Treated, comparison and excluded places around sites

The buffer design of place-based evaluations: a place (a block, an address) within distance r of a site (a new facility, a treated corner) is treated; one within r of a comparison point and farther than buf from every site is a comparison; the rest are excluded, so that spillover from the sites (displacement or a diffusion of benefits, Bowers and Johnson 2003) does not reach the comparison. buf should be several times r.

Usage

bufas(x, y = NULL, site, comp, r, buf = 3 * r, lonlat = FALSE)

Arguments

x, y

The points: x and y numeric vectors of equal length; or x a matrix or data frame with columns named x and y (or X and Y, as sf::st_coordinates() names them, or lon and lat with lonlat = TRUE) and y NULL; or x an sf or sfc object of POINT geometries, whose coordinate reference system must agree with lonlat. An unnamed two-column matrix is read as x then y; other columns are never taken by position. Vectors given separately (x, y, and w or t where a function takes them) are matched by name when every one is named and no name repeats, as elsewhere in the package; otherwise by position. No missing or infinite values.

site, comp

The sites and the comparison points, as coordinates in the form of x (longitude and latitude when lonlat = TRUE).

r

The treatment radius: positive, in the unit of the coordinates (metres when lonlat = TRUE).

buf

The distance from every site a comparison place keeps, from 0.

lonlat

FALSE (the default) when x and y are planar coordinates in one length unit; TRUE when they are longitude and latitude in degrees, which are then projected to metres by eqproj() about their own centre, and centres are returned in degrees. It is never guessed.

Value

A list of class bufas: pts, a data frame with one row per place: grp (a factor: treated, comparison, excluded), dsite and dcomp (the distances to the nearest site and comparison point); n, the counts of the groups; par, r and buf.

References

Bowers, K. J. and Johnson, S. D. (2003). Measuring the geographical displacement and diffusion of benefit effects of crime prevention activity. Journal of Quantitative Criminology, 19(3), 275-301. doi:10.1023/A:1024909009240

Examples

set.seed(4)
p <- cbind(x = runif(200, 0, 5000), y = runif(200, 0, 5000))
b <- bufas(p, site = cbind(c(1000, 3500), c(1200, 3000)),
           comp = cbind(c(2500, 4500), c(500, 4000)), r = 330)
b
#> bufas: radius 330, buffer 990
#>    treated comparison   excluded 
#>          5          4        191