Mucking around with maps, schools and ethnicity in NZ

[This article was first published on Quantum Forest » rblogs, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I’ve been having a conversation for a while with @kamal_hothi and @aschiff on maps, schools, census, making NZ data available, etc. This post documents some basic steps I used for creating a map on ethnic diversity in schools at the census-area-unit level. This “el quicko” version requires 3 ingredients:

  • Census area units shape files (available from Statistics New Zealand for free here).
  • School directory (directory-school-current.csv available for free here).
  • R with some spatial packages (also free).

We’ll read the school directory data, aggregate ethnicity information to calculate the Herfindahl–Hirschman Index of diversity and then plot it.

# School directory
direc = read.csv('directory-school-current.csv', skip = 3)
 
# Total number of students for each ethnicity by Census Area Unit 
hhi = aggregate(cbind(European..Pakeha, Maori, Pasifika, Asian, MELAA, Other) ~ 
                Census.Area.Unit, data = direc, FUN = sum, na.rm = TRUE)
 
# Function to calculate 
index = function(x) {
  total = sum(x, na.rm = TRUE)
  frac = x/total
  h = sum(frac^2)
  hn = if(total > 1, (h - 1/total)/(1 - 1/total), 1)
  return(hn)
}
 
Calculate the index for each area
hhi$hn = apply(hhi[,2:7], 1, index)
 
# Write data to use in QGis later
write.csv(hhi, 'diversity-index-census-area-unit.csv', quote = FALSE,
          row.names = FALSE)

Then I moved to create a map in R, for the sake of it:

library(rgdal) # for readOGR
library(sp)    # for spplot
 
# Reading shapefile
cau = readOGR(dsn='/Users/lap44/Dropbox/research/2015/census/2014 Digital Boundaries Generlised Clipped',
              layer='AU2014_GV_Clipped')
 
# Joining with school ethnicity data (notice we refer to @data, as cau contains spatial info as well)
[email protected] = data.frame([email protected], 
                      hhi[match([email protected][,"AU2014_NAM"], hhi[,"Census.Area.Unit"]),])
 
# Limiting map to the area around Christchurch
spplot(cau, zcol = "hn", xlim = c(1540000, 1590000), 
       ylim= c(5163000, 5198000))

And we get a plot like this:

Ethnic diversity in schools at the Census Area Unit level (0 very diverse, 1 not diverse at all).

Ethnic diversity in schools at the Census Area Unit level (0 very diverse, 1 not diverse at all).

Just because it is Monday down under.

P.S. Using the diversity-index-census-area-unit.csv and joining it with the shapefile in QGIS one can get something prettier (I have to work on matching the color scales):

Similar map produced with point and click in QGIS.

Similar map produced with point and click in QGIS.

Map rendering is so much faster in QGIS than in R! Clearly the system has been optimized for this user case.

To leave a comment for the author, please follow the link and comment on their blog: Quantum Forest » rblogs.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)