SpatialNeighbours: Nearest Neighbours Among Data Points on a Two-dimensional Map

In cuboideum/deadpop: Analysis of Skeletal Populations

Description

The function takes a range of points on a two-dimensional surface as input, specified by vectors of x- and y-coordinates and a vector of identifiers. For any point in this cloud, the function determins a specified number of other points with smallest distances to the point in questions. The calculation of distances can be carried out in a non-projected space or as distances between geographical coordinates.

Usage

SpatialNeighbours(ID, x, y, n, l = NA, l.included = TRUE, l.x = NA, l.y = NA)

Arguments

ID	A vector of distinct identifiers for all cloud points.
x, y	Numeric vectors containing the x- and y-coordinates of the points specified in ID.
n	An integer specifying the number of individuals that are to form the group of spatial neighbours to be determined. l.included specifies whether this group contains the individual defined in l or not.
l	The identifier of the inidvidual for which nearest neighbours are to be determined. l.included specifies whether l is part of the group of spatial neighbours to be determined. This also determines whether it needs to be an element of ID or not.
l.included	A logical operator. If set to TRUE (this is the default), the individual defined by l is part of the group of spatial neighbours to be determined. In this case, l has to be an element of ID. If set to FALSE, l will not be part of the resulting group and it does not matter if it is an element of ID or not.
l.x, l.y	Numeric values specifying the x- and y-coordinates of the point specified by l. Specification of these parameters only makes sense if l.included == FALSE and l is not an element of ID.

Value

A vector of length n is returned, giving the distances to l for all nearest neighbours that have been determined. The names of the vector elements give the points identifiers from ID.

Note

As ID, x and y all refer to the same point set, they have to be of the same length.

Examples

## Data for 50 spatial points
ID_x <- paste(rep("grave", 50), 1:50, sep=" ")
x_x <- sample(50, 50)
y_x <- sample(50, 50)

## The focus point is part of the specified spatial points
x1 <- SpatialNeighbours(ID=ID_x, x=x_x, y=y_x, n=20, l="grave 25")
plot(x_x, y_x, xlab="x", ylab="y") # all spatial points
points(x_x[ID_x %in% names(x1)], y_x[ID_x %in% names(x1)], pch=19) # focus group
points(x_x[25], y_x[25], col="red", pch=19) # focus point

## The focus point is outside the specified spatial points
x2 <- SpatialNeighbours(ID=ID_x, x=x_x, y=y_x, n=20, l.included=FALSE, l.x=25, l.y=25)
plot(x_x, y_x, xlab="x", ylab="y") # all spatial points
points(x_x[ID_x %in% names(x2)], y_x[ID_x %in% names(x2)], pch=19) # focus group
points(25, 25, col="red", pch=19) # focus point

cuboideum/deadpop documentation built on Feb. 5, 2021, 11:21 p.m.