Process NZ population data into grid points from SA2s

population_grid.R

# Create a bubble grid of New Zealand population using
# data for Statistical Areas 2
# For #30DayMapChallenge day 1 - points
# -- David Friggens, November 2021

library(readr)
library(sf)

# Get the centroids for SA2s
# From https://datafinder.stats.govt.nz
# There are various flavours on offer, and different download options
# I chose Geopackage :-)
geo_sa2 <-
  read_sf("statistical-area-2-2021-centroid-inside.gpkg") %>% 
  select(area_code = SA22021_V1_00,
         area_name = SA22021_V1_00_NAME,
         lat = LATITUDE,
         lon = LONGITUDE,
         geom)

# Get population for SA2s
# From https://nzdotstat.stats.govt.nz
# Again various output options - need to select to have code in
# the output as well as names; I chose "CSV" with tab separator
pop_sa2 <-
  read_tsv("population.tsv",
           col_types = "cccccciii") %>% 
  filter(YEAR == 2021L) %>% 
  select(area_code = AREA,
         population = `Value  Flags`)

# Join them together, round lat/lon to 1dp and sum
corners <- 
  geo_sa2 %>% 
  transmute(area_code,
            lat = round(lat, 1),
            lon = round(lon, 1)) %>% 
  st_drop_geometry() %>% 
  inner_join(pop_sa2) %>% 
  group_by(lat, lon) %>% 
  summarise(population = sum(population)) %>% 
  ungroup() %>% 
  filter(population > 0)

write_csv(corners, "population_corners.csv")

# # If you want to plot in R
# library(purrr)
# corners_geo <- 
#   corners %>% 
#   mutate(geom = 
#            list(lon, lat) %>% 
#            transpose() %>% 
#            map(unlist) %>% 
#            map(st_point)) %>% 
#   st_as_sf()
# 
# library(ggplot)
# ggplot(corners_g) +
#   geom_sf(aes(size = population),
#           alpha = 0.4,
#           show.legend = FALSE) +
#   coord_sf(datum = NA)