#Install Libraries

# SPDS
library(tidyverse)
library(sf)
library(units)

# Data
library(USAboundaries)
library(rnaturalearth)

# Visualization
library(gghighlight)
library(ggrepel)
library(knitr)

Question 1

# Question 1.1 -Define a Projection 
eqdc = '+proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs'

This projection is named “eqdc” because it is an equal distance projection. It has a latitude of origin of 40 and a central meridian at -96. The latitude of the first standard parallel is at 20 and the latitude at the second standard parallel is 60. The false easting and false northing are both 0. The datum used is NAD83 and units are in meters.

# Question 1.2 - Get USA state boundaries 
data = USAboundaries::us_states(resolution = "low") %>%
  filter(!state_name %in% c("Puerto Rico", "Alaska", "Hawaii")) %>%
  st_transform(eqdc)

# Question 1.3 
country = rnaturalearth::countries110

country_sf = st_as_sf(country) %>%
  filter(admin %in% c("United States of America", "Mexico", "Canada")) %>%
  st_transform(eqdc)

# Question 1.4- City locations

cities = readr::read_csv("~/github/geog-176A-labs/data/uscities.csv")

cities_sf = data.frame(y = c(-121.7608, -119.7202), x = c(34.4285, 37.6861))
cities_sf = st_as_sf(cities, coords = c("lng", "lat"), crs = 4326) %>%
  filter(!state_name %in% c("Puerto Rico", "Alaska", "Hawaii")) 

cities_eqdc = st_transform(cities_sf, eqdc) %>% 
   select(city, state_name, population)

Question 2

# Union and Combine States
usa_geom2 = data$geometry

ca_u_ml = st_union(usa_geom2)  %>%
    st_cast("MULTILINESTRING")

ca_c_ml = st_combine(usa_geom2)  %>%
    st_cast("MULTILINESTRING")


# Distance to US Border
cities_eqdc = cities_eqdc %>%
  mutate(dist_border = st_distance(cities_eqdc, ca_u_ml)) %>%
  mutate(dist_border = units::set_units(dist_border, "km"))
# Distance to state borders
cities_eqdc = cities_eqdc %>%
  mutate(dist_state = st_distance(cities_eqdc, ca_c_ml)) %>%
  mutate(dist_state = units::set_units(dist_state, "km"))
# Distance to Canada
cities_eqdc = cities_eqdc %>%
  mutate(dist_Canada = st_distance(cities_eqdc, filter(country_sf, admin == "Canada"))) %>%
  mutate(dist_Canada = units::set_units(dist_Canada, "km"))
# Distance to Mexico
cities_eqdc = cities_eqdc %>%
  mutate(dist_Mexico = st_distance(cities_eqdc, filter(country_sf, admin == "Mexico"))) %>%
  mutate(dist_Mexico = units::set_units(dist_Mexico, "km"))


# Distances:
# Cities to US Border 
cities_eqdc %>% select(city,state_name, dist_border) %>% slice_max(dist_border, n = 5) %>% 
  st_drop_geometry() %>% 
knitr::kable(caption = "Cities Farthest from US Border",
             col.names = c("City", "State", "Distance to US Border"),
             format.args = list(big.mark = ",")) %>%
  kableExtra::kable_styling("striped", full_width = TRUE, font_size = 14)
Cities Farthest from US Border
City State Distance to US Border
Dresden Kansas 1,012.317 [km]
Herndon Kansas 1,007.750 [km]
Hill City Kansas 1,005.147 [km]
Atwood Kansas 1,004.734 [km]
Jennings Kansas 1,003.646 [km] 
# Cities to states
cities_eqdc %>% select(city,state_name, dist_state) %>% slice_max(dist_state, n = 5) %>% 
  st_drop_geometry() %>% 
knitr::kable(caption = "Cities Farthest from State Boundaries",
             col.names = c("City", "State", "Distance to State Boundary"),
             format.args = list(big.mark = ",")) %>%
  kableExtra::kable_styling("striped", full_width = TRUE, font_size = 14)
Cities Farthest from State Boundaries
City State Distance to State Boundary
Lampasas Texas 308.9216 [km]
Bertram Texas 302.8190 [km]
Kempner Texas 302.5912 [km]
Harker Heights Texas 298.8125 [km]
Florence Texas 298.6804 [km] 
# Distance to Mexico
cities_eqdc %>% select(city,state_name, dist_Mexico) %>% slice_max(dist_Mexico, n = 5) %>% 
  st_drop_geometry() %>% 
knitr::kable(caption = "Cities Farthest from Mexican Border",
             col.names = c("City", "State", "Distance to Mexican Border"),
             format.args = list(big.mark = ",")) %>%
  kableExtra::kable_styling("striped", full_width = TRUE, font_size = 14)
Cities Farthest from Mexican Border
City State Distance to Mexican Border
Caribou Maine 3,250.334 [km]
Presque Isle Maine 3,234.570 [km]
Calais Maine 3,134.348 [km]
Eastport Maine 3,125.624 [km]
Old Town Maine 3,048.366 [km] 
# Distance to Canada
cities_eqdc %>% select(city,state_name, dist_Canada) %>% slice_max(dist_Canada, n = 5) %>% 
  st_drop_geometry() %>% 
knitr::kable(caption = "Cities Farthest from Canadian Border",
             col.names = c("City", "State", "Distance to Canadian Border"),
             format.args = list(big.mark = ",")) %>%
  kableExtra::kable_styling("striped", full_width = TRUE, font_size = 14)
Cities Farthest from Canadian Border
City State Distance to Canadian Border
Guadalupe Guerra Texas 2,206.455 [km]
Sandoval Texas 2,205.641 [km]
Fronton Texas 2,204.784 [km]
Fronton Ranchettes Texas 2,202.118 [km]
Evergreen Texas 2,202.020 [km]

Question 3

# 3.1 - 10 Largest Cities

big_cities2 = cities_eqdc %>%
  slice_max(population, n = 10)

ggplot() +
  geom_sf(data = country_sf) +
  geom_sf(data = data, lty = "dashed", size = .5) +
  geom_sf(data = big_cities2, size = 1) +
  ggrepel::geom_label_repel(
    data = big_cities2,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 2) +
  labs(title = "Most Populous US Cities",
       x = " ",
       y = " ") +
  theme_minimal()

# 3.2- City Distance to Border 
farthest_cities = cities_eqdc %>%
  slice_max(dist_border, n = 5)

ggplot() +
  geom_sf(data = data) +
  geom_sf(data = cities_eqdc, aes(col = as.numeric(dist_border)), size = 0.1) +
  geom_sf(data = farthest_cities, size = 0.5, color = "red") +
  scale_color_gradient(low = "gray", high = "blue", name = "Distance (km)") +
   ggrepel::geom_label_repel(
    data = farthest_cities,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 2) +
    labs(title = "City Distance from National Border & Farthest Cities",
       x = " ",
       y = " ") + 
  theme_minimal() 

# 3.3 - City Distance from Nearest State 
farthest_cities_state = cities_eqdc %>%
  slice_max(dist_state, n = 5)

ggplot() +
  geom_sf(data = data) +
  geom_sf(data = farthest_cities_state, size = 1, color = "red") +
  geom_sf(data = cities_eqdc, aes(col = as.numeric(dist_state)), size = 0.1) +
  scale_color_gradient(low = "gray", high = "blue", name = "Distance (km)") +
   ggrepel::geom_label_repel(
    data = farthest_cities_state,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 2) +
     labs(title = "City Distance from Nearest State Border & Farthest Cities",
       x = " ",
       y = " ") + 
  theme_minimal() 

# 3.4- Equidistance Boundary from Mexico and Canada 

cities_eqdc = cities_eqdc %>%
  mutate(difference = abs(dist_Canada - dist_Mexico)) 
  

big_cities3 = cities_eqdc %>%
  filter(as.numeric(difference) < 100 ) %>%
  slice_max(population, n = 5)

ggplot() +
  geom_sf(data = data) +
  geom_sf(data = cities_eqdc, aes(col = as.numeric(difference)), size = 0.1) + 
  gghighlight::gghighlight(as.numeric(difference) < 100) +
  geom_sf(data = big_cities3, size = 0.5, color = "red") +
  scale_color_gradient(low = "gray", high = "blue", name = "Distance (km)") +
  ggrepel::geom_label_repel(
    data = big_cities3,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 2) +
     labs(title = "Cities Equidistant from Canadian and Mexican Borders (Within 100 km)",
         subtitle = "And Most Populous Cities",
       x = " ",
       y = " ") + 
  theme_minimal()

Question 4

# 4.1 - Quantifying Border Zone 
total_pop = cities_eqdc %>%
  select(population) %>%
  summarise(sum(population))

danger_zone = cities_eqdc %>%
  mutate(total_pop = sum(population)) %>%
  filter(as.numeric(dist_border) <= 160) %>%
  summarise(number_cities = n(), sum_pop = sum(population), percent = 100 * (sum_pop/total_pop[1])) %>%
  st_drop_geometry() 

  knitr::kable(danger_zone, caption = "Cities Within 100 Miles of State Border",
             col.names = c("Number of Cities", "Number of People", "Percent of Population"),
             format.args = list(big.mark = ",")) %>%
  kableExtra::kable_styling("striped", full_width = TRUE, font_size = 14)
Cities Within 100 Miles of State Border
Number of Cities Number of People Percent of Population
12,283 259,935,815 65.43979 
# 4.2 - Mapping Border Zone 
highest_pop_dangerzone = cities_eqdc %>%
  filter(as.numeric(dist_border) <= 160) %>%
  select(city, population) %>%
  slice_max(population, n = 10)
  
ggplot() +
  geom_sf(data = data) +
  geom_sf(data = cities_eqdc, aes(col = as.numeric(dist_border)), size = 0.1) +
  gghighlight::gghighlight(as.numeric(cities_eqdc$dist_border) <= 160) +
  scale_color_gradient(low = "orange", high = "dark red", name = "Distance (km)") +
  geom_sf(data = highest_pop_dangerzone, size = 1) +
  ggrepel::geom_label_repel(
    data = highest_pop_dangerzone,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 2) +
   labs(title = "Cities in 100 Mile Zone & Most Populous Cities",
       x = " ",
       y = " ") + 
  theme_minimal()

Extra Credit

highest_pop_state = cities_eqdc %>%
  filter(as.numeric(dist_border) <= 160) %>%
  select(city, state_name, population) %>%
  group_by(state_name) %>%
  filter(population == max(population))

ggplot() +
  geom_sf(data = data) +
  geom_sf(data = cities_eqdc, aes(col = as.numeric(dist_border)), size = 0.1) +
  gghighlight::gghighlight(as.numeric(cities_eqdc$dist_border) <= 160) +
  scale_color_gradient(low = "orange", high = "dark red", name = "Distance (km)") +
  geom_sf(data = highest_pop_state, size = 1) +
  ggrepel::geom_label_repel(
    data = highest_pop_state,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 2) +
  labs(title = "Cities in 100 Mile Zone & Most Populous City Per State",
       x = " ",
       y = " ") + 
  theme_minimal()