spdep (version 1.3-1)

localmoran_bv: Compute the Local Bivariate Moran's I Statistic

Description

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

Usage

localmoran_bv(x, y, listw, nsim = 199, scale = TRUE, alternative="two.sided",
 iseed=1L, no_repeat_in_row=FALSE)

Value

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

Arguments

x

a numeric vector of same length as y.

y

a numeric vector of same length as x.

listw

a listw object for example as created by nb2listw().

nsim

the number of simulations to run.

scale

default TRUE.

alternative

a character string specifying the alternative hypothesis, must be one of "greater" (default), "two.sided", or "less".

iseed

default NULL, used to set the seed for possible parallel RNGs.

no_repeat_in_row

default FALSE, if TRUE, sample conditionally in each row without replacements to avoid duplicate values, https://github.com/r-spatial/spdep/issues/124

Author

Josiah Parry

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 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

$I_i^B=c{x}_i{\mathrm{\Sigma }}_j{w}_{ij}{y}_j$

References

Anselin, Luc, Ibnu Syabri, and Oleg Smirnov. 2002. “Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows.” In New Tools for Spatial Data Analysis: Proceedings of the Specialist Meeting, edited by Luc Anselin and Sergio Rey. University of California, Santa Barbara: Center for Spatially Integrated Social Science (CSISS).

Examples

# load columbus data
columbus <- st_read(system.file("shapes/columbus.shp", package="spData"))
nb <- poly2nb(columbus)
listw <- nb2listw(nb)
set.seed(1)
(res <- localmoran_bv(columbus$CRIME, columbus$INC, listw, nsim = 499))