localmoran_bv: Compute the Local Bivariate Moran's I Statistic

In spdep: Spatial Dependence: Weighting Schemes, Statistics

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, zero.policy=attr(listw, "zero.policy"))

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; the output will only be reproducible if the count of CPU cores across which computation is distributed is the same.
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
zero.policy	default default attr(listw, "zero.policy") as set when listw was created, if attribute not set, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA

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= cx_i\Sigma_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.

Author(s)

Josiah Parry [e-mail]

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 datay
columbus <- st_read(system.file("shapes/columbus.gpkg", package="spData"))
nb <- poly2nb(columbus)
listw <- nb2listw(nb)
set.seed(1)
(res <- localmoran_bv(columbus$CRIME, columbus$INC, listw, nsim = 499))
columbus$hs <- hotspot(res, Prname="Pr(folded) Sim", cutoff=0.05,
 quadrant.type="pysal", p.adjust="none")

if (require("tmap", quietly=TRUE)) {
tmap4 <- packageVersion("tmap") >= "3.99"
if (tmap4) {
  tm_shape(columbus) + tm_polygons(fill="hs",
    fill.scale=tm_scale(values="brewer.set3"),
    fill.legend=tm_legend(position=tm_pos_in("left", "top"),
      frame=FALSE, item.r=0), lwd=0.01)
} else {
  tm_shape(columbus) + tm_fill("hs")
}
}

moran.plot(x=columbus$CRIME, y=columbus$INC, listw=listw)

spdep documentation built on Sept. 1, 2025, 1:09 a.m.