data-raw/load_census_api.R
In CMAP-REPOS/cmapgeo: R-Friendly Geodata for the Chicago Region
library(tidyverse)
devtools::load_all()
# Set common parameters
BASE_YEAR <- 2023 # TIGER/Line vintage to use by default
STATE <- "17" # Illinois
COUNTIES_7CO <- c("031", "043", "089", "093", "097", "111", "197") # CMAP 7
COUNTIES_MPO <- c(COUNTIES_7CO, "063", "037") # CMAP 7, plus Grundy and DeKalb
# Get CMAP counties for spatial filtering (not saved) -- includes Lake Michigan
temp_cmap_sf <- tigris::counties(state = STATE, year = BASE_YEAR) %>%
filter(COUNTYFP %in% COUNTIES_7CO) %>%
sf::st_transform(cmap_crs) %>%
select(GEOID)
# Get Lake Michigan tracts for erasing
temp_lakemich_sf <- tigris::tracts(state = "17", year = BASE_YEAR) %>%
bind_rows(tigris::tracts(state = "18", year = BASE_YEAR)) %>%
bind_rows(tigris::tracts(state = "55", year = BASE_YEAR)) %>%
filter(TRACTCE == "990000") %>% # Water tracts only
sf::st_transform(cmap_crs) %>%
rmapshaper::ms_dissolve()
# Define helper function to determine overlaps with CMAP counties
intersects_cmap <- function(in_sf) {
apply(sf::st_overlaps(in_sf, temp_cmap_sf, sparse = FALSE), 1, any) |
apply(sf::st_covers(in_sf, temp_cmap_sf, sparse = FALSE), 1, any) |
apply(sf::st_covered_by(in_sf, temp_cmap_sf, sparse = FALSE), 1, any)
}
# Process Census counties
keep_counties <- unique(unlist(county_fips_codes))
state_fips <- unique(substr(keep_counties, 1, 2))
county_sf <- tigris::counties(state = state_fips, year = BASE_YEAR) %>%
filter(GEOID %in% keep_counties) %>%
sf::st_transform(cmap_crs) %>%
rmapshaper::ms_erase(temp_lakemich_sf) %>% # Erase Lake Michigan
rename(geoid_county = GEOID,
county = NAME) %>%
mutate(state = case_when(STATEFP == "17" ~ "IL",
STATEFP == "18" ~ "IN",
STATEFP == "55" ~ "WI"),
cmap = geoid_county %in% county_fips_codes$cmap,
msa = geoid_county %in% county_fips_codes$msa,
travel_model = geoid_county %in% unlist(county_fips_codes[c("cmap", "xil", "xin", "xwi")]),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_county, county, state, cmap, msa, travel_model, sqmi) %>%
arrange(geoid_county)
# Process Census county subdivisions (a.k.a. political townships)
township_sf <- tigris::county_subdivisions(state = STATE, county = COUNTIES_MPO, year = BASE_YEAR) %>%
filter(COUSUBFP != "00000", # Exclude Lake Michigan "townships"
COUNTYFP %in% COUNTIES_7CO | NAME %in% c("Aux Sable", "Sandwich", "Somonauk")) %>%
sf::st_transform(cmap_crs) %>%
rename(geoid_cousub = GEOID,
township = NAME) %>%
mutate(county_fips = paste0(STATEFP, COUNTYFP),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_cousub, township, county_fips, sqmi) %>%
arrange(geoid_cousub)
# Process Census places (a.k.a. municipalities)
municipality_sf <- tigris::places(state = STATE, year = BASE_YEAR) %>%
filter(!str_detect(NAMELSAD, " CDP")) %>% # Incorporated places only
sf::st_transform(cmap_crs) %>%
filter(intersects_cmap(.)) %>% # Restrict to CMAP region
rename(geoid_place = GEOID,
municipality = NAME) %>%
mutate(sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_place, municipality, sqmi) %>%
arrange(geoid_place)
# Process Congressional Districts (U.S. House of Representatives)
################################################################
#### Code to manually download congressional districts due to issue with tigris
#### functionality on congressional districts. All lines involving downloading
#### temp files and extracting can be removed when tigris is updated.
# Set temp dir to download and extract ZIP files into
congress_unzip_dir <- tempdir()
# URL for current shapefile
congress_zip_url <- c("https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/CD/tl_rd22_17_cd118.zip")
# Create temporary file, download, and unzip to temp directory before unlinking
congress_zip <- tempfile()
download.file(congress_zip_url, congress_zip)
unzip(congress_zip, exdir = congress_unzip_dir)
unlink(congress_zip)
#################################################################
#### Commnted code below can be used when tigris is updated to allow for 118th
#### Congress.
#### The first line of this function should be replaced with the commented
#### line below when tigris is updated
######################################
congress_sf <- sf::read_sf(paste(congress_unzip_dir,"tl_rd22_17_cd118.shp",sep="\\")) %>%
######################################
# congress_sf <- tigris::congressional_districts(year = BASE_YEAR) %>%
######################################
#### The commented line below should be uncommented and replace the subsequent
#### line when tigris is updated
######################################
# filter(STATEFP == STATE, LSAD == "C2") %>%
######################################
filter(STATEFP20 == STATE, LSAD20 == "C2") %>%
######################################
sf::st_transform(cmap_crs) %>%
mutate(dist_num = as.integer(CD118FP),
dist_name = paste("Illinois",
nombre::nom_ord(dist_num, max_n = 0),
"Congressional District"),
dist_name_short = paste0("IL-", dist_num),
cmap = intersects_cmap(.),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(dist_num, dist_name, dist_name_short, cmap, sqmi) %>%
arrange(dist_num)
# Process IL House Districts (Illinois General Assembly)
ilga_house_sf <- tigris::state_legislative_districts(state = STATE, house = "lower", year = BASE_YEAR) %>%
filter(LSAD == "LL") %>%
sf::st_transform(cmap_crs) %>%
rename(dist_name = NAMELSAD) %>%
mutate(dist_num = as.integer(SLDLST),
cmap = intersects_cmap(.),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(dist_num, dist_name, cmap, sqmi) %>%
arrange(dist_num)
# Process IL Senate Districts (Illinois General Assembly)
ilga_senate_sf <- tigris::state_legislative_districts(state = STATE, house = "upper", year = BASE_YEAR) %>%
filter(LSAD == "LU") %>%
sf::st_transform(cmap_crs) %>%
rename(dist_name = NAMELSAD) %>%
mutate(dist_num = as.integer(SLDUST),
cmap = intersects_cmap(.),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(dist_num, dist_name, cmap, sqmi) %>%
arrange(dist_num)
# Process Public Use Microddata Areas (PUMAs)
# Note: Now includes 2020 boundaries, as of 2022 TIGER/Line
puma_sf <- tigris::pumas(state = STATE, year = BASE_YEAR) %>%
sf::st_transform(cmap_crs) %>%
filter(intersects_cmap(.)) %>% # Restrict to CMAP region
rmapshaper::ms_erase(temp_lakemich_sf) %>% # Erase Lake Michigan
rename(geoid_puma = GEOID20,
name = NAMELSAD20) %>%
mutate(sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_puma, name, sqmi) %>%
arrange(geoid_puma)
# Process ZIP Code Tabulation Areas (ZCTAs)
zcta_sf <- tigris::zctas(starts_with = "6", year = BASE_YEAR) %>%
sf::st_transform(cmap_crs) %>%
filter(intersects_cmap(.)) %>% # Restrict to CMAP region
rename(geoid_zcta = GEOID20) %>%
mutate(sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_zcta, sqmi) %>%
arrange(geoid_zcta)
# Process IDOT regions
county_district = c(
`17031`="D1", `17089`="D1", `17197`="D1", `17043`="D1", `17097`="D1", `17111`="D1",
`17161`="D2", `17103`="D2", `17177`="D2", `17085`="D2", `17073`="D2", `17201`="D2", `17141`="D2", `17007`="D2", `17195`="D2", `17015`="D2",
`17011`="D3", `17093`="D3", `17037`="D3", `17075`="D3", `17053`="D3", `17091`="D3", `17105`="D3", `17063`="D3", `17099`="D3",
`17131`="D4", `17057`="D4", `17175`="D4", `17155`="D4", `17109`="D4", `17203`="D4", `17179`="D4", `17123`="D4", `17187`="D4", `17143`="D4", `17095`="D4", `17071`="D4",
`17113`="D5", `17147`="D5", `17041`="D5", `17039`="D5", `17183`="D5", `17019`="D5", `17045`="D5",
`17067`="D6", `17009`="D6", `17135`="D6", `17001`="D6", `17171`="D6", `17129`="D6", `17169`="D6", `17125`="D6", `17149`="D6", `17017`="D6", `17167`="D6", `17137`="D6", `17117`="D6", `17107`="D6", `17021`="D6",
`17025`="D7", `17185`="D7", `17029`="D7", `17049`="D7", `17035`="D7", `17033`="D7", `17115`="D7", `17023`="D7", `17159`="D7", `17191`="D7", `17173`="D7", `17051`="D7", `17139`="D7", `17079`="D7", `17101`="D7", `17047`="D7",
`17005`="D8", `17083`="D8", `17163`="D8", `17189`="D8", `17027`="D8", `17061`="D8", `17157`="D8", `17013`="D8", `17121`="D8", `17119`="D8", `17133`="D8",
`17151`="D9", `17055`="D9", `17059`="D9", `17065`="D9", `17193`="D9", `17181`="D9", `17199`="D9", `17069`="D9", `17127`="D9", `17145`="D9", `17087`="D9", `17153`="D9", `17003`="D9", `17165`="D9", `17081`="D9", `17077`="D9"
)
idot_sf <- tigris::counties(state = STATE, year = BASE_YEAR) %>%
sf::st_transform(cmap_crs) %>%
rmapshaper::ms_erase(temp_lakemich_sf) %>% # Erase Lake Michigan
mutate(district = recode(GEOID, !!!county_district),
region = case_when(district == "D1" ~ "R1",
district %in% c("D2", "D3") ~ "R2",
district %in% c("D4", "D5") ~ "R3",
district %in% c("D6", "D7") ~ "R4",
district %in% c("D8", "D9") ~ "R5"),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
group_by(district, region) %>%
summarize(sqmi = sum(sqmi), .groups = "drop")
# Process 2020 Census geographies (block, block group, tract)
tract_sf <- tigris::tracts(state = STATE, county = COUNTIES_7CO, year = BASE_YEAR) %>%
filter(TRACTCE != "990000") %>% # Exclude Lake Michigan tracts
sf::st_transform(cmap_crs) %>%
rename(geoid_tract = GEOID) %>%
mutate(county_fips = paste0(STATEFP, COUNTYFP),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_tract, county_fips, sqmi) %>%
arrange(geoid_tract)
blockgroup_sf <- tigris::block_groups(state = STATE, county = COUNTIES_7CO, year = BASE_YEAR) %>%
filter(TRACTCE != "990000") %>% # Exclude Lake Michigan tracts
sf::st_transform(cmap_crs) %>%
rename(geoid_blkgrp = GEOID) %>%
mutate(county_fips = paste0(STATEFP, COUNTYFP),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_blkgrp, county_fips, sqmi) %>%
arrange(geoid_blkgrp)
block_sf <- tigris::blocks(state = STATE, county = COUNTIES_7CO, year = BASE_YEAR) %>%
filter(TRACTCE20 != "990000") %>% # Exclude Lake Michigan tracts
sf::st_transform(cmap_crs) %>%
rename(geoid_block = GEOID20) %>%
mutate(county_fips = paste0(STATEFP20, COUNTYFP20),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_block, county_fips, sqmi) %>%
arrange(geoid_block)
# Save processed data to package's data dir
usethis::use_data(county_sf, overwrite = TRUE)
usethis::use_data(township_sf, overwrite = TRUE)
usethis::use_data(municipality_sf, overwrite = TRUE)
usethis::use_data(congress_sf, overwrite = TRUE)
usethis::use_data(ilga_house_sf, overwrite = TRUE)
usethis::use_data(ilga_senate_sf, overwrite = TRUE)
usethis::use_data(puma_sf, overwrite = TRUE)
usethis::use_data(zcta_sf, overwrite = TRUE)
usethis::use_data(idot_sf, overwrite = TRUE)
usethis::use_data(tract_sf, overwrite = TRUE)
usethis::use_data(blockgroup_sf, overwrite = TRUE)
usethis::use_data(block_sf, overwrite = TRUE)
CMAP-REPOS/cmapgeo documentation built on Jan. 28, 2024, 10:16 p.m.
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library(tidyverse)
devtools::load_all()
# Set common parameters
BASE_YEAR <- 2023 # TIGER/Line vintage to use by default
STATE <- "17" # Illinois
COUNTIES_7CO <- c("031", "043", "089", "093", "097", "111", "197") # CMAP 7
COUNTIES_MPO <- c(COUNTIES_7CO, "063", "037") # CMAP 7, plus Grundy and DeKalb
# Get CMAP counties for spatial filtering (not saved) -- includes Lake Michigan
temp_cmap_sf <- tigris::counties(state = STATE, year = BASE_YEAR) %>%
filter(COUNTYFP %in% COUNTIES_7CO) %>%
sf::st_transform(cmap_crs) %>%
select(GEOID)
# Get Lake Michigan tracts for erasing
temp_lakemich_sf <- tigris::tracts(state = "17", year = BASE_YEAR) %>%
bind_rows(tigris::tracts(state = "18", year = BASE_YEAR)) %>%
bind_rows(tigris::tracts(state = "55", year = BASE_YEAR)) %>%
filter(TRACTCE == "990000") %>% # Water tracts only
sf::st_transform(cmap_crs) %>%
rmapshaper::ms_dissolve()
# Define helper function to determine overlaps with CMAP counties
intersects_cmap <- function(in_sf) {
apply(sf::st_overlaps(in_sf, temp_cmap_sf, sparse = FALSE), 1, any) |
apply(sf::st_covers(in_sf, temp_cmap_sf, sparse = FALSE), 1, any) |
apply(sf::st_covered_by(in_sf, temp_cmap_sf, sparse = FALSE), 1, any)
}
# Process Census counties
keep_counties <- unique(unlist(county_fips_codes))
state_fips <- unique(substr(keep_counties, 1, 2))
county_sf <- tigris::counties(state = state_fips, year = BASE_YEAR) %>%
filter(GEOID %in% keep_counties) %>%
sf::st_transform(cmap_crs) %>%
rmapshaper::ms_erase(temp_lakemich_sf) %>% # Erase Lake Michigan
rename(geoid_county = GEOID,
county = NAME) %>%
mutate(state = case_when(STATEFP == "17" ~ "IL",
STATEFP == "18" ~ "IN",
STATEFP == "55" ~ "WI"),
cmap = geoid_county %in% county_fips_codes$cmap,
msa = geoid_county %in% county_fips_codes$msa,
travel_model = geoid_county %in% unlist(county_fips_codes[c("cmap", "xil", "xin", "xwi")]),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_county, county, state, cmap, msa, travel_model, sqmi) %>%
arrange(geoid_county)
# Process Census county subdivisions (a.k.a. political townships)
township_sf <- tigris::county_subdivisions(state = STATE, county = COUNTIES_MPO, year = BASE_YEAR) %>%
filter(COUSUBFP != "00000", # Exclude Lake Michigan "townships"
COUNTYFP %in% COUNTIES_7CO | NAME %in% c("Aux Sable", "Sandwich", "Somonauk")) %>%
sf::st_transform(cmap_crs) %>%
rename(geoid_cousub = GEOID,
township = NAME) %>%
mutate(county_fips = paste0(STATEFP, COUNTYFP),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_cousub, township, county_fips, sqmi) %>%
arrange(geoid_cousub)
# Process Census places (a.k.a. municipalities)
municipality_sf <- tigris::places(state = STATE, year = BASE_YEAR) %>%
filter(!str_detect(NAMELSAD, " CDP")) %>% # Incorporated places only
sf::st_transform(cmap_crs) %>%
filter(intersects_cmap(.)) %>% # Restrict to CMAP region
rename(geoid_place = GEOID,
municipality = NAME) %>%
mutate(sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_place, municipality, sqmi) %>%
arrange(geoid_place)
# Process Congressional Districts (U.S. House of Representatives)
################################################################
#### Code to manually download congressional districts due to issue with tigris
#### functionality on congressional districts. All lines involving downloading
#### temp files and extracting can be removed when tigris is updated.
# Set temp dir to download and extract ZIP files into
congress_unzip_dir <- tempdir()
# URL for current shapefile
congress_zip_url <- c("https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/CD/tl_rd22_17_cd118.zip")
# Create temporary file, download, and unzip to temp directory before unlinking
congress_zip <- tempfile()
download.file(congress_zip_url, congress_zip)
unzip(congress_zip, exdir = congress_unzip_dir)
unlink(congress_zip)
#################################################################
#### Commnted code below can be used when tigris is updated to allow for 118th
#### Congress.
#### The first line of this function should be replaced with the commented
#### line below when tigris is updated
######################################
congress_sf <- sf::read_sf(paste(congress_unzip_dir,"tl_rd22_17_cd118.shp",sep="\\")) %>%
######################################
# congress_sf <- tigris::congressional_districts(year = BASE_YEAR) %>%
######################################
#### The commented line below should be uncommented and replace the subsequent
#### line when tigris is updated
######################################
# filter(STATEFP == STATE, LSAD == "C2") %>%
######################################
filter(STATEFP20 == STATE, LSAD20 == "C2") %>%
######################################
sf::st_transform(cmap_crs) %>%
mutate(dist_num = as.integer(CD118FP),
dist_name = paste("Illinois",
nombre::nom_ord(dist_num, max_n = 0),
"Congressional District"),
dist_name_short = paste0("IL-", dist_num),
cmap = intersects_cmap(.),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(dist_num, dist_name, dist_name_short, cmap, sqmi) %>%
arrange(dist_num)
# Process IL House Districts (Illinois General Assembly)
ilga_house_sf <- tigris::state_legislative_districts(state = STATE, house = "lower", year = BASE_YEAR) %>%
filter(LSAD == "LL") %>%
sf::st_transform(cmap_crs) %>%
rename(dist_name = NAMELSAD) %>%
mutate(dist_num = as.integer(SLDLST),
cmap = intersects_cmap(.),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(dist_num, dist_name, cmap, sqmi) %>%
arrange(dist_num)
# Process IL Senate Districts (Illinois General Assembly)
ilga_senate_sf <- tigris::state_legislative_districts(state = STATE, house = "upper", year = BASE_YEAR) %>%
filter(LSAD == "LU") %>%
sf::st_transform(cmap_crs) %>%
rename(dist_name = NAMELSAD) %>%
mutate(dist_num = as.integer(SLDUST),
cmap = intersects_cmap(.),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(dist_num, dist_name, cmap, sqmi) %>%
arrange(dist_num)
# Process Public Use Microddata Areas (PUMAs)
# Note: Now includes 2020 boundaries, as of 2022 TIGER/Line
puma_sf <- tigris::pumas(state = STATE, year = BASE_YEAR) %>%
sf::st_transform(cmap_crs) %>%
filter(intersects_cmap(.)) %>% # Restrict to CMAP region
rmapshaper::ms_erase(temp_lakemich_sf) %>% # Erase Lake Michigan
rename(geoid_puma = GEOID20,
name = NAMELSAD20) %>%
mutate(sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_puma, name, sqmi) %>%
arrange(geoid_puma)
# Process ZIP Code Tabulation Areas (ZCTAs)
zcta_sf <- tigris::zctas(starts_with = "6", year = BASE_YEAR) %>%
sf::st_transform(cmap_crs) %>%
filter(intersects_cmap(.)) %>% # Restrict to CMAP region
rename(geoid_zcta = GEOID20) %>%
mutate(sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_zcta, sqmi) %>%
arrange(geoid_zcta)
# Process IDOT regions
county_district = c(
`17031`="D1", `17089`="D1", `17197`="D1", `17043`="D1", `17097`="D1", `17111`="D1",
`17161`="D2", `17103`="D2", `17177`="D2", `17085`="D2", `17073`="D2", `17201`="D2", `17141`="D2", `17007`="D2", `17195`="D2", `17015`="D2",
`17011`="D3", `17093`="D3", `17037`="D3", `17075`="D3", `17053`="D3", `17091`="D3", `17105`="D3", `17063`="D3", `17099`="D3",
`17131`="D4", `17057`="D4", `17175`="D4", `17155`="D4", `17109`="D4", `17203`="D4", `17179`="D4", `17123`="D4", `17187`="D4", `17143`="D4", `17095`="D4", `17071`="D4",
`17113`="D5", `17147`="D5", `17041`="D5", `17039`="D5", `17183`="D5", `17019`="D5", `17045`="D5",
`17067`="D6", `17009`="D6", `17135`="D6", `17001`="D6", `17171`="D6", `17129`="D6", `17169`="D6", `17125`="D6", `17149`="D6", `17017`="D6", `17167`="D6", `17137`="D6", `17117`="D6", `17107`="D6", `17021`="D6",
`17025`="D7", `17185`="D7", `17029`="D7", `17049`="D7", `17035`="D7", `17033`="D7", `17115`="D7", `17023`="D7", `17159`="D7", `17191`="D7", `17173`="D7", `17051`="D7", `17139`="D7", `17079`="D7", `17101`="D7", `17047`="D7",
`17005`="D8", `17083`="D8", `17163`="D8", `17189`="D8", `17027`="D8", `17061`="D8", `17157`="D8", `17013`="D8", `17121`="D8", `17119`="D8", `17133`="D8",
`17151`="D9", `17055`="D9", `17059`="D9", `17065`="D9", `17193`="D9", `17181`="D9", `17199`="D9", `17069`="D9", `17127`="D9", `17145`="D9", `17087`="D9", `17153`="D9", `17003`="D9", `17165`="D9", `17081`="D9", `17077`="D9"
)
idot_sf <- tigris::counties(state = STATE, year = BASE_YEAR) %>%
sf::st_transform(cmap_crs) %>%
rmapshaper::ms_erase(temp_lakemich_sf) %>% # Erase Lake Michigan
mutate(district = recode(GEOID, !!!county_district),
region = case_when(district == "D1" ~ "R1",
district %in% c("D2", "D3") ~ "R2",
district %in% c("D4", "D5") ~ "R3",
district %in% c("D6", "D7") ~ "R4",
district %in% c("D8", "D9") ~ "R5"),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
group_by(district, region) %>%
summarize(sqmi = sum(sqmi), .groups = "drop")
# Process 2020 Census geographies (block, block group, tract)
tract_sf <- tigris::tracts(state = STATE, county = COUNTIES_7CO, year = BASE_YEAR) %>%
filter(TRACTCE != "990000") %>% # Exclude Lake Michigan tracts
sf::st_transform(cmap_crs) %>%
rename(geoid_tract = GEOID) %>%
mutate(county_fips = paste0(STATEFP, COUNTYFP),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_tract, county_fips, sqmi) %>%
arrange(geoid_tract)
blockgroup_sf <- tigris::block_groups(state = STATE, county = COUNTIES_7CO, year = BASE_YEAR) %>%
filter(TRACTCE != "990000") %>% # Exclude Lake Michigan tracts
sf::st_transform(cmap_crs) %>%
rename(geoid_blkgrp = GEOID) %>%
mutate(county_fips = paste0(STATEFP, COUNTYFP),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_blkgrp, county_fips, sqmi) %>%
arrange(geoid_blkgrp)
block_sf <- tigris::blocks(state = STATE, county = COUNTIES_7CO, year = BASE_YEAR) %>%
filter(TRACTCE20 != "990000") %>% # Exclude Lake Michigan tracts
sf::st_transform(cmap_crs) %>%
rename(geoid_block = GEOID20) %>%
mutate(county_fips = paste0(STATEFP20, COUNTYFP20),
sqmi = unclass(sf::st_area(geometry) / sqft_per_sqmi)) %>%
select(geoid_block, county_fips, sqmi) %>%
arrange(geoid_block)
# Save processed data to package's data dir
usethis::use_data(county_sf, overwrite = TRUE)
usethis::use_data(township_sf, overwrite = TRUE)
usethis::use_data(municipality_sf, overwrite = TRUE)
usethis::use_data(congress_sf, overwrite = TRUE)
usethis::use_data(ilga_house_sf, overwrite = TRUE)
usethis::use_data(ilga_senate_sf, overwrite = TRUE)
usethis::use_data(puma_sf, overwrite = TRUE)
usethis::use_data(zcta_sf, overwrite = TRUE)
usethis::use_data(idot_sf, overwrite = TRUE)
usethis::use_data(tract_sf, overwrite = TRUE)
usethis::use_data(blockgroup_sf, overwrite = TRUE)
usethis::use_data(block_sf, overwrite = TRUE)
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