Neutral landscape models operators

`nlm.binarize`#

Habitat (2) and matrix (1) of a map (NLMR's util_binarize): the share of habitat given, the cells with the highest values. Several shares give a stack, a map each.

Input	Type	What it is
`map`required	map	the values (a neutral landscape)

Output	Type	What it is
`map`	any	1 matrix, 2 habitat: a map, or a stack by share

Setting	Type	What it is
`breaks`required	any	the share of habitat, in [0, 1], or several

`nlm.classify`#

Classes 1, 2... of a map (NLMR's util_classify): by the weights, each class's share of the cells (the cells sorted, the boundaries at the cumulative shares); by a real landscape's shares of its values; or in n classes by Jenks's natural breaks.

Input	Type	What it is
`map`required	map	the values (a neutral landscape)
`real`	map	a real landscape whose classes' shares to take

Output	Type	What it is
`classes`	map	the classes 1, 2...

Setting	Type	What it is
`n`	integer	the classes, by natural breaks
`weights`	any	each class's share of the cells (null for an even share of what the others leave)
`mask_value`	number	the real landscape's value to leave out

`nlm.curds`#

A hierarchical binary map (NLMR's nlm_curds, curdling): at each level every cell splits into steps x steps cells and a share of them becomes matrix (0), the rest habitat (1); with wheyes, a share of the matrix turns back to habitat (wheyed curdling). Its side is the product of the steps.

Output	Type	What it is
`map`	map	1 habitat, 0 matrix

Setting	Type	What it is
`curds`required	any	each level's share of matrix cells, e.g. [0.5, 0.3, 0.6]
`steps`required	any	each level's split, e.g. [32, 6, 2]
`wheyes`	any	each level's share of matrix turned to habitat
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it

`nlm.distance_gradient`#

The distance of every cell to a rectangle of cells (NLMR's nlm_distancegradient). As NLMR's (the default): terra's distance, on a lon/lat grid of 1 degree cells for maps up to 360 x 90 (metres on the sphere), else GDAL's proximity in cells; otherwise the exact distance between the cells' centres, in map units.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the distances

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`origin`required	any	the rectangle, 1-based and inclusive: NLMR's [row from, row to, column from, column to] (as its checks: the second at most the columns and the fourth at most the rows); without NLMR's, [column from, column to, row from, row to]
`nlmr_compat`	bool	NLMR's distances and origin (default true)

`nlm.edge_gradient`#

A gradient that peaks in the middle of the map and falls to its edges along a direction (NLMR's nlm_edgegradient): the planar gradient rescaled and folded around 0.5.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the gradient
`direction`	number	its direction, in degrees

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`direction`	number	in degrees, 0 north to 90 east (default: a uniform draw from 0 to 360 with the seed)

`nlm.fbm`#

A fractal surface from 0 to 1 (NLMR's nlm_fbm, fractional Brownian motion): rough for a small fractal dimension, smooth for a large one (2 is a plane).

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the surface, 0 to 1

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`dimension`	number	the fractal dimension, in (0, 2] (default 1)

`nlm.gaussian_field`#

A smooth random surface from 0 to 1 (NLMR's nlm_gaussianfield): a Gaussian field whose autocorrelation fades over the range given. A driver, a suitability or an elevation to try a model on; reclassified, a map of classes.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the field, 0 to 1

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`range`	number	the range of the autocorrelation, in cells (default 10)
`variance`	number	of the autocorrelated part (default 5)
`nugget`	number	the variance of the noise without structure (default 0.2)

`nlm.merge`#

Landscapes merged (NLMR's util_merge): the primary plus the sum of the secondary ones times the scaling, e.g. a gradient with noise on it.

Input	Type	What it is
`primary`required	map	the primary landscape
`secondary`required	any	a map or a stack of maps on its grid

Output	Type	What it is
`map`	map	the merged landscape

Setting	Type	What it is
`rescale`	bool	rescale the values to 0-1 (default true)
`scaling`	number	of the secondary landscapes (default 1)

`nlm.mosaic_field`#

A mosaic random field (NLMR's nlm_mosaicfield, spatstat's rMosaicField): the sum of n mosaics, each a Poisson line tessellation with a normal value per tile; optionally every step's sum and the infinite field (a log-Gaussian Cox process).

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the sum of the n mosaics
`steps`	stack	the sum after each step (`collect`)
`infinit`	map	the infinite field (`infinit`)

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`n`	integer	the mosaics (default 20; 0 for only the infinite field)
`mean`	number	of the tiles' values (default 0.5)
`sd`	number	of the tiles' values (default 0.5)
`collect`	bool	every step's sum too (default false)
`infinit`	bool	the infinite field too (default false)
`nlmr_compat`	bool	NLMR's quirks (default true): its steps rescaled with the first's range

`nlm.mosaic_gibbs`#

A Voronoi mosaic of classes from germs kept apart (NLMR's nlm_mosaicgibbs): spatstat's rSSI germs at least R apart, each with a class 1..patch_classes, every cell the class of its nearest germ. As NLMR (nlmr_compat, the default) the k-th class goes to the k-th Voronoi cell in the order GEOS lists them (GEOS 3.11 or later, next to GDAL); else each germ keeps its own class.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the mosaic

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`germs`required	integer	how many germs
`nlmr_compat`	bool	NLMR's assignment of the values to the Voronoi cells by GEOS's order (default true)
`r`required	number	the shortest distance between germs, in cells
`patch_classes`required	integer	the classes

`nlm.mosaic_tess`#

A Voronoi mosaic of random values (NLMR's nlm_mosaictess): random germs (sf's st_sample), a uniform value each, every cell the value of its nearest germ. As NLMR (nlmr_compat, the default) the k-th value goes to the k-th Voronoi cell in the order GEOS lists them (GEOS 3.11 or later, next to GDAL) and the values are floats, as terra::rasterize writes them; else each germ keeps its own value, in double precision.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the mosaic

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`germs`required	integer	how many germs
`nlmr_compat`	bool	NLMR's assignment of the values to the Voronoi cells by GEOS's order (default true)

`nlm.mpd`#

A fractal surface by midpoint displacement (NLMR's nlm_mpd, the diamond-square algorithm): each step halves the cells and displaces the midpoints by a deviation that shrinks with the roughness. NLMR's grid (the default) is the odd side above the larger dimension, less its border: it may differ from the size asked for (a warning says so).

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the surface

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`roughness`	number	in [0, 1]: how fast the deviation shrinks (0.5)
`deviation`	number	the first step's deviation (default 1)
`torus`	bool	wrap the edges around (default false)
`nlmr_compat`	bool	NLMR's grid (default true)

`nlm.neigh`#

A map of categories grown by neighbourhood (NLMR's nlm_neigh): cells are filled one at a time, with the category of their neighbours (probability p_neigh) or, around empty ones, at random (p_empty), until each category has its share. Categories 0, 1...

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the categories

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`p_neigh`required	number	the probability of a cell with filled neighbours, [0, 1]
`p_empty`required	number	the probability of a cell without, [0, 1]
`categories`	integer	how many (default 3)
`neighbourhood`	integer	4 (default) or 8 neighbours
`proportions`	any	each category's share of the cells, summing to 1 (default equal shares)

`nlm.percolation`#

A random binary map (NLMR's nlm_percolation): each cell is 1 with the probability given (a uniform draw below it, by column), else 0.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	1 and 0

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`prob`	number	the probability of a 1, in [0, 1] (default 0.5)

`nlm.planar_gradient`#

A linear gradient across the map along a direction (NLMR's nlm_planargradient): eastness times the column plus southness times the row.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the gradient
`direction`	number	its direction, in degrees

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`direction`	number	in degrees, 0 north to 90 east (default: a uniform draw from 0 to 360 with the seed)

`nlm.random`#

Spatially random values (NLMR's nlm_random): a uniform draw for every cell, by column as R fills a matrix. The null model of a landscape without structure.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the values (0 to 1)

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)

`nlm.random_cluster`#

A map of patches of classes 1, 2... (NLMR's nlm_randomcluster, Saura & Martínez-Millán 2000): a share p of the cells grows into clusters, each of a class drawn with the weights, and the rest takes its neighbours' class. A forest map to try MSPA or a land change model on. The weights give the shares only roughly: the clusters are classed as wholes, so with few large clusters (p above about 0.59, the percolation threshold, where one cluster spans the map) one class may take most of it.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the classes 1, 2...

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`p`	number	the share of cells in clusters, up to 1 (default 0.5)
`weights`	any	the classes' weights, their share of the clusters (default [0.5, 0.5]: two classes)
`neighbourhood`	integer	of a cluster's cells: 4 (default) or 8

`nlm.random_rectangular_cluster`#

Overlapping rectangles of random values (NLMR's nlm_randomrectangularcluster): rectangles with sides from minl to maxl cells, each of a uniform value, until the map is covered.

Input	Type	What it is
`like`	map	a map whose grid to draw on (else rows x columns)

Output	Type	What it is
`map`	map	the values

Setting	Type	What it is
`rows`	integer	rows (default 256)
`columns`	integer	columns (default 256)
`resolution`	number	the cells' side (default 1)
`seed`	integer	the seed of R's generator (default 1): NLMR's map for it
`rescale`	bool	rescale the values to 0-1 (default true)
`minl`required	integer	the shortest side, in cells
`maxl`required	integer	the longest, at most the rows and the columns

`nlm.rescale`#

A map rescaled to 0-1 (NLMR's util_rescale): (x - min) / (max - min), all 0 when the minimum and maximum are all.equal.

Input	Type	What it is
`map`required	map	the values

Output	Type	What it is
`map`	map	rescaled

nlm.binarize#

nlm.classify#

nlm.curds#

nlm.distance_gradient#

nlm.edge_gradient#

nlm.fbm#

nlm.gaussian_field#

nlm.merge#

nlm.mosaic_field#

nlm.mosaic_gibbs#

nlm.mosaic_tess#

nlm.mpd#

nlm.neigh#

nlm.percolation#

nlm.planar_gradient#

nlm.random#

nlm.random_cluster#

nlm.random_rectangular_cluster#

nlm.rescale#