over: consistent spatial overlay for points, grids and polygons
In dis-organization/sp_dplyrexpt: Classes and Methods for Spatial Data

Description Usage Arguments Value Methods Note Author(s) See Also Examples

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

1 2	over(x, y, returnList = FALSE, fn = NULL, ...) x %over% y

`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
`...`	arguments passed on to function `fn`

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.

x = "SpatialPoints", y = "SpatialPolygons": returns a numeric vector of length equal to the number of points; the number is the index (number) of the polygon of y in which a point falls; NA denotes the point does not fall in a polygon; if a point falls in multiple polygons, the last polygon is recorded.
x = "SpatialPointsDataFrame", y = "SpatialPolygons": equal to the previous method, except that an argument fn=xxx is allowed, e.g. fn = mean which will then report a data.frame with the mean attribute values of the x points falling in each polygon (set) of y
x = "SpatialPoints", y = "SpatialPolygonsDataFrame": returns a data.frame of the second argument with row entries corresponding to the first argument
x = "SpatialPolygons", y = "SpatialPoints": returns the polygon index of points in y; if x is a SpatialPolygonsDataFrame, a data.frame with rows from x corresponding to points in y is returned.
x = "SpatialGridDataFrame", y = "SpatialPoints": returns object of class SpatialPointsDataFrame with grid attribute values x at spatial point locations y; NA for NA grid cells or points outside grid, and NA values on NA grid cells.
x = "SpatialGrid", y = "SpatialPoints": returns grid values x at spatial point locations y; NA for NA grid cells or points outside the grid
x = "SpatialPixelsDataFrame", y = "SpatialPoints": returns grid values x at spatial point locations y; NA for NA grid cells or points outside the grid
x = "SpatialPixels", y = "SpatialPoints": returns grid values x at spatial point locations y; NA for NA grid cells or points outside the grid
x = "SpatialPoints", y = "SpatialGrid": xx
x = "SpatialPoints", y = "SpatialGridDataFrame": xx
x = "SpatialPoints", y = "SpatialPixels": xx
x = "SpatialPoints", y = "SpatialPixelsDataFrame": xx
x = "SpatialPolygons", y = "SpatialGridDataFrame": xx

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 require package rgeos, and use gIntersects.

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

point.in.polygon, package gIntersects

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)