Montreal FSA Scraping Part Dieux

August 14, 2016

(This article was first published on r - Brandon Bertelsen, and kindly contributed to R-bloggers)

Montreal FSA Scraping Part Dieux

Although we were able to scrape from the web the FSA we wanted, it was unfortunately not a complete list. Instead, let’s try another route using some data that’s been crowdsourced, namely the dataset or a subset provided by aggdata (as the table is 50mbs and I don’t need that level of accuracy).

Let’s install some packages first. You may need to install some system files for this to work:

sudo apt-get install libgeos-dev libgdal1-dev libproj-dev  

Now we can install the appropriate packages in R, if they aren’t already:


Now we can run a short script to find the FSA’s within the boundaries of our economic region.


# Canadian shapefiles
# select your own ( or 
# economic regions ( direct download
shp <- file.path("path/to/ger_000b11a_e.shp")  
map <- readShapePoly(shp, proj4string = CRS("+init=epsg:25832"))  
sel <- map$ERNAME == "Montérégie"

fsa_db <- read.csv("", fileEncoding = "Windows-1252") setNames(fsa_db, c("fsa","place","province","lat","long"))

region <- map[sel,]  
points <- data.frame(long=as.numeric(fsa_db$long),  
                     lat =as.numeric(fsa_db$lat),
                     id  =fsa_db$fsa, stringsAsFactors=F)

# We know that Monteregie is in JXX FSAs
points$yes <- substr(points$id,0,1) == "J"  
points <- points[points$yes,]

# Identify if FSA Long/Lat is within Economic Region

listing <- list()  
for(i in 1:nrow(points)) {  
  p1 <- points[i,1:2]
  sp2   <- SpatialPoints(p1,proj4string=CRS(proj4string(region)))
  listing[[i]] <- gContains(region,sp2)

points <- points[listing %>% unlist,]

ggplot(region, aes(x=long,y=lat,group=group))+  
  geom_path(colour="grey50") +
  geom_point(data=points,aes(x=long,y=lat,group=NULL, color=id), size=1) +
  coord_fixed() + theme(legend.position = "none")

Montreal FSA Scraping Part Dieux

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