title: “Geography 176A” author: “Van Jackson” subtitle: ‘Lab 03: Distances and the Border Zone’ output: html_document: theme: journal —
# SPDS
library(tidyverse)## ── Attaching packages ──────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.1
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(sf)## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1library(units)## udunits system database from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/units/share/udunits# Data
library(USAboundaries)
library(rnaturalearth)
# Visualization
library(gghighlight)
library(ggrepel)
library(knitr)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'
# remotes::install_github("ropensci/USAboundaries")
# remotes::install_github("ropensci/USAboundariesData")
# remotes::install_github("ropenscilabs/rnaturalearthdata")#Question 1
#1.1
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 is the base projection being used to map the North American vontinent for this lab.
#1.2
ContinentalUS = USAboundaries::us_states(resolution = "low") %>%
filter(name != "Hawaii") %>%
filter(name != "Alaska") %>%
filter(name != "Puerto Rico")
ContinentalUS <- st_transform(ContinentalUS, eqdc)
#st_crs(ContinentalUS)
#1.3
na = rnaturalearth::countries110 %>%
st_as_sf() %>%
filter(admin %in% c("Mexico", "Canada", "United States of America")) %>% st_transform(eqdc)
#1.4
cities = read_csv("../data/uscities.csv") %>%
st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
st_transform(eqdc) %>%
st_filter(ContinentalUS)## Parsed with column specification:
## cols(
## city = col_character(),
## city_ascii = col_character(),
## state_id = col_character(),
## state_name = col_character(),
## county_fips = col_double(),
## county_name = col_character(),
## county_fips_all = col_character(),
## county_name_all = col_character(),
## lat = col_double(),
## lng = col_double(),
## population = col_double(),
## density = col_double(),
## source = col_character(),
## military = col_logical(),
## incorporated = col_logical(),
## timezone = col_character(),
## ranking = col_double(),
## zips = col_character(),
## id = col_double()
## )#Question 2
#2.1
conus_string = st_union(ContinentalUS) %>%
st_cast("MULTILINESTRING")
na_string = st_combine(na) %>%
st_cast("MULTILINESTRING")
cities = cities %>%
mutate(dist_to_border = st_distance(cities, conus_string),
dist_to_border = units::set_units(dist_to_border, "km"),
dist_to_border = units::drop_units(dist_to_border))
furthest_from_border = cities %>%
slice_max(dist_to_border, n = 5) %>%
select(city, state_name, dist_to_border) %>%
st_drop_geometry()
kable(furthest_from_border, caption = "Furthest Cities From Border", col.names = c ("City", "State", "Distance"))| 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
conus_string2 = st_combine(ContinentalUS) %>%
st_cast("MULTILINESTRING")
cities = cities %>%
mutate(dist_to_state = st_distance(cities, conus_string2),
dist_to_state = units::set_units(dist_to_state, "km"),
dist_to_state = units::drop_units(dist_to_state))
furthest_from_state = cities %>%
slice_max(dist_to_state, n = 5) %>%
select(city, state_name, dist_to_state) %>%
st_drop_geometry()
kable(furthest_from_state, caption = "Furthest Cities From State Border", col.names = c ("City", "State", "Distance"))| 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
cities = cities %>%
mutate(dist_to_mex = st_distance(cities, filter(na, admin == "Mexico")),
dist_to_mex = units::set_units(dist_to_mex, "km"),
dist_to_mex = units::drop_units(dist_to_mex))
furthest_from_mex = cities %>%
slice_max(dist_to_mex, n = 5) %>%
select(city, state_name, dist_to_mex) %>%
st_drop_geometry()
kable(furthest_from_mex, caption = "Furthest Cities From Mexico", col.names = c ("City", "State", "Distance"))| 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
cities = cities %>%
mutate(dist_to_can = st_distance(cities, filter(na, admin == "Canada")),
dist_to_can = units::set_units(dist_to_can, "km"),
dist_to_can = units::drop_units(dist_to_can))
furthest_from_can = cities %>%
slice_max(dist_to_can, n = 5) %>%
select(city, state_name, dist_to_can) %>%
st_drop_geometry()
kable(furthest_from_can, caption = "Furthest Cities From Canada", col.names = c ("City", "State", "Distance"))| 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
top_ten = cities %>%
slice_max(population, n = 10)
ggplot() +
geom_sf(data = ContinentalUS) +
geom_sf(data = na_string) +
geom_sf(data = top_ten) +
geom_label_repel(
data = top_ten,
aes(label = city, geometry=geometry),
stat = "sf_coordinates") +
theme_linedraw()
#3.2
ggplot() +
geom_sf(data = cities, size = .1, aes(color = dist_to_border)) +
scale_color_gradient(low = "navy", high = "red") +
geom_sf(data= slice_max(cities, dist_to_border, n =5), col = "black") +
geom_label_repel(
data = slice_max(cities, dist_to_border, n =5),
aes(label = city, geometry = geometry),
stat= "sf_coordinates") +
theme_linedraw()
#3.3
ggplot() +
geom_sf(data = cities, size = .1, aes(color = dist_to_state)) +
scale_color_gradient(low = "navy", high = "red") +
geom_sf(data= slice_max(cities, dist_to_state, n =5), col = "black") +
geom_label_repel(
data = slice_max(cities, dist_to_state, n =5),
aes(label = city, geometry = geometry),
stat= "sf_coordinates") +
theme_linedraw()
#3.4
dist_mex_can = cities %>%
mutate(diff = abs(dist_to_mex - dist_to_can))
disttop5 = dist_mex_can %>%
filter(diff<=100) %>%
slice_max(population, n=5)
ggplot() +
geom_sf(data = dist_mex_can, size = .1, aes(col = diff)) +
scale_color_gradient(low = "navy", high = "red") +
gghighlight(diff<=100) +
geom_sf(data = disttop5, col = "green") +
geom_label_repel(
data = disttop5,
aes(label = city, geometry = geometry),
stat= "sf_coordinates") +
theme_linedraw()
#Question 4
#4.1
cities_100m = cities %>%
group_by(state_name) %>%
filter(dist_to_border <= 160.9) %>%
ungroup()#There are 12,302 cities within the 100 mile zone
popcount = cities_100m %>%
st_drop_geometry() %>%
summarise(population) %>%
sum()#There are 261547106 people in the zone
percent = popcount/328200000#79.7% of the population are in this zone
#Does it match? #No, they claim 2/3 of the population are in these zones when it is actually 79%
#4.2
ggplot() +
geom_sf(data = cities, size = .2) +
scale_color_gradient(low = "orange", high = "dark red") +
gghighlight(dist_to_border <= 160.9) 