# over-methods

##### consistent spatial overlay for points, grids and polygons

consistent spatial overlay for points, grids and polygons: at the spatial locations of object x retrieves the indexes or attributes from spatial object y

- Keywords
- methods

##### Usage

```
over(x, y, returnList = FALSE, fn = NULL, ...)
x %over% y
## S3 method for class 'Spatial':
aggregate(x, by, FUN = mean, \dots)
```

##### Arguments

- x
- geometry (locations) of the queries
- y
- layer from which the geometries or attributes are queried
- returnList
- logical; see value
- fn
- (optional) a function; see value
- by
- geometry over which attributes in
`x`

are aggregated - FUN
- aggregation function
- ...
- arguments passed on to function fn or FUN

##### Value

- If
`y`

is only geometry an object of length`length(x)`

. If`returnList`

is FALSE, a vector with the (first) index of`y`

for each geometry (point, grid cell centre, polygon or lines) in`x`

. if`returnList`

is TRUE, a list of length`length(x)`

, with list element`i`

the vector of all indices of the geometries in`y`

that correspond to the $i$-th geometry in`x`

.If

`y`

has attribute data, attribute data are returned.`returnList`

is FALSE, a`data.frame`

with number of rows equal to`length(x)`

is returned, if it is TRUE a list with`length(x)`

elements is returned, with a list element the`data.frame`

elements of all geometries in`y`

that correspond to that element of`x`

.Function aggregate.Spatial aggregates the attribute values of

`x`

over the geometry of`by`

, using aggregation function FUN.

##### Note

`over`

can be seen as a left outer join in SQL; the
match is a spatial intersection.

points on a polygon boundary and points corresponding to a polygon vertex are considered to be inside the polygon.

These methods assume that pixels and grid cells are never
overlapping; for objects of class `SpatialPixels`

this is
not guaranteed.

over methods that involve `SpatialLines`

objects, or pairs of
`SpatialPolygons`

are implemented in, package rgeos.

##### See Also

##### Examples

```
r1 = cbind(c(180114, 180553, 181127, 181477, 181294, 181007, 180409,
180162, 180114), c(332349, 332057, 332342, 333250, 333558, 333676,
332618, 332413, 332349))
r2 = cbind(c(180042, 180545, 180553, 180314, 179955, 179142, 179437,
179524, 179979, 180042), c(332373, 332026, 331426, 330889, 330683,
331133, 331623, 332152, 332357, 332373))
r3 = cbind(c(179110, 179907, 180433, 180712, 180752, 180329, 179875,
179668, 179572, 179269, 178879, 178600, 178544, 179046, 179110),
c(331086, 330620, 330494, 330265, 330075, 330233, 330336, 330004,
329783, 329665, 329720, 329933, 330478, 331062, 331086))
r4 = cbind(c(180304, 180403,179632,179420,180304),
c(332791, 333204, 333635, 333058, 332791))
sr1=Polygons(list(Polygon(r1)),"r1")
sr2=Polygons(list(Polygon(r2)),"r2")
sr3=Polygons(list(Polygon(r3)),"r3")
sr4=Polygons(list(Polygon(r4)),"r4")
sr=SpatialPolygons(list(sr1,sr2,sr3,sr4))
srdf=SpatialPolygonsDataFrame(sr, data.frame(cbind(1:4,5:2), row.names=c("r1","r2","r3","r4")))
data(meuse)
coordinates(meuse) = ~x+y
plot(meuse)
polygon(r1)
polygon(r2)
polygon(r3)
polygon(r4)
# retrieve mean heavy metal concentrations per polygon:
over(sr, meuse[,1:4], fn = mean)
# return the number of points in each polygon:
sapply(over(sr, geometry(meuse), returnList = TRUE), length)
data(meuse.grid)
coordinates(meuse.grid) = ~x+y
gridded(meuse.grid) = TRUE
over(sr, geometry(meuse))
over(sr, meuse)
over(sr, geometry(meuse), returnList = TRUE)
over(sr, meuse, returnList = TRUE)
over(meuse, sr)
over(meuse, srdf)
# same thing, with grid:
over(sr, meuse.grid)
over(sr, meuse.grid, fn = mean)
over(sr, meuse.grid, returnList = TRUE)
over(meuse.grid, sr)
over(meuse.grid, srdf, fn = mean)
over(as(meuse.grid, "SpatialPoints"), sr)
over(as(meuse.grid, "SpatialPoints"), srdf)
```

*Documentation reproduced from package sp, version 1.0-11, License: GPL (>= 2)*