tourism_vulnerability.R

# https://www.mbie.govt.nz/immigration-and-tourism/tourism-research-and-data/tourism-data-releases/monthly-regional-tourism-estimates/regional-tourism-estimates/regional-tourism-estimates-key-pivot-table/
# Regional Tourism Estimates key pivot table

library(readxl)
library(tidyr)
library(dplyr)
library(ggplot2)
library(forcats)
tourism <- read_excel("rte-pivot-table-ye-march-2015.xlsx", sheet="Database")
TA_tour <- tourism %>% filter(YEMar == 2015) %>%
  group_by(Territorial_Authority) %>%
  summarise(domestic = sum(Spend * (Type == "Domestic")),
            international = sum(Spend * (Type == "International"))) %>%
  ungroup() %>%
  mutate(Territorial_Authority = gsub("Wanganui", "Whanganui", Territorial_Authority))

# MBIE
# Modelled Territorial Authority Gross Domestic Product
# 2019 release to march 2018
# https://www.mbie.govt.nz/business-and-employment/economic-development/regional-economic-development/modelled-territorial-authority-gross-domestic-product/2019-release/

GDP <- read_excel("modelled-territorial-authority-gdp-release-2019.xlsx", sheet="data download")
combined <- GDP %>% filter(AreaType=="TA",RGDP_industry=="Total GDP", Year==2018) %>% 
  select(Area, `GDP($m)`) %>% rename(Territorial_Authority = Area, gdp=`GDP($m)`) %>%
  inner_join(TA_tour, by = "Territorial_Authority")
g1 <- combined %>% mutate(vulnerablity_percent = 100 * international/gdp,
                          Territorial_Authority = fct_reorder(Territorial_Authority, vulnerablity_percent)) %>%
  arrange(Territorial_Authority) %>% 
  ggplot(aes(x=Territorial_Authority, y=vulnerablity_percent)) + geom_col() + coord_flip() + theme_minimal() + ggtitle("International vulnerability") + ylim(0,70)
g1  
g2 <- combined %>% mutate(vulnerablity_percent = 100 * domestic/gdp,
                          Territorial_Authority = fct_reorder(Territorial_Authority, vulnerablity_percent)) %>%
  arrange(Territorial_Authority) %>% 
  ggplot(aes(x=Territorial_Authority, y=vulnerablity_percent)) + geom_col() + coord_flip() + theme_minimal() + ggtitle("Domestic vulnerability")+ylim(0,70)
g2  
g3 <- combined %>% mutate(vulnerablity_percent = 100 * (domestic + international)/gdp,
                          Territorial_Authority = fct_reorder(Territorial_Authority, vulnerablity_percent)) %>%
  arrange(Territorial_Authority) %>% 
  ggplot(aes(x=Territorial_Authority, y=vulnerablity_percent)) + geom_col() + coord_flip() + theme_minimal() + ggtitle("Total vulnerability") +ylim(0,70)
g3