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
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 |
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) matching 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.
In case the rgeos over methods are used,
matching is done by gRelate, which uses DE-9IM
(https://en.wikipedia.org/wiki/DE-9IM). From the string
returned, characters 1, 2, 4 and 5 are used, indicating the dimension
of the overlap of the inner and boundary of each x geometry
with the inner and boundary of each y geometry. The order
in which matched y geometries are returned is determined by
the dimension of the overlap (2: area overlap, 1: line in common,
0: point in common), and then by the position in the string (1,
2, 4, 5, meaning points in polygons are preferred over points on
polygon boundaries).
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.
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
returns a data.frame of the second argument with row entries corresponding to the first argument
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.
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.
returns grid values x at spatial point locations y; NA for NA grid cells or points outside the grid
returns grid values x at spatial point locations y; NA for NA grid cells or points outside the grid
returns grid values x at spatial point locations y; NA for NA grid cells or points outside the grid
xx
xx
xx
xx
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
vignette("over") for examples and figures;
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.