SpatialVx (version 0.3)

Cindex: Connectivity Index

Description

Calculate the connectivity index of an image.

Usage

Cindex(x, thresh = NULL, connect.method = "C", ...)
## S3 method for class 'default':
Cindex(x, thresh = NULL, connect.method = "C", ...)
## S3 method for class 'SpatialVx':
Cindex(x, thresh = NULL, connect.method = "C", ...,
    time.point = 1, model = 1)

Arguments

x

Default: m by n numeric matrix giving the field for which the shape index is to be calculated.

Sindex.SpatialVx: list object of class SpatialVx.

thresh

Set values under (strictly less than) this threshold to zero, and calculate the connectivity index for the resulting image. If NULL, no threshold is applied.

connect.method

character string giving the method argument for the connected function of package spatstat. This must be one of C or interpreted. See the help file for connected for mor

time.point

numeric or character indicating which time point from the SpatialVx verification set to select for analysis.

model

numeric indicating which forecast model to select for the analysis.

...

Not used.

Value

• numeric giving the connectivity index.

Details

The connectivity index is introduced in AghaKouchak et al. (2011), and is designed to automaticlaly determine how connected an image is. It is defined by

Cindex = 1 - (NC - 1)/(sqrt(NP) + NC),

where 0 <= 1="" cindex="" <="1" is="" the="" connectivity="" index="" (values="" close="" to="" zero="" are="" less="" connected,="" and="" values="" more="" connected),="" np="" number="" of="" nonzero="" pixels,="" nc="" isolated="" clusters.="" p="">

The function connected from package spatstat is used to identify the number of isolated clusters.

References

AghaKouchak, A., Nasrollahi, N., Li, J., Imam, B. and Sorooshian, S. (2011) Geometrical characterization of precipitation patterns. J. Hydrometerology, 12, 274--285, doi:10.1175/2010JHM1298.1.

See Also

connected, as.im, Sindex, Aindex

Examples

data(geom000)
Cindex(geom000)

data(pert000)
Cindex(pert000)

# Two separate areas with highly structured shapes, but far away from each other.
data(pert006)
data(ICPg240Locs)

hold <- make.SpatialVx(pert000, pert006, loc=ICPg240Locs,
    projection=TRUE, map=TRUE, loc.byrow = TRUE,
    field.type="Precipitation", units="mm/h",
    data.name=c("ICP Perturbed Cases", "pert000", "pert006"))

plot(hold, set.pw=TRUE)

Cindex(hold)