discretize: Rasterize Area Search or Transect Data

In secr: Spatially Explicit Capture-Recapture

Rasterize Area Search or Transect Data

Description

It is sometimes useful to re-cast area-search (polygon or polygonX) data as if it was from a set of closely spaced point detectors, i.e. to rasterize the detection locations. This function makes that conversion. Each polygon detector in the input is replaced by a number of point detectors, each representing a square pixel. Detections are mapped to the new detectors on the basis of their x-y coordinates.

If object contains transect data the problem is passed to snip and reduce.capthist.

Usage

discretize(object, spacing = 5, outputdetector = c("proximity", "count", "multi"), 
    tol = 0.001, cell.overlap = FALSE, type = c("centre","any", "all"), ...)

Arguments

object	secr capthist or traps object
spacing	numeric spacing between point detectors in metres
outputdetector	character output detector type
tol	numeric fractional inflation of perimeter (see Details)
cell.overlap	logical; if TRUE the area of overlap is stored in usage attribute
type	character; see Details
...	other arguments passed to snip if object is transect

Details

The input should have detector type ‘polygon’ or ‘polygonX’.

A new array of equally spaced detectors is generated within each polygon of the input, inflated radially by 1 + tol to avoid some inclusion problems. The origin of the superimposed grid is fixed automatically. If type = "centre" detectors are included if they lie within the (inflated) polygon. Otherwise, the decision on whether to include a candidate new detector is based on the corner vertices of the cell around the detector (side = spacing); type = "any" and type = "all" have the obvious meanings.

tol may be negative, in which case the array(s) will be shrunk relative to the polygon(s).

For irregular polygons the edge cells in the output may be only partially contained within the polygon they represent. Set cell.overlap = TRUE to retain the proportion of overlap as the ‘usage’ of the new traps object. This can take a few seconds to compute. If ‘usage’ is already defined then the new ‘usage’ is the old multiplied by the proportion of overlap.

Combining type = "any" and cell.overlap = TRUE with tol > 0 can have the odd effect of including some marginal detectors that are assigned zero usage.

With type = "any", the sum of the overlap proportions times cell area is equal to the area of the polygons.

Value

A capthist or traps object of the requested detector type, but otherwise carrying forward all attributes of the input. The embedded traps object has a factor covariate ‘polyID’ recording the polygon to which each point detector relates.

Note

Consider the likely number of detectors in the output before you start.

See Also

reduce.capthist, snip

Examples

## Not run: 

## generate some polygon data
pol <- make.poly()
CH <- sim.capthist(pol, popn = list(D = 30), detectfn = 'HHN', 
   detectpar = list(lambda0 = 0.3))
plot(CH, border = 10, gridl = FALSE, varycol = FALSE)

## discretize and plot
CH1 <- discretize(CH, spacing = 10, output = 'count')
plot(CH1, add = TRUE, cappar = list(col = 'orange'), varycol =
    FALSE, rad = 0)
plot(traps(CH1), add = TRUE)
# overlay cell boundaries
plot(as.mask(traps(CH1)), dots = FALSE, col = NA, meshcol = 'green', 
    add = TRUE)

## show how detections are snapped to new detectors
newxy <- traps(CH1)[nearesttrap(xy(CH),traps(CH1)),]
segments(xy(CH)[,1], xy(CH)[,2], newxy[,1], newxy[,2])

plot(traps(CH), add = TRUE)  # original polygon

## Incomplete overlap

pol <- rotate(make.poly(), 45)
CH2 <- sim.capthist(pol, popn = list(D = 30), detectfn = 'HHN', 
   detectpar = list(lambda0 = 0.3))
plot(CH2, border = 10, gridl = FALSE, varycol = FALSE)
CH3 <- discretize(CH2, spacing = 10, output = 'count', type = 'any', 
    cell.overlap = TRUE, tol=0.05)
    
plot(CH3, add = TRUE, cappar = list(col = 'orange'), varycol =
    FALSE, rad = 0)
plot(traps(CH3), add = TRUE)

# overlay cell boundaries and usage
msk <- as.mask(traps(CH3))
covariates(msk) <- data.frame(usage = usage(traps(CH3))[,1])
plot(msk, dots = FALSE, cov='usage', meshcol = 'green', 
    add = TRUE)
    

## End(Not run)

secr documentation built on Oct. 18, 2023, 1:07 a.m.