moran.plot: Moran scatterplot

In spdep: Spatial Dependence: Weighting Schemes, Statistics

Moran scatterplot

Description

A plot of spatial data against its spatially lagged values, augmented by reporting the summary of influence measures for the linear relationship between the data and the lag. If zero policy is TRUE, such observations are also marked if they occur.

Usage

moran.plot(x, listw, y=NULL, zero.policy=attr(listw, "zero.policy"), spChk=NULL,
 labels=NULL, xlab=NULL, ylab=NULL, quiet=NULL, plot=TRUE, return_df=TRUE, ...)

Arguments

x	a numeric vector the same length as the neighbours list in listw
listw	a listw object created for example by nb2listw
y	an optional numeric vector the same length as the neighbours list in listw for a bi-variate plot
zero.policy	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
spChk	should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()
labels	character labels for points with high influence measures, if set to FALSE, no labels are plotted for points with large influence
xlab	label for x axis
ylab	label for x axis
quiet	default NULL, use !verbose global option value; if TRUE, output of summary of influence object suppressed
plot	default TRUE, if false, plotting is suppressed
return_df	default TRUE, invisibly return a data.frame object; if FALSE invisibly return an influence measures object
...	further graphical parameters as in par(..)

Value

The function returns a data.frame object with coordinates and influence measures if return_df is TRUE, or an influence object from influence.measures.

Author(s)

Roger Bivand [e-mail]

References

Anselin, L. 1996. The Moran scatterplot as an ESDA tool to assess local instability in spatial association. pp. 111–125 in M. M. Fischer, H. J. Scholten and D. Unwin (eds) Spatial analytical perspectives on GIS, London, Taylor and Francis; Anselin, L. 1995. Local indicators of spatial association, Geographical Analysis, 27, 93–115

See Also

localmoran, influence.measures

Examples

data(afcon, package="spData")
mp <- moran.plot(afcon$totcon, nb2listw(paper.nb),
 labels=as.character(afcon$name), pch=19)
moran.plot(as.vector(scale(afcon$totcon)), nb2listw(paper.nb),
 labels=as.character(afcon$name), xlim=c(-2, 4), ylim=c(-2,4), pch=19)
if (require(ggplot2, quietly=TRUE)) {
  xname <- attr(mp, "xname")
  ggplot(mp, aes(x=x, y=wx)) + geom_point(shape=1) + 
    geom_smooth(formula=y ~ x, method="lm") + 
    geom_hline(yintercept=mean(mp$wx), lty=2) + 
    geom_vline(xintercept=mean(mp$x), lty=2) + theme_minimal() + 
    geom_point(data=mp[mp$is_inf,], aes(x=x, y=wx), shape=9) +
    geom_text(data=mp[mp$is_inf,], aes(x=x, y=wx, label=labels, vjust=1.5)) +
    xlab(xname) + ylab(paste0("Spatially lagged ", xname))
}
columbus <- st_read(system.file("shapes/columbus.shp", package="spData"))
nb <- poly2nb(columbus)
listw <- nb2listw(nb)
moran.plot(x=columbus$CRIME, y=columbus$INC, listw=listw)
moran.plot(x=columbus$INC, y=columbus$CRIME, listw=listw)

spdep documentation built on Sept. 13, 2024, 5:07 p.m.