localG {spdep} | R Documentation

## G and Gstar local spatial statistics

### Description

The local spatial statistic G is calculated for each zone based on the spatial weights object used. The value returned is a Z-value, and may be used as a diagnostic tool. High positive values indicate the posibility of a local cluster of high values of the variable being analysed, very low relative values a similar cluster of low values. For inference, a Bonferroni-type test is suggested in the references, where tables of critical values may be found (see also details below).

### Usage

    localG(x, listw, zero.policy=FALSE, spChk=NULL)

### Arguments

`x` | a numeric vector the same length as the neighbours list in listw
`listw` | a `listw` object created for example by `nb2listw`
`zero.policy` | if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
`spChk` | should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use `get.spChkOption()`

### Details

If the neighbours member of listw has a "self.included" attribute set to TRUE, the Gstar variant, including the self-weight w_{ii} > 0, is calculated and returned. The returned vector will have a "gstari" attribute set to TRUE. Self-weights can be included by using the `include.self` function in the spweights package before converting the neighbour list to a spatial weights list with `nb2listw` as shown below in the example.

The critical values of the statistic under assumptions given in the references for the 95th percentile are for n=1: 1.645, n=50: 3.083, n=100: 3.289, n=1000: 3.886.

### Value

A vector of G or Gstar values, with attributes "gstari" set to TRUE or FALSE, "call" set to the function call, and class "localG".

### Author(s)

Roger Bivand Roger.Bivand@nhh.no

### References

Ord, J. K. and Getis, A. 1995 Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27, 286–306; Getis, A. and Ord, J. K. 1996 Local spatial statistics: an overview. In P. Longley and M. Batty (eds) Spatial analysis: modelling in a GIS environment (Cambridge: Geoinformation International), 261–277.

### Examples

    data(getisord)
    xycoords <- cbind(xyz$x, xyz$y)
    nb30 <- dnearneigh(xycoords, 0, 30)
    G30 <- localG(xyz$val, nb2listw(nb30, style="B"))
    G30[length(xyz$val)-136]
    nb60 <- dnearneigh(xycoords, 0, 60)
    G60 <- localG(xyz$val, nb2listw(nb60, style="B"))
    G60[length(xyz$val)-136]
    nb90 <- dnearneigh(xycoords, 0, 90)
    G90 <- localG(xyz$val, nb2listw(nb90, style="B"))
    G90[length(xyz$val)-136]
    nb120 <- dnearneigh(xycoords, 0, 120)
    G120 <- localG(xyz$val, nb2listw(nb120, style="B"))
    G120[length(xyz$val)-136]
    nb150 <- dnearneigh(xycoords, 0, 150)
    G150 <- localG(xyz$val, nb2listw(nb150, style="B"))
    G150[length(xyz$val)-136]
    brks <- seq(-5,5,1)
    cm.col <- cm.colors(length(brks)-1)
    image(x, y, t(matrix(G30, nrow=16, ncol=16, byrow=TRUE)),
      breaks=brks, col=cm.col, asp=1)
    text(xyz$x, xyz$y, round(G30, digits=1), cex=0.7)
    polygon(c(195,225,225,195), c(195,195,225,225), lwd=2)
    title(main=expression(paste("Values of the ", G[i], " statistic")))
    G30s <- localG(xyz$val, nb2listw(include.self(nb30),
     style="B"))
    cat("value according to Getis and Ord's eq. 14.2, p. 263 (1996)\n")
    G30s[length(xyz$val)-136]
    cat(paste("value given by Getis and Ord (1996), p. 267",
      "(division by n-1 rather than n \n in variance)\n"))
    G30s[length(xyz$val)-136] *
      (sqrt(sum(scale(xyz$val, scale=FALSE)^2)/length(xyz$val)) /
      sqrt(var(xyz$val)))
    image(x, y, t(matrix(G30s, nrow=16, ncol=16, byrow=TRUE)),
      breaks=brks, col=cm.col, asp=1)
    text(xyz$x, xyz$y, round(G30s, digits=1), cex=0.7)
    polygon(c(195,225,225,195), c(195,195,225,225), lwd=2)
    title(main=expression(paste("Values of the ", G[i]^"*", " statistic")))

[Package spdep version 0.4-56 Index]