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aggregate

aggregation of spatial objects

Description

spatial aggregation of thematic information in spatial objects

Usage

## S3 method for class 'Spatial'
aggregate(x, by = list(ID = rep(1, length(x))),
	FUN, ..., dissolve = TRUE, areaWeighted = FALSE)

Arguments

`x`	object deriving from Spatial, with attributes
`by`	aggregation predicate; if `by` is a Spatial object, the geometry by which attributes in `x` are aggregated; if `by` is a list, aggregation by attribute(s), see aggregate.data.frame
`FUN`	aggregation function, e.g. mean; see details
`...`	arguments passed on to function `FUN`, unless `minDimension` is specified, which is passed on to function over
`dissolve`	logical; should, when aggregating based on attributes, the resulting geometries be dissolved? Note that if `x` has class `SpatialPointsDataFrame`, this returns an object of class `SpatialMultiPointsDataFrame`
`areaWeighted`	logical; should the aggregation of `x` be weighted by the areas it intersects with each feature of `by`? See value.

Details

FUN should be a function that takes as first argument a vector, and that returns a single number. The canonical examples are mean and sum. Counting features is obtained when summing an attribute variable that has the value 1 everywhere.

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.

If by is missing, aggregates over all features.

Note

uses over to find spatial match if by is a Spatial object

Author(s)

Edzer Pebesma, edzer.pebesma@uni-muenster.de

Examples

data("meuse")
coordinates(meuse) <- ~x+y
data("meuse.grid")
coordinates(meuse.grid) <- ~x+y
gridded(meuse.grid) <- TRUE
i = cut(meuse.grid$dist, c(0,.25,.5,.75,1), include.lowest = TRUE)
j = sample(1:2, 3103,replace=TRUE)
## Not run: 
if (require(rgeos)) {
	# aggregation by spatial object:
	ab = gUnaryUnion(as(meuse.grid, "SpatialPolygons"), meuse.grid$part.a)
	x = aggregate(meuse["zinc"], ab, mean)
	spplot(x)
	# aggregation of multiple variables
	x = aggregate(meuse[c("zinc", "copper")], ab, mean)
	spplot(x)
	# aggregation by attribute, then dissolve to polygon:
	x = aggregate(meuse.grid["dist"], list(i=i), mean)
	spplot(x["i"])
	x = aggregate(meuse.grid["dist"], list(i=i,j=j), mean)
	spplot(x["dist"], col.regions=bpy.colors())
	spplot(x["i"], col.regions=bpy.colors(4))
	spplot(x["j"], col.regions=bpy.colors())
}

## End(Not run)

x = aggregate(meuse.grid["dist"], list(i=i,j=j), mean, 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), mean)
	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)
}

sp

Classes and Methods for Spatial Data

v1.4-5

GPL (>= 2)

Authors

Edzer Pebesma [aut, cre], Roger Bivand [aut], Barry Rowlingson [ctb], Virgilio Gomez-Rubio [ctb], Robert Hijmans [ctb], Michael Sumner [ctb], Don MacQueen [ctb], Jim Lemon [ctb], Finn Lindgren [ctb], Josh O'Brien [ctb], Joseph O'Rourke [ctb]

Initial release