getis.cluster {bispdep}

Getis and Ord's Gi* Cluster and Significance Map

Description

Create the Getis Gi* Cluster Map and the corresponding Significance Map. Maps are done calculating the Local Gi* (localG - spdep) for each spatial unit and testing its significance.

Usage

getis.cluster(x, listw, zero.policy = NULL, polygons, significant = TRUE, pleg, ...)

Arguments

x

variable to create cluster and significance map

listw

a neighbours list with spatial weights. From package spdep: a listw object. Use poly2nb (class nb) and nb2listw (class listw, nb) from package spdep. Can be any type of listw object, for instance, rook contiguity (common edge) or queen contiguity (common edge or common vertex)

zero.policy

by default = NULL, if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors. Parameter inherited from the spdep package.

polygons

SpatialPolygons, SpatialPolygonsDataFrame or sfc_POLYGON object

significant

by default is TRUE, if FALSE the significant map is not created

pleg

the x and y co-ordinates to be used to position the legend. They can be specified by keyword or in any way which is accepted by xy.coords

...

other graphical parameters as in par(..)

Details

Using the function localG (spdep) create the Getis Gi* Cluster Map and the corresponding Significance Map. The significance map is done testing the null hypothesis (Ho) of zero spatial autocorrelation for each spatial unit, then plotting a choropleth map with this legend values: (Not Significant, p-value=0.05, p-value= 0.01, p-value=0.001, p-value=0.0001, and Neighborless). Most significant clustered spatial units are those with p-values smaller than 0.0001. Not significant clustered spatial units are those with p-values grather than 0.05. Gi* Cluster Map is done based on the significance map, but the choropleth legend is different (Not - Significant, High-High, Low-Low, Low-High, High-Low, and Neighborless).

Value

one or two maps

Links

1. Spatial Autocorrelation

See Also

• Bivariate Moran's Ixy: moran.bi

• Plot Bivariate Moran's Ixy: moranbi.plot

• Bivariate Moran's Ixy Test: moranbi.test

• Bivariate Local Moran's Ixy and Test: localmoran.bi

• Create object "nb": poly2nb

• Create object "listw"/"nb": nb2listw

Examples

library(spdep)
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
col_nbq <- poly2nb(columbus)
a.lw <- nb2listw(col_nbq, style="W")
getis.cluster(columbus$CRIME, a.lw, zero.policy = FALSE, st_geometry(columbus),
              significant=TRUE, pleg = "topleft")