lisa: Local indicator of spatial association

In ncf: Spatial Covariance Functions

Local indicator of spatial association

Description

lisa is a function to estimate the local indicators of spatial association. The function assumes univariate data at each location. For multivariate data use lisa.nc

Usage

lisa(x, y, z, neigh, resamp = 999, latlon = FALSE, quiet = FALSE)

Arguments

x	vector of length n representing the x coordinates (or latitude; see latlon).
y	vector of length n representing the y coordinates (or longitude).
z	vector of n representing the observation at each location.
neigh	neighborhood size.
resamp	number of resamples under the NULL to generate p-values
latlon	If TRUE, coordinates are latitude and longitude.
quiet	If TRUE, the counter is suppressed during execution.

Details

This is the function to estimate the local indicators of spatial association modified form Anselin (1995). The statistic is the average autocorrelation within a neighborhood.

Value

An object of class "lisa" is returned, consisting of the following components:

correlation	the autocorrelation within the neighborhood (neigh) of each observation measured using Moran's I.
p	the permutation two-sided p-value for each observation.
mean	the mean of the observations inside each neighborhooddistance within each neighborhood.
n	the number of observations within each neighborhood.
dmean	the actual mean distance within each neighborhood.
z	the original observations
coord	a list with the x and y coordinates.

Author(s)

Ottar N. Bjornstad [e-mail]

References

Anselin, L. 1995. Local indicators of spatial association - LISA. Geographical Analysis 27:93-115. <doi:10.1111/j.1538-4632.1995.tb00338.x>

See Also

plot.lisa

Examples

# first generate some sample data
x <- expand.grid(1:20, 1:5)[, 1]
y <- expand.grid(1:20, 1:5)[, 2]

# z data from an exponential random field
z <- rmvn.spa(x = x, y = y, p = 2, method = "gaus")

# lisa analysis
fit1 <- lisa(x = x, y = y, z = z, neigh = 3, resamp = 499)
## Not run: plot(fit1, neigh.mean=FALSE)

ncf documentation built on May 7, 2022, 5:05 p.m.