# Mucking around with maps, schools and ethnicity in NZ

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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 = data.frame([email protected]data, hhi[match([email protected]data[,"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:

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):

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

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