moranbi.plot {bispdep}

Bivariate Moran scatterplot

Description

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

Usage

moranbi.plot(varY, varX, listw, zero.policy=NULL, spChk=NULL, labels=NULL,
 xlab=NULL, ylab=NULL, quiet=NULL, plot=TRUE, return_df=TRUE, ...)

Arguments

varX

a numeric vector of the same length as the neighbours list in listw with the values of the variable x

varY

a numeric vector of the same length as the neighbours list in listw with the values of the variable y

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 is TRUE, to suppress the plotting use FALSE

return_df

default TRUE, invisibly return a data.frame object; if FALSE invisibly return an influence measures object

...

other 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.

References

Matkan, A.A., Shahri, M. and Mirzaie, M., 2013, September. Bivariate Moran’s I and LISA to explore the crash risky locations in urban areas. In Proceedings of the Conference of Network-Association of European Researchers on Urbanisation in the South, Enschede, The Netherlands (pp. 12-14).

See Also

localmoran.bi, influence.measures

Examples

library(spdep)
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
col_nbq <- poly2nb(columbus)
a.lw <- nb2listw(col_nbq, style="W")

# Editing axis labels
CRIME <- as.vector(scale(columbus$CRIME))
INCOME <- as.vector(scale(columbus$INC))
moranbi.plot(CRIME,INCOME,quiet =FALSE,zero.policy =FALSE,listw=a.lw)
# Without editing the label of the axes
moranbi.plot(as.vector(scale(columbus$CRIME)),as.vector(scale(columbus$INC)),
             quiet =FALSE,zero.policy =FALSE,listw=a.lw)

# Moran scatterplot
mp <- moranbi.plot(CRIME,INCOME,quiet=FALSE,zero.policy=FALSE,listw=a.lw,
                   label=columbus$POLYID, plot = FALSE)

# Plot Moran Scatterplot in ggplot
if (require(ggplot2, quietly=TRUE)) {
# xname <- attr(mp, "xname")
ggplot2::ggplot(mp, aes(x=varY, y=wx)) + geom_point(shape=1) +
  geom_smooth(formula=y ~ x, method="lm") +
  geom_hline(yintercept=mean(mp$wx), lty=2) +
  geom_vline(xintercept=mean(mp$varY), lty=2) + theme_minimal() +
  geom_point(data=mp[mp$is_inf,], aes(x=varY, y=wx), shape=9) +
  geom_text(data=mp[mp$is_inf,], aes(x=varY, y=wx, label=labels, vjust=1.5)) +
  # xlab(xname) + ylab(paste0("Spatially lagged ", xname))
  xlab("Crime") + ylab("Spatially Lagged Income")
}