| calc.sdd {aspace} | R Documentation |
The dispersion of a set of points on a Cartesian plane can be described using the Standard Distance Deviation (SDD) or Standard Distance. For the purpose of geographic visualization, the SDD is typically portrayed as a circle with radius SDD centered on the mean center of a set of point observations. The orthogonal dispersion of a set of points can also be described using the standard deviation of the x- and y-coordinates of a set of point observations. The standard deviation of x- and y-coordinates can be geographically visualized using a box, with the edges set, respectively, to the standard deviation of the x- and y-coordinates.
calc.sdd(id = 1, filename = "SDD_Output.txt", centre.xy = centre, calccentre = TRUE, useWMC = FALSE, weightpoints = FALSE, weights = wts, destmat = activities, verbose = FALSE, plot = TRUE, plothv = TRUE, plotdest = TRUE, plotcenter = TRUE, box = TRUE)
id |
A unique integer to identify the shape |
filename |
A string indicating the ASCII textfile where shape coordinates will be written |
centre.xy |
A vector of length 2, containing the x- and y-coordinates of the SDD centroid |
calccentre |
Boolean: Set to TRUE if the mean center is to be calculated |
useWMC |
Boolean: Set to TRUE if the mean center is to be computed with weighted coordinates |
weightpoints |
Boolean: Set to TRUE if the point observations are to be weighted |
weights |
Weights applied to point observations |
destmat |
A 2-column matrix or data frame containing point coordinates |
verbose |
Boolean: Set to TRUE if extensive feedback is desired on the standard output |
plot |
Boolean: Set to TRUE if the SDD is to be plotted |
plothv |
Boolean: Set to TRUE if the orthogonal N-S, E-W axes are to be plotted through the center |
plotdest |
Boolean: Set to TRUE if the point observations are to be plotted |
plotcenter |
Boolean: Set to TRUE if the mean center is to be plotted |
box |
Boolean: Set to TRUE if the standard deviation of the x- and y-coordinates are to be plotted as a box |
This function is most powerful when used repetitively within a loop to compute the SDD for subsets of points stored in a large table.
The result is a list of terms:
id |
Identifier for the SDD shape - it should be unique |
calccentre |
True if mean centre is computed |
Orig.x |
Original x-coordinate of center before mean center calculation |
Orig.y |
Original y-coordinate of center before mean center calculation |
CENTRE.x |
Actual, used x-coordinate of centre |
CENTRE.y |
Actual, used y-coordinate of centre |
SD.x |
Standard deviation of the x-coordinates |
SD.y |
Standard deviation of the y-coordinates |
SDD.radius |
SDD value, radius of the SDD |
Box.area |
Area of the box formed by the standard deviation of the x- and y-coordinates |
SDD.area |
Area of the SDD circle |
useWMC |
Boolean: TRUE if the weighted mean center is used |
WeightPoints |
Boolean: TRUE if point observations are weighted |
This function can be used on its own (once) or repetitively in a loop to process grouped point data stored in a larger table. When used repetitively, be sure to increment the id parameter to ensure that each SDD has a unique identifier. The output ASCII coordinate file can be further processed using the makeshapes function to generate an ESRI Shapefile for SDD polygons.
Tarmo K. Remmel, Ron Buliung
ellipse3, calc.mcp,
calc.sde, makeshapes
calc.sdd(id = 1, filename = "SDD_Output.txt", centre.xy = centre, calccentre = TRUE, useWMC = FALSE, weightpoints = FALSE, destmat = activities, verbose = FALSE, plot = TRUE, plothv = TRUE, plotdest = TRUE, plotcenter = TRUE, box = TRUE)