Using data from the FFS and the Human Fertility Database we have recomputed desired fertility estimates using Rodriguez and Trussel (1981) method and simulated the Parity Progression Ratios to first births for women in 11 European countries.
Working paper soon to follow.
The idea behind spatial analysis is that space matters and near things are more similar: a variable measured in city A is (ideally) different from the same variable measured in city B. A simple way to get a feeling and to represent this hypothesis is through graphical visualization, usually a map(s).
However, when dealing with time series maps are cumbersome and with sometimes some information is lost, such as the national average or path convergence. Box plots are a simple yet very effective way to synthesize a lot of information in one graph. The following plot depicts TFR over a 30 years period for 910 Spanish areas with respect to the national average value (thick black line in the middle of the boxes).
p <- ggplot(dat, aes(x=factor(YEAR), y=dat$TFR))
p <- p + geom_boxplot()
p <- p + scale_y_continuous(limits=c(0,2.5)) + scale_x_discrete("YEAR", breaks=seq(1981,2011,by=5))
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..