aggregate | R Documentation |

spatial aggregation of thematic information in spatial objects

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

`x` |
object deriving from Spatial, with attributes |

`by` |
aggregation predicate; if |

`FUN` |
aggregation function, e.g. mean; see details |

`...` |
arguments passed on to function |

`dissolve` |
logical; should, when aggregating based on attributes, the
resulting geometries be dissolved? Note that if |

`areaWeighted` |
logical; should the aggregation of |

`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.

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 `factor`

s, 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.

uses over to find spatial match if `by`

is a
Spatial object

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

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) }

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