The Spatial side of Demography at EPC…

Come meet me at EPC to discuss Spatial Demography!

Thursday, 7 June 2018, 9:00-10:30 Session 1 Motherhood, Labor Market and Wages: Contextual Determinants of Childbearing in Spain: A Spatial Panel Study

Friday, 8 June 2018, 11:00-12:30 Session 65 Fertility Trends and ProspectsThe Geography of Fertility Rates in Low and Middle-Income Countries: Analysis of Cross-Sectional Surveys from 74 Countries

Moran plots in ggplot2

Moran plots are one of the many way to depict spatial autocorrelation:
moran.test(varofint,listw)
where “varofint” is the variable we are studying, “listw” a listwise neighbourhood matrix, and the function “moran.test” performs the Moran’s test (duh!) for spatial autocorrelation and is included in the spdep funtionality. The same plot can be done using ggplo2 library. Provided that we already have our listwise matrix of neighborhood relationships listw, we first define the variable and the lagged variable under study, computing their mean and saving them into a data frame (there are a lot of datasets you can find implemented in R: afcon, columbus, syracuse, just to cite a few). The purpose is to obtain something that looks like this (I have used my own *large* set of Spanish data to obtain it):

ggplot2.moranplot1

Upload your data. Here is Anselin (1995) data on African conflicts, afcon:

data(afcon)
varofint listw varlag var.name <- "Total Conflicts"
m.varofint m.varlag
and compute the local Moran's statistic using localmoran:

lisa
and save everything into a dataframe:
df

use these variables to derive the four sectors "High-High"(red), "Low-Low"(blue), "Low-High"(lightblue), "High-Low"(pink):
df$sector significance vec =df$m.varofint & df$varlag>=df$m.varlag]  df$sector[df$varofint<df$m.varofint & df$varlag<df$m.varlag]  df$sector[df$varofint<df$m.varofint & df$varlag>=df$m.varlag]  =df$m.varofint & df$varlag<df$m.varlag]

df$sec.data

df$sector.col[df$sec.data==1] <- "red"
df$sector.col[df$sec.data==2] <- "blue"
df$sector.col[df$sec.data==3] <- "lightblue"
df$sector.col[df$sec.data==4] <- "pink"
df$sector.col[df$sec.data==0] <- "white"

df$sizevar df$sizevar 0.1)
df$FILL df$BORDER
to get the ggplot graph:
p 0.05", "High-High", "Low-Low","Low-High","High-Low"))+
scale_x_continuous(name=var.name)+
scale_y_continuous(name=paste("Lagged",var.name))+
theme(axis.line=element_line(color="black"),
axis.title.x=element_text(size=20,face="bold",vjust=0.1),
axis.title.y=element_text(size=20,face="bold",vjust=0.1),
axis.text= element_text(colour="black", size=20, angle=0,face = "plain"),
plot.margin=unit(c(0,1.5,0.5,2),"lines"),
panel.background=element_rect(fill="white",colour="black"),
panel.grid=element_line(colour="grey"),
axis.text.x  = element_text(hjust=.5, vjust=.5),
axis.text.y  = element_text(hjust=1, vjust=1),
strip.text.x  = element_text(size = 20, colour ="black", angle = 0),
plot.title= element_text(size=20))+
stat_smooth(method="lm",se=F,colour="black", size=1)+
geom_vline(xintercept=m.varofint,colour="black",linetype="longdash")+
geom_hline(yintercept=m.varlag,colour="black",linetype="longdash")+
theme(legend.background =element_rect("white"))+
theme(legend.key=element_rect("white",colour="white"),
legend.text =element_text(size=20))

Check out the interactive shiny version on pracademic

Location, location, location! Why space matters in demography and why we should care.

Read my first contribution to the Demotrends blog! and don’t forget to like Demotrends either in facebook or twitter 🙂
Of course all graphics have been realized in R (maptools library and a bunch of others).
Location, location, location! Why space matters in demography and why we should care..