#30DayMapChallenge 2021 - Day 16 - Urban/Rural

hamilton_urban.R

# Show the differences around Hamilton in the change from
# New Zealand Standard Areas Classification 1992 to
# Statistical Standard for Geographic Areas 2018.
# For #30DayMapChallenge 2021 - Day 16 - Urban/Rural
#  -- David Friggens, November 2021


# Setup -------------------------------------------------------------------

library(glue)
library(readr)
library(dplyr)
library(stringr)
library(sf)
library(rmapshaper)
library(ggplot2)
library(ggtext)

FONT <- "Poppins"

COLOUR_NEW <- "black"
FILL_NEW <- "#ff6600"
COLOUR_OLD <- "#003352"
FILL_OLD <- "#003352"
COLOUR_LAND <- "#34a56f"
COLOUR_BG <- COLOUR_LAND
COLOUR_TEXT <- "black"


# Data - General --------------------------------------------------------------------

# All from https://datafinder.stats.govt.nz

# This is a rather general setup, with the intention of producing these for
# more (all?) areas. But just creating one now in the interests of time.

relations <- 
  read_csv("statsnz/geographic-areas-table-2021.csv") %>% 
  count(UA2017_code, UA2017_name,
        UR2021_code, UR2021_name) %>% 
  rename(old_code = UA2017_code,
         old_zone = UA2017_name,
         new_code = UR2021_code,
         new_name = UR2021_name) %>% 
  mutate(new_code = as.character(new_code))

geo_new <- 
  read_sf("statsnz/urban-rural-2021-clipped-generalised.gpkg") %>% 
  select(new_code = UR2021_V1_00,
         new_name = UR2021_V1_00_NAME,
         new_type = IUR2021_V1_00_NAME)
geo_nz <- 
  geo_new %>% 
  summarise(n = n())
geo_new <- 
  geo_new %>% 
  filter(new_type %>% str_detect("urban"))
geo_old <- 
  read_sf("statsnz/urban-area-2015-clipped.gpkg") %>% 
  select(old_code = UA2015_V1_00,
         old_zone = UA2015_V1_00_NAME,
         land_area = LAND_AREA_SQ_KM) %>% 
  left_join(
    tribble(~old_zone, ~old_name,
            "Northern Auckland Zone", "Auckland",
            "Western Auckland Zone", "Auckland",
            "Central Auckland Zone", "Auckland",
            "Southern Auckland Zone", "Auckland",
            "Hamilton Zone", "Hamilton",
            "Cambridge Zone", "Hamilton",
            "Te Awamutu Zone", "Hamilton",
            "Napier Zone", "Napier-Hastings",
            "Hastings Zone", "Napier-Hastings",
            "Upper Hutt Zone", "Wellington",
            "Lower Hutt Zone", "Wellington",
            "Porirua Zone", "Wellington",
            "Wellington Zone", "Wellington"),
    by = "old_zone"
  ) %>% 
  mutate(old_name = coalesce(old_name, old_zone)) %>% 
  filter(old_name %>% str_detect("Rural|Inlet|Inland", negate = TRUE))

# I'm not 100% sure on this - I'll verify later :-) - but I think that
# combining the three geographies before simplifying ensures that
# aligned boundries don't get shifted in different ways.

geo_urban <- 
  bind_rows(
    geo_nz %>% 
      transmute(gclass = "NZ",
                old_code = NA_character_,
                old_name = NA_character_,
                old_zone = NA_character_,
                new_code = NA_character_,
                new_name = NA_character_,
                new_type = NA_character_),
    geo_old %>% 
      transmute(gclass = "old",
                old_code,
                old_name,
                old_zone,
                new_code = NA_character_,
                new_name = NA_character_,
                new_type = NA_character_),
    geo_new %>% 
      transmute(gclass = "new",
                old_code = NA_character_,
                old_name = NA_character_,
                old_zone = NA_character_,
                new_code,
                new_name,
                new_type)
  ) %>% 
  ms_simplify(keep = 0.05, keep_shapes = TRUE)


# Data - Hamilton ----------------------------------------------------------------

hamilton_bbox <- 
  geo_urban %>% 
  filter(old_name %in% c("Hamilton")) %>% 
  st_buffer(2000) %>% 
  st_bbox()

hamilton_old <- 
  geo_urban %>% 
  filter(gclass == "old") %>% 
  ms_clip(bbox = hamilton_bbox)

hamilton_new <- 
  geo_urban %>% 
  filter(gclass == "new") %>% 
  ms_clip(bbox = hamilton_bbox)

# Kind of superfluous here since it's inland
# but necessary for coastal areas
hamilton_nz <- 
  geo_urban %>% 
  filter(gclass == "NZ") %>% 
  ms_clip(bbox = hamilton_bbox)


# Plot - Hamilton -------------------------------------------------------------------------

info_box <- 
  tibble(
    x = 1820500, y = 5788000,
    label = glue("The recent decision by StatsNZ to move from ",
                 "<span style='color:{FILL_OLD};'>**New Zealand Standard Areas Classification 1992**</span> ",
                 "to ",
                 "<span style='color:{FILL_NEW};'>**Statistical Standard for Geographic Areas 2018**</span> ",
                 "has given us more precise geographical definitions of towns and cities.")
  ) %>% 
  st_as_sf(coords = c("x", "y"),
           crs = st_crs(hamilton_old))

gg_hamilton <- 
  ggplot() +
  geom_sf(data = hamilton_nz,
          colour = COLOUR_LAND, size = 0.1,
          fill = COLOUR_LAND,
          alpha = 0.4) +
  geom_sf(data = hamilton_old,
          colour = COLOUR_OLD, size = 3,
          fill = FILL_OLD, alpha = 0.3) +
  geom_sf(data = hamilton_new,
          colour = COLOUR_NEW, size = 1,
          fill = FILL_NEW, alpha = 0.8) +
  geom_textbox(data = info_box,
               stat = "sf_coordinates",
               aes(geometry = geometry,
                   label = label),
               size = 12,
               width = unit(7, "inch")) +
  # Old Urban Areas
  geom_sf_label(data = hamilton_old %>% filter(old_code == "006"), # Hamilton Zone
                aes(label = old_zone), family = FONT, size = 18, colour = COLOUR_OLD, label.padding = unit(0.6, "lines"),
                nudge_x = 5000, nudge_y = 7000) +
  geom_sf_label(data = hamilton_old %>% filter(old_code == "007"), # Cambridge Zone
                aes(label = old_zone), family = FONT, size = 18, colour = COLOUR_OLD, label.padding = unit(0.6, "lines"),
                nudge_x = 4500, nudge_y = 3500) +
  geom_sf_label(data = hamilton_old %>% filter(old_code == "008"), # Te Awamutu Zone
                aes(label = old_zone), family = FONT, size = 18, colour = COLOUR_OLD, label.padding = unit(0.6, "lines"),
                nudge_x = 3000, nudge_y = 5000) +
  geom_sf_label(data = hamilton_old %>% filter(old_code == "231"), # Morrinsville
                aes(label = old_zone), family = FONT, size = 12, colour = COLOUR_OLD, label.padding = unit(0.6, "lines"),
                nudge_x = 0, nudge_y = -2700) +
  # New Urban
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1180"), # Hamilton
                aes(label = new_name), family = FONT, size = 18, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = 0, nudge_y = -10000) +
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1169"), # Ngaruawahia
                aes(label = new_name), family = FONT, size = 14, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = 7000, nudge_y = 0) +
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1186"), # Cambridge
                aes(label = new_name), family = FONT, size = 18, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = 7000, nudge_y = -3000) +
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1187"), # Te Awamutu
                aes(label = new_name), family = FONT, size = 18, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = -8600, nudge_y = -1500) +
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1189"), # Kihikihi
                aes(label = new_name), family = FONT, size = 18, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = -6500, nudge_y = -500) +
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1184"), # Pirongia
                aes(label = new_name), family = FONT, size = 12, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = -1000, nudge_y = 2500) +
  geom_sf_label(data = hamilton_new %>% filter(new_code == "1173"), # Morrinsville
                aes(label = new_name), family = FONT, size = 12, colour = FILL_NEW, label.padding = unit(0.6, "lines"),
                nudge_x = 0, nudge_y = -4000) +
  coord_sf(datum = NULL) +
  labs(title = glue("<br>New Zealand Urban Area Definitions<br>Hamilton: <span style='color:{FILL_OLD};'>old</span> and <span style='color:{FILL_NEW};'>new</span>"),
       subtitle = "#30DayMapChallenge 2021 - Day 16 - Urban/Rural",
       caption = "Map: David Friggens, @dakvid. Data: StatsNZ<br>") +
  theme_void() +
  theme(plot.background = element_rect(colour = COLOUR_BG, fill = COLOUR_BG),
        panel.background = element_rect(colour = COLOUR_BG, fill = COLOUR_BG),
        plot.title = element_markdown(colour = COLOUR_TEXT, family = FONT, size = 72, hjust = 0.5),
        plot.subtitle = element_text(colour = COLOUR_TEXT, family = FONT, size = 36, hjust = 0.5),
        plot.caption = element_markdown(colour = COLOUR_TEXT, family = FONT, size = 32, hjust = 1))

ggsave(plot = gg_hamilton,
       "Day_16/Day_16_hamilton_urban.png",
       height = 36, width = 24)