local_moran_bv: Compute the Local Bivariate Moran's I Statistic

In sfdep: Spatial Dependence for Simple Features

Compute the Local Bivariate Moran's I Statistic

Description

Given two continuous numeric variables, calculate the bivariate Local Moran's I.

Usage

local_moran_bv(x, y, nb, wt, nsim = 499)

Arguments

x	a numeric vector of same length as y.
y	a numeric vector of same length as x.
nb	a neighbor list object for example as created by st_contiguity().
wt	a weights list as created by st_weights().
nsim	the number of simulations to run.

Details

The Bivariate Local Moran, like its global counterpart, evaluates the value of x at observation i with its spatial neighbors' value of y. The value of

I_i^B

is just xi * Wyi. Or, in simpler words the local bivariate Moran is the result of multiplying x by the spatial lag of y. Formally it is defined as

I_i^B= cx_iΣ_j{w_{ij}y_j}

Value

a data.frame containing two columns Ib and p_sim containing the local bivariate Moran's I and simulated p-values respectively.

References

Local Spatial Autocorrelation (3): Multivariate Local Spatial Autocorrelation, Luc Anselin

See Also

Other global_moran: global_moran_bv(), global_moran_perm(), global_moran_test(), global_moran()

Examples

x <- guerry_nb$crime_pers
y <- guerry_nb$wealth
nb <- guerry_nb$nb
wt <- guerry_nb$wt
local_moran_bv(x, y, nb, wt)

sfdep documentation built on Jan. 11, 2023, 9:08 a.m.