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 |

For as far as these functions use package rgeos, (lines, polygons, dissolve = TRUE), they are deprecated as rgeos will retire; try using sf::aggregate instead.

`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 computes the intersection of `x`

and `by`

; see examples below. As this uses code from package
rgeos, it is deprecated as package rgeos will retire.

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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