I spent some time exploring those numbers, here are some of the interesting graphs that I generated. Each graph has a link to its specific datasource in the legend. I will be publishing reproducible code that can generate those charts. Hopefully I can keep this updated.
These are numbers only, I leave you to jump to your own conclusions.
ATM withdrawal vs POS purchases by Residents
Geographical distributions of ATMs
Source: Banque du Liban - Data Series: Cement Deliveries
`df <- read.csv(“t51-12f.csv”)
df <- subset(df, df$in.tons != 0)
df$Period <- as.Date( df$Period, “%d/%m/%y”)
aggr <- aggregate(df$in.tons ~ format(df$Period, “%Y”), FUN = sum)
names(aggr) <- c(“Period” , “Year”)
write.csv (aggr, file= “t51-12f-annual-summary.csv”)