Distances and the Border Zone

The projection used in the analysis is NAD83

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'

region = data.frame(region = state.region, state_name = state.name)

stateboundary = USAboundaries::us_states() %>%
  filter(!name %in% c("Alaska", "Hawaii", "Puerto Rico"))

  
  

countryboundary = rnaturalearthdata::countries110 %>%
  st_as_sf() %>%
  filter(name %in% c("Mexico", "United States", "Canada"))
  

uscities = readr::read_csv("data/uscities.csv") %>%
  filter(!state_name %in% c("Alaska", "Hawaii", "Puerto Rico")) %>%
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
  st_filter(stateboundary, .predicate = st_intersects)

stateboundary = st_transform(stateboundary, eqdc)
countryboundary = st_transform(countryboundary, eqdc)
uscities = st_transform(uscities, eqdc)

border = st_union(stateboundary) %>%
  st_cast("MULTILINESTRING")

border_dis = uscities %>%
  mutate(dist_to_border = st_distance(uscities, border),
         dist_to_border = units::set_units(dist_to_border, "km"), 
         dist_to_border = units::drop_units(dist_to_border)) 
  
border_cities = border_dis %>%
  slice_max(dist_to_border, n = 5) %>%
  st_drop_geometry() %>%
  select(city, state = state_name, distance = dist_to_border) 
  

knitr::kable(border_cities,
             caption = "Farthest Cities to Border",
             col.names = c("City Name", "State", "Distance"))

Farthest Cities to Border
City Name	State	Distance
Dresden	Kansas	1012.398
Herndon	Kansas	1007.763
Hill City	Kansas	1005.143
Atwood	Kansas	1004.754
Jennings	Kansas	1003.646

state_b = st_combine(stateboundary) %>%
  st_cast("MULTILINESTRING")

state_dis = uscities %>%
  mutate(dist_to_state = st_distance(uscities, state_b),
         dist_to_state = units::set_units(dist_to_state, "km"),
         dist_to_state = units::drop_units(dist_to_state))
  
state_cities = state_dis %>%
  slice_max(dist_to_state, n = 5) %>%
  st_drop_geometry() %>%
  select(city, state = state_name, distance = dist_to_state)

knitr::kable(state_cities,
             caption = "Farthest Cities to the States",
             col.names = c("City Name", "State", "Distance"))

Farthest Cities to the States
City Name	State	Distance
Lampasas	Texas	308.9216
Kempner	Texas	302.5912
Bertram	Texas	302.5703
Harker Heights	Texas	298.8125
Florence	Texas	298.6804

mex_b = countryboundary %>%
  filter(name %in% c("Mexico")) %>%
  st_cast("MULTILINESTRING")


MEX_dis = uscities %>%
  mutate(dist_to_mex = st_distance(uscities, mex_b),
         dist_to_mex = units::set_units(dist_to_mex, "km"),
         dist_to_mex = units::drop_units(dist_to_mex))

mex_cities = MEX_dis %>%
  slice_max(dist_to_mex, n = 5) %>%
  st_drop_geometry() %>%
  select(city, state = state_name, distance = dist_to_mex)

knitr::kable(mex_cities,
             caption = "Farthest Cities to Mexico",
             col.names = c("City Name", "State", "Distance"))

Farthest Cities to Mexico
City Name	State	Distance
Caribou	Maine	3250.334
Presque Isle	Maine	3234.570
Calais	Maine	3134.348
Passamaquoddy Pleasant Point	Maine	3127.869
Eastport	Maine	3125.624

can_b = countryboundary %>%
  filter(name %in% c("Canada")) %>%
  st_cast("MULTILINESTRING")

CAN_dis = uscities %>%
  mutate(dist_to_can = st_distance(uscities, can_b),
         dist_to_can = units::set_units(dist_to_can, "km"),
         dist_to_can = units::drop_units(dist_to_can))

can_cities = CAN_dis %>%
  slice_max(dist_to_can, n = 5) %>%
  st_drop_geometry() %>%
  select(city, state = state_name, distance = dist_to_can)

knitr::kable(can_cities,
             caption = "Farthest Cities to Canada",
             col.names = c("City Name", "State", "Distance"))

Farthest Cities to Canada
City Name	State	Distance
Guadalupe Guerra	Texas	2206.455
Fronton	Texas	2204.790
Fronton Ranchettes	Texas	2202.118
Evergreen	Texas	2202.020
Ramos	Texas	2201.882

country_b = countryboundary %>%
  st_combine() %>%
  st_cast("MULTILINESTRING")



large_cities = uscities %>%
  st_filter(stateboundary, .predicate = st_intersects) %>%
  slice_max(population, n = 10) 




ggplot() +
  geom_sf(data = country_b, aes(), size = .1) +
  geom_sf(data = state_b, aes(), lty = 2) +
  geom_sf(data = border, color = "yellow") +
  geom_sf(data = large_cities, color = "navy") +
  ggrepel::geom_label_repel(data = large_cities,
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3) +
  labs(title = "Large Cities in the United States", 
       x = "Longitude", 
       y = "Latitude",
       caption = "based on https://simplemaps.com/data/us-cities") +
  theme_gray()

border_cities1 = border_dis %>%
  slice_max(dist_to_border, n = 5)

ggplot() +
  geom_sf(data = border, aes()) +
  geom_sf(data =border_dis , aes(col = dist_to_border)) +
  geom_sf(data = border_cities1, color = "yellow") +
  ggrepel::geom_label_repel(data = border_cities1,
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3) +
  labs(title = "Cities Farthest to the Border", 
       x = "Longitude", 
       y = "Latitude",
       caption = "based on https://simplemaps.com/data/us-cities") +
  theme_gray()

state_cities1 = state_dis %>%
  slice_max(dist_to_state, n = 5)

ggplot() +
  geom_sf(data = state_b, aes()) +
  geom_sf(data =state_dis , aes(col = dist_to_state)) +
  geom_sf(data = state_cities1, color = "yellow") +
  ggrepel::geom_label_repel(data = state_cities1,
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3) +
  labs(title = "Cities Farthest to the State Boundary", 
       x = "Longitude", 
       y = "Latitude",
       caption = "based on https://simplemaps.com/data/us-cities") +
  theme_gray()

MC_cities = st_join(MEX_dis, CAN_dis, by = "city") %>%
  mutate(equidist = abs(dist_to_mex - dist_to_can))

MC_l_cities = MC_cities %>%
  filter(equidist <= 100) %>%
  slice_max(population.x, n = 5)

ggplot() +
  geom_sf(data = state_b) +
  geom_sf(data = MC_cities, aes(col = equidist)) +
  gghighlight::gghighlight(equidist <= 100) +
  ggrepel::geom_label_repel(data = MC_l_cities,
                            aes(label = city.x, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3) +
  labs(title = "Large Cities Equally Fartherst to the Canada&Mexico Boundary", 
       x = "Longitude", 
       y = "Latitude",
       caption = "based on https://simplemaps.com/data/us-cities") +
  theme_gray()

total_pop = border_dis %>%
  mutate(totalpop = sum(population)) %>%
  select(id, totalpop) %>%
  st_drop_geometry()


danger_zone = border_dis %>%
  filter(dist_to_border <= 160) %>%
  mutate(danger_pop = sum(population)) %>%
  left_join(total_pop, by = "id") 

numberofcity = length(danger_zone$city)

The number of city in this 100 mile zone is 11931

stat_zone = danger_zone %>%  
  mutate(number = numberofcity) %>%
  select(number, danger_pop, totalpop) %>%
  st_drop_geometry() %>%
  mutate(percent = danger_pop / totalpop) %>%
  select(number, danger_pop, percent) %>%
  head(1)

knitr::kable(stat_zone,
             caption = "ACLU Estimate Is True",
             col.names = c("Cities Number", "Population In the Zone", "Percentage of Total Population"))

ACLU Estimate Is True
Cities Number	Population In the Zone	Percentage of Total Population
11931	259823474	0.6501426

big_city_national = danger_zone %>%
  slice_max(population, n = 10) 

bigcitynational = big_city_national %>%
  select(id) %>%
  st_drop_geometry()

big_city_state = danger_zone %>%
  group_by(state_id) %>%
  slice_max(population, n = 1) %>%
  anti_join(bigcitynational, by = "id")


ggplot() +
  geom_sf(data = state_b) +
  geom_sf(data = border_dis, aes(col = dist_to_border)) +
  gghighlight::gghighlight(dist_to_border <= 100) +
  scale_color_gradient(low = "darkred", high = "orange") +
  ggrepel::geom_label_repel(data = big_city_national,
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 5) +
  ggrepel::geom_label_repel(data = big_city_state,
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 2) +
  labs(title = "Large Cities in the Danger Zone", 
       x = "Longitude", 
       y = "Latitude",
       caption = "based on https://simplemaps.com/data/us-cities") +
  
  annotation_scale(
    bar_cols = c("grey60", "white"),
    text_family = "ArcherPro Book") +
  
  annotation_north_arrow(
    location = "bl", which_north = "true",
    pad_x = unit(0.4, "in"), pad_y = unit(0.4, "in"),
    style = ggspatial::north_arrow_nautical(
      fill = c("grey40", "white"),
      line_col = "grey20",
      text_family = "ArcherPro Book"
    )
  ) +
  theme_gray()

Chi Zhang

Distances and the Border Zone