sp (version 1.1-0)

aggregate: aggregation of spatial objects

Description

spatial aggregation of thematic information in spatial objects

Usage

## S3 method for class 'Spatial':
aggregate(x, by, FUN = mean, \dots, dissolve = TRUE, areaWeighted = FALSE)

Arguments

x

object deriving from Spatial, with attributes

by

aggregation predicate; if by is a Spatial object, the geometry over which attributes in x are aggregated; if by is a list, aggregation by attribute(s), see

FUN

aggregation function

...

arguments passed on to function FUN

dissolve

logical; should, when aggregating based on attributes, the resulting geometries be dissolved? Note that if x has class SpatialPointsDataFrame, this is not possible

areaWeighted

logical; should the aggregation of x be weighted by the areas it intersects with each feature of by? See value.

Value

• The aggregation of attribute values of x either over the geometry of by by using over for spatial matching, or by attribute values, using aggregation function FUN.

  If areaWeighted is TRUE, FUN is ignored and the area weighted mean is computed for numerical variables, or if all attributes are factors, the area dominant factor level (area mode) is returned. This will compute the gIntersection of x and by; see examples below.

Examples

data("meuse")
coordinates(meuse) <- ~x+y
data("meuse.grid")
coordinates(meuse.grid) <- ~x+y
gridded(meuse.grid) <- TRUE
i = cut(meuse.grid$dist, c(0,.25,.5,.75,1), include.lowest = TRUE)
j = sample(1:2, 3103,replace=TRUE)
if (require(rgeos)) {
	# aggregation by spatial object:
	ab = gUnaryUnion(as(meuse.grid, "SpatialPolygons"), meuse.grid$part.a)
	x = aggregate(meuse["zinc"], ab, mean)
	spplot(x)
	# aggregation by attribute, then dissolve to polygon:
	x = aggregate(meuse.grid["dist"], list(i=i))
	spplot(x["i"])
	x = aggregate(meuse.grid["dist"], list(i=i,j=j))
	spplot(x["dist"], col.regions=bpy.colors())
	spplot(x["i"], col.regions=bpy.colors(4))
	spplot(x["j"], col.regions=bpy.colors())
}

x = aggregate(meuse.grid["dist"], list(i=i,j=j), dissolve = FALSE)
spplot(x["j"], col.regions=bpy.colors())

if (require(gstat) && require(rgeos)) {
	x = idw(log(zinc)~1, meuse, meuse.grid, debug.level=0)[1]
	spplot(x[1],col.regions=bpy.colors())
	i = cut(x$var1.pred, seq(4, 7.5, by=.5), 
		include.lowest = TRUE)
	xa = aggregate(x["var1.pred"], list(i=i))
	spplot(xa[1],col.regions=bpy.colors(8))
}

if (require(rgeos)) {
# Area-weighted example, using two partly overlapping grids:

  gt1 = SpatialGrid(GridTopology(c(0,0), c(1,1), c(4,4)))
  gt2 = SpatialGrid(GridTopology(c(-1.25,-1.25), c(1,1), c(4,4)))

  # convert both to polygons; give p1 attributes to aggregate
  p1 = SpatialPolygonsDataFrame(as(gt1, "SpatialPolygons"), 
		  data.frame(v = 1:16, w=5:20, x=factor(1:16)), match.ID = FALSE)
  p2 = as(gt2, "SpatialPolygons")

  # plot the scene:
  plot(p1, xlim = c(-2,4), ylim = c(-2,4))
  plot(p2, add = TRUE, border = 'red')
  i = gIntersection(p1, p2, byid = TRUE)
  plot(i, add=TRUE, density = 5, col = 'blue')
  # plot IDs p2:
  ids.p2 = sapply(p2@polygons, function(x) slot(x, name = "ID"))
  text(coordinates(p2), ids.p2)
  # plot IDs i:
  ids.i = sapply(i@polygons, function(x) slot(x, name = "ID"))
  text(coordinates(i), ids.i, cex = .8, col = 'blue')

  # compute & plot area-weighted average; will warn for the factor
  ret = aggregate(p1, p2, areaWeighted = TRUE)
  spplot(ret)

  # all-factor attributes: compute area-dominant factor level:
  ret = aggregate(p1["x"], p2, areaWeighted = TRUE) 
  spplot(ret)
}