spdep (version 1.3-7)

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

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; 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

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

\( I_i^B= cx_i\Sigma_j{w_{ij}y_j} \)

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 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$hs <- hotspot(res, Prname="Pr(folded) Sim", cutoff=0.05,
 quadrant.type="pysal", p.adjust="none")
# \donttest{
if (require("tmap", quietly=TRUE)) {
tmap4 <- packageVersion("tmap") >= "3.99"
if (tmap4) {
  tm_shape(columbus) + tm_polygons(fill="hs",
    fill.scale=tm_scale(values="brewer.set3"),
    fill.legend=tm_legend(position=tm_pos_in("left", "top"),
      frame=FALSE, item.r=0), lwd=0.01)
} else {
  tm_shape(columbus) + tm_fill("hs")
}
}
# }
moran.plot(x=columbus$CRIME, y=columbus$INC, listw=listw)