sp (version 2.1-3)

aggregate: aggregation of spatial objects

Description

spatial aggregation of thematic information in spatial objects

Usage

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

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 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.

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; deprecated

areaWeighted

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

Author

Edzer Pebesma,

Details

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.

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)

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