(r_start, B = -0.3, J = 0.7, updates = 10)


  r1 = terra::rast(system.file("raster/r_start.tif", package = "spatialising"))
  terra::plot(r1)
  r2 = kinetic_ising(r1, B = -0.3, J = 0.7)
  terra::plot(r2)

  library(terra)
  ri1 = kinetic_ising(r1, B = -0.3, J = 0.7, updates = 9)
  plot(ri1)

  ri2 = kinetic_ising(r1, B = 0.3, J = 0.7, updates = 9)
  plot(ri2)

  ri3 = kinetic_ising(r1, B = -0.3, J = 0.4, updates = 9)
  plot(ri3)

Ensemble of Ising models for spatial data

Description

Creates an ensemble of simulations based on the given parameters of the Ising model

Usage

kinetic_ising_ensemble(runs, ...)

Arguments

runs

A number of simulations to perform

...

Arguments for kinetic_ising()

Value

A list of objects of the same class as x

See Also

kinetic_ising(), kinetic_ising_exemplar()

Examples

data(r_start, package = "spatialising")
l = kinetic_ising_ensemble(100, r_start, B = -0.3, J = 0.7)


  library(terra)
  r1 = rast(system.file("raster/r_start.tif", package = "spatialising"))
  plot(r1)
  r2 = kinetic_ising_ensemble(100, r1, B = -0.3, J = 0.7)

Exemplar of an Ising model for spatial data

Description

Creates an ensemble of simulations based on the given parameters of the Ising model and selects an exemplar (a model that is closest to the average of the ensemble)

Usage

kinetic_ising_exemplar(runs, ...)

Arguments

runs

A number of simulations to perform

...

Arguments for kinetic_ising()

Value

Object of the same class as x

See Also

kinetic_ising(), kinetic_ising_ensemble()

Examples

data(r_start, package = "spatialising")
l = kinetic_ising_exemplar(100, r_start, B = -0.3, J = 0.7)


  library(terra)
  r1 = rast(system.file("raster/r_start.tif", package = "spatialising"))
  plot(r1)
  r2 = kinetic_ising_exemplar(100, r1, B = -0.3, J = 0.7)
  plot(r2)

An example matrix object

Description

A matrix has 50 columns and 50 rows. The matrix contains two values: -1 and 1. This object was created with r_end = kinetic_ising(r_start, B = -0.3, J = 0.7)

Usage

data(r_end)

Format

A matrix

An example matrix object

Description

A matrix has 50 columns and 50 rows. The matrix contains two values: -1 and 1.

Usage

data(r_start)

Format

A matrix

An example binary raster

Description

A raster file covering an area of 50x50 cells. The raster file contains two values: -1 and 1. system.file("raster/r_start.tif", package = "spatialising")

Format

A raster file

Texture index

Description

Calculates texture index – an average (over an array) of a product of the values of neighboring cells. The value of texture index is between 0 (fine texture), and 1 (coarse texture).

Usage

texture_index(x, ...)

Arguments

x

SpatRaster or matrix containing two values: -1 and 1

...

Arguments for comat::get_coma()

Value

A numeric vector

See Also

kinetic_ising()

Examples

data(r_start, package = "spatialising")
texture_index(r_start)
ts1 = kinetic_ising(r_start, B = -0.3, J = 0.7)
texture_index(ts1)
ts2 = kinetic_ising(r_start, B = -0.3, J = 0.7, updates = 2)
texture_index(ts2)


  library(terra)
  r1 = rast(system.file("raster/r_start.tif", package = "spatialising"))
  texture_index(r1)
  r2 = kinetic_ising(r1, B = -0.3, J = 0.7)
  texture_index(r2)