R/generate-nsf.r
Optimization by simulated annealing requires a function to select a neighbouring state to test given the current state. This function generates such a neighbour selection function. The resulting function will swap the status of a single planning unit.
generate_nsf(pu, buffer, locked, recalculate = 1L)
| pu | raster::RasterStack, sp::SpatialPolygonsDataFrame, or sf::sf object; planning units. |
|---|---|
| buffer | numeric; a buffer around the perimeter of the currently selected planning units in the units of the projection. Any planning units within this buffer will be considered for switching when generating a neighbouring state. |
| locked | name of a binary column (for vector data) or layer (for raster data) that specified planning units that are locked in or out and should be left unchanged. Alternatively, a integer vector of planning unit numbers may be supplied specifying units to leave unchanged. |
| recalculate | integer; frequency at which the set of planning units to
consider for switching should be recalculated, e.g. a value of 100 would
result in recalculating every 100 calls to the returned function. This
process of recalculating can be computationally intensive for large sets
of planning units, so choosing a larger value for |
A function to choose a neighbouring state given a vector of binary
decision variables. To return the list of planning units under consideration
for switching use pu_list = TRUE.
In an attempt to more intelligently select a planning unit to switch, only those planning units meeting the following criteria will be considered:
Units touching the boundary of the existing reserve network, whether inside or outside.
Units within a defined distance, buffer, of the boundary of the existing
reserve network.
This ensures that units in the interior of an existing reserve or far from an existing reserve are not changed.
# generate data r <- raster::raster(nrows = 10, ncols = 10, crs = "+proj=aea", vals = 0) # lock some units to be unchanged names(r) <- "locked_out" r[sample(1:100, 10)] <- 1 pus <- raster::rasterToPolygons(r) selected <- sample(c(FALSE, TRUE), 100, replace = TRUE, prob = c(0.9, 0.1)) # generate neighbour selection function neighbour <- generate_nsf(pus, buffer = 20, locked = "locked_out") # neighbouring solution neighbour(selected)#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE #> [13] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [25] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [37] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> [61] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE #> [73] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE #> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [97] FALSE FALSE FALSE FALSEneighbour(selected, pu_list = TRUE)#> [1] 4 5 6 7 8 10 14 15 17 18 19 20 24 25 26 28 29 30 34 35 36 37 38 39 40 #> [26] 46 48 49 50 55 56 57 58 60 61 62 65 66 67 72 75 76 77 78 81 82 85 86 87 91 #> [51] 92 95 96 97