Libraries

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

# Data
library(USAboundaries)
library(rnaturalearth)

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

Question 1:

# North America Equidistant Conic (CONUS):

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'

(1.1: Define a Projection) The “eqdc” projection has a ORIGIN latitude of 40 and a longitude of -96, FIRST standard parallel of 20, SECOND standard parallel of 60, with false easting and northing. The datum is NAD83, units of measure are given in meters.

#1.2: Get USA state boundaries

s_border = USAboundaries::us_states(resolution = "low") %>%
  filter(!(state_name %in% c("Puerto Rico", "Alaska", "Hawaii"))) %>% st_transform(eqdc)

#1.3: Get country boundaries for Mexico, USA, and Canada

c_border = st_as_sf(rnaturalearth::countries110) %>%
  filter(admin %in% c("United States of America", "Mexico", "Canada")) %>%
  st_transform(eqdc)

#1.4: Get city locations from the CSV file

cities = readr::read_csv("uscities.csv") %>%
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
  filter(!(state_name %in% c("Puerto Rico", "Alaska", "Hawaii"))) %>%
  st_transform(eqdc)

Question 2:

#2.1: Distance to USA border (coastline or national) (km)

USAborder = st_union(s_border) %>%
  st_cast("MULTILINESTRING") 

cities = cities %>%
  mutate(dist_to_border = drop_units(set_units(st_distance(.,USAborder), "km")))

cities %>% select(city,state_name, dist_to_border) %>%
  slice_max(dist_to_border, n=5) %>%
  st_drop_geometry() %>%
  knitr::kable(caption = "Five Cities Farthest From USA Border", col.names = c("city", "state", "distance"))
Five Cities Farthest From USA Border
city state distance
Dresden Kansas 1012.317
Herndon Kansas 1007.750
Hill City Kansas 1005.147
Atwood Kansas 1004.734
Jennings Kansas 1003.646 
#2.2: Distance to States (km)

STATEborder = st_combine(s_border) %>%
  st_cast("MULTILINESTRING") 

cities = cities %>%
  mutate(dist_to_stborder = drop_units(set_units(st_distance(.,STATEborder), "km")))

cities %>% select(city,state_name, dist_to_stborder) %>%
  slice_max(dist_to_stborder, n=5) %>%
  st_drop_geometry() %>%
  knitr::kable(caption = "Five Cities Farthest From State Border", col.names = c("city", "state", "distance"))
Five Cities Farthest From State Border
city state distance
Lampasas Texas 308.9216
Bertram Texas 302.8190
Kempner Texas 302.5912
Harker Heights Texas 298.8125
Florence Texas 298.6804 
#2.3: Distance to Mexico (km)

MEXICOborder = c_border %>% filter (admin %in% c("Mexico")) %>% st_cast("MULTILINESTRING") 

cities = cities %>%
  mutate(dist_to_mexicoborder = drop_units(set_units(st_distance(.,MEXICOborder), "km")))

cities %>% select(city,state_name, dist_to_mexicoborder) %>%
  slice_max(dist_to_mexicoborder, n=5) %>%
  st_drop_geometry() %>%
  knitr::kable(caption = "Five Cities Farthest From Mexico Border", col.names = c("city", "state", "distance"))
Five Cities Farthest From Mexico Border
city state distance
Caribou Maine 3250.334
Presque Isle Maine 3234.570
Calais Maine 3134.348
Eastport Maine 3125.624
Old Town Maine 3048.366 
#2.4: Distance to Canada (km)

CANADAborder = c_border %>% filter (admin %in% c("Canada")) %>% st_cast("MULTILINESTRING") 

cities = cities %>%
  mutate(dist_to_canadaborder = drop_units(set_units(st_distance(.,CANADAborder), "km")))

cities %>% select(city,state_name, dist_to_canadaborder) %>%
  slice_max(dist_to_canadaborder, n=5) %>%
  st_drop_geometry() %>%
  knitr::kable(caption = "Five Cities Farthest From Canada Border", col.names = c("city", "state", "distance"))
Five Cities Farthest From Canada Border
city state distance
Guadalupe Guerra Texas 2206.455
Sandoval Texas 2205.641
Fronton Texas 2204.784
Fronton Ranchettes Texas 2202.118
Evergreen Texas 2202.020

Question 3:

#3.1: Data, 10 largest USA cities (by pop.)

largestcities = cities %>% slice_max(population, n=10) 

ggplot() +
  geom_sf(data = s_border, lty = "solid", size = .5) +
  geom_sf(data = largestcities, color = "green", size = .5) +
  ggrepel::geom_label_repel(
    data = largestcities,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 1.5) +
  labs(title = "Largest USA Cities by Population",
       x = " ",
       y = " ") + theme_bw()

ggsave(plot = last_plot(), file= "../img/dataLab-03.png")
#3.2: City Distance from the Border

distanceborder = cities %>% 
  slice_max(dist_to_border, n = 5)

ggplot() +
   geom_sf(data = s_border) +
  geom_sf(data = cities, 
          aes(col = dist_to_border), 
          size = .025) +
  geom_sf(data = distanceborder, color = "green",
          size = .5) + 
  scale_color_gradient(low = 'yellow', high = 'blue', name = 'Distance (km)') +
  ggrepel::geom_label_repel(
    data = distanceborder,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 1.5) +
  labs(title = "City Distance from the Border",
       x = " ",
       y = " ") + theme_bw()

ggsave(plot = last_plot(), file= "../img/cityLab-03.png")
#3.3: City Distance from Nearest State

distancestate = cities %>% 
  slice_max(dist_to_stborder, n = 5)

ggplot() +
  geom_sf(data = s_border) +
  geom_sf(data = cities, 
          aes(col = dist_to_stborder), 
          size = .025) +
  scale_color_gradient(low = 'yellow', high = 'blue', name = 'Distance (km)') +
    geom_sf(data = distancestate, color = "green",
          size = .5) +
  ggrepel::geom_label_repel(
    data = distancestate,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 1.5) +
  labs(title = "City Distance from Nearest State",
       x = " ",
       y = " ") + theme_bw()

ggsave(plot = last_plot(), file= "../img/stateLab-03.png")
#3.4: Equidistance Boundary from Mexico and Canada

cities = cities %>% mutate(dist_mexico_canada = abs(dist_to_mexicoborder - dist_to_canadaborder))
         
distanceequi = cities %>% filter(dist_mexico_canada < 100) %>% slice_max(population, n = 5)

ggplot() +
  geom_sf(data = s_border) +
  geom_sf(data = cities, 
          aes(col = dist_mexico_canada), 
          size = .025) +
  gghighlight((dist_mexico_canada) < 100) +
  scale_color_gradient(low = 'yellow', high = 'blue', name = 'Distance (km)') +
  geom_sf(data = distanceequi, color = "green",
          size = .5) +
  ggrepel::geom_label_repel(
    data = distanceequi,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 1.5) +
  labs(title = "Equidistance Boundary from Mexico and Canada",
       x = " ",
       y = " ") + theme_bw()

ggsave(plot = last_plot(), file= "../img/distanceequiLab-03.png")

Question 4:

#4.1: Quantifing Border Zone

totpopulation = cities %>%
  mutate(totpop = sum(population)) %>%
  select(id, totpop) %>%
  st_drop_geometry()


danger_zone = cities %>%
  filter(dist_to_border <= 160) %>%
  mutate(border_pop = sum(population)) %>%
  left_join(totpopulation, by = "id") 

numberofcity = length(danger_zone$city)

border_zone = danger_zone %>%  
  mutate(number = numberofcity) %>%
  select(number, border_pop, totpop) %>%
  st_drop_geometry() %>%
  mutate(percent = border_pop / totpop) %>%
  select(number, border_pop, percent) %>%
  head(1)


knitr::kable(border_zone, caption = 'Cities 100 Miles or Less to State Border', col.names = c("# Cities", "# People", "% of Population"))
Cities 100 Miles or Less to State Border
# Cities # People % of Population
12283 259935815 0.6543979

(4.1: Quantifing Border Zone) The ACLU states that, “nearly 2 out of 3 people live within the 100-mile border zone”. After quantifing the border zone, I found that 65.44% of population lives 100 miles or less from the border zone. These results support the ACLU claim and concern over the powers granted to Border Patrol agents.

#4.2: Mapping Border Zone

border_zone = cities %>% filter(dist_to_border <= 160) %>% slice_max(population, n=10)

ggplot()+
  geom_sf(data = s_border, size = 0.25) +
  geom_sf(data = cities, 
          aes(col = dist_to_border), 
          size = .025) +
  gghighlight(dist_to_border <= 160) +
  scale_color_gradient(low= 'dark red', high = "orange", "Border Distance (km)") +
   theme(legend.position="right") +
   geom_sf(data = border_zone, aes(size = population), col = "black") +
   ggrepel::geom_label_repel(data = border_zone, aes(label=city, geometry= geometry), stat = "sf_coordinates", size = 2) +
   labs(title = "USA Cities 100 mi (160 km) from Border", 
        x = " ", 
        y = " ", caption = "ACLU") + theme_bw()

  ggsave(plot = last_plot(), file="../img/mapLab-03.png")

Extra Credit:

#Extra Credit: 10 Most Populous Cities 100m (160 km)

populous_border_zone = cities %>% group_by(state_name) %>% filter(dist_to_border <= 160) %>%  slice_max(population, n=1)

ggplot()+
  geom_sf(data = s_border, size = 0.25) +
  geom_sf(data = cities, 
          aes(col = dist_to_border), 
          size = .025) +
  gghighlight(dist_to_border <= 160) +
  scale_color_gradient(low= 'dark red', high = "orange", "Border Distance (km)") +
   theme(legend.position="right") +
   geom_sf(data = border_zone, aes(size = population), col = "black") +
  geom_sf(data = populous_border_zone, size = .025) +
   ggrepel::geom_label_repel(data = populous_border_zone, aes(label=city, geometry= geometry), stat = "sf_coordinates", size = 2) +
   labs(title = "Largest Cities per State: 100 m (160 km) from Border", 
        x = " ", 
        y = " ", caption = "ACLU") + theme_bw()

  ggsave(plot = last_plot(), file="../img/extraLab-03.png")