Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

moran.plot

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, zero.policy=NULL, 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

zero.policy

default NULL, 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 Roger.Bivand@nhh.no

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

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))
}

spdep

Spatial Dependence: Weighting Schemes, Statistics

v1.1-11
GPL (>= 2)
Authors
Roger Bivand [cre, aut] (<https://orcid.org/0000-0003-2392-6140>), Micah Altman [ctb], Luc Anselin [ctb], Renato Assunção [ctb], Olaf Berke [ctb], Andrew Bernat [ctb], Guillaume Blanchet [ctb], Eric Blankmeyer [ctb], Marilia Carvalho [ctb], Bjarke Christensen [ctb], Yongwan Chun [ctb], Carsten Dormann [ctb], Stéphane Dray [ctb], Virgilio Gómez-Rubio [ctb], Martin Gubri [ctb], Rein Halbersma [ctb], Elias Krainski [ctb], Pierre Legendre [ctb], Nicholas Lewin-Koh [ctb], Angela Li [ctb], Hongfei Li [ctb], Jielai Ma [ctb], Abhirup Mallik [ctb, trl], Giovanni Millo [ctb], Werner Mueller [ctb], Hisaji Ono [ctb], Pedro Peres-Neto [ctb], Gianfranco Piras [ctb], Markus Reder [ctb], Jeff Sauer [ctb], Michael Tiefelsdorf [ctb], René Westerholt [ctb], Levi Wolf [ctb], Danlin Yu [ctb]
Initial release
2021-09-07

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.