I use R which is good for this sort of thing. Learnt it as part of my PhD.
The scraping of google maps against the PPR was carried out using code literally copied and pasted from here:
shanelynn.ie/massive-geocodi … ogle-maps/
(Thank god, because that would have taken me a few days to work out, I’m rusty).
This is an abbreviated version since the code contains everything from start to finish.
I’ll do the 2014 & 2013 Dublin files next, then maybe I can look at changes.
Really could do with getting square footage for each house but google doesn’t seem to keep cached daft entries as I was hoping.
#Didn’t use all of these, but I tend to stick em in
#Read Shape Files
#Take only the Dublin Part
#This long string looks complicated but it’s copy and pasted from one of the qualities contained in the ED file. Use summary(ED) to get it from any R shape file
proj4string <- “+proj=tmerc +lat_0=53.5 +lon_0=-8 +k=1.000035 +x_0=200000 +y_0=250000
+datum=ire65 +units=m +no_defs +ellps=mod_airy
#This takes the Dublin co-ordinates from ED and puts them into GPS so I can set limits for the map later
XY<- project(XY, proj4string,inverse=TRUE)
Source data (GPS co-ordinates at this point)
#Pick only the points that actually ended up in Dublin
data<-data[data$long>LongLim & data$long<LongLim & data$lat>LatLim & data$lat<LatLim,]
Transform data to Irish GRID
XY<- project(XY, proj4string)
XY <- SpatialPoints(XY,proj4string=CRS(proj4string(ED)))
#Link the points in the list XY with their appropriate GEOGID (this is the electoral district) and add that Data to the datafile.
#The ED file also has the number of dwellings per ED for 2011 which I used for turnover
The other relevant bits are probably generating the colour ramp:
(at this point I had created a data frame called summary which pulled only the relevant columns from data, bad practice to use a function name but twas done)
and adding the colours to the original ED shape file so that they can be plotted
ED@data<-data.frame(ED@data, summary[match(ED@data$GEOGID, summary$GEOGID),])