This is an application example of totalcensus package.
The geographic hierarchy primer in Census 2010 summary file 1 technical documentation displays the relationship between geographic entities. The lower one of the two entities connected by a line is entirely within the boundary of the upper one. For example, a county subdivision is always within the boundaries of a county and a school district always within the boundaries of a state. If two entities are not connected, they may not belong to each other. For example, the ZIP code tabulation areas may cross state borders though they are much smaller than states.
It is easy to get the summary statistics of lower geographies within a higher one when they are connected. For example, if we want the race population of all county subdivision in Kent county, RI, we can run
library(totalcensus)
library(data.table)
library(magrittr)
sub_kent <- read_acs5year(
year = 2016,
states = "RI",
areas = "Kent county, RI",
table_contents = c(
"white = B02001_002",
"black = B02001_003",
"asian = B02001_005"
),
summary_level = "070" # of county subdivision
)
print(sub_kent)
# area GEOID lon lat state population white black asian GEOCOMP SUMLEV NAME
# 1: Kent County, RI 07000US440031864031240 -71.73078 41.69073 RI 728 724 0 4 all 070 Greene CDP, Coventry town, Kent County, Rhode Island
# 2: Kent County, RI 07000US440031864099999 -71.59396 41.69140 RI 34225 32994 384 187 all 070 Remainder of Coventry town, Coventry town, Kent County, Rhode Island
# 3: Kent County, RI 07000US440032224099999 -71.48331 41.64415 RI 13104 12120 77 404 all 070 East Greenwich town, East Greenwich town, Kent County, Rhode Island
# 4: Kent County, RI 07000US440037430074300 -71.42452 41.71389 RI 81881 74990 1163 2237 all 070 Warwick city, Warwick city, Kent County, Rhode Island
# 5: Kent County, RI 07000US440037772099999 -71.65790 41.62810 RI 6112 5611 26 314 all 070 West Greenwich town, West Greenwich town, Kent County, Rhode Island
# 6: Kent County, RI 07000US440037844099999 -71.51749 41.70306 RI 28836 26196 704 806 all 070 West Warwick town, West Warwick town, Kent County, Rhode IslandIf two geographic entities are not connected by a line, how do we know, for example, how many ZIP code tabulation areas are in or partially in Boston city?
The key to answer this question is that census blocks are connected to and lower than all other geographies. We can connect any two geographic entities through census blocks: if an ZIP code tabulation area and Boston city share a census block, the ZIP code is in or partially in the city. The decennial census 2010 has data down to block level, with which we can find all zip codes in Boston using totalcensus package.
zip_boston <- read_decennial(
year = 2010,
states = "MA",
geo_headers = c("ZCTA5", "PLACE"),
summary_level = "block"
) %>%
# use search_fips("boston", "MA") to find its PLACE code is "07000"
.[PLACE == "07000", unique(ZCTA5)]
zip_boston
# all zip code in Boston:
# [1] "02134" "02135" "02467" "02215" "02163" "02115" "02116" "02199"
# [9] "02108" "02114" "02113" "02109" "02110" "02203" "02129" "02128"
# [17] "02127" "02210" "02118" "02111" "02119" "02120" "02130" "02121"
# [25] "02125" "02122" "02124" "02126" "02131" "02132" "02136" "99999"
# [33] "02152" "02151"Now let’s read race population by zip code in or partially in Boston city from the latest 2016 ACS 5-year survey.
# read data for all zip code
race_zip_boston <- read_acs5year(
year = 2016,
states = "US", # ZCTA5 only in national files
geo_headers = "ZCTA5",
table_contents = c(
"white = B02001_002",
"black = B02001_003",
"asian = B02001_005"
),
summary_level = "860" # of ZCTA5
) %>%
# select zip codes in or partially in Boston city
.[ZCTA5 %in% zip_boston]
head(race_zip_boston, 3)
# GEOID lon lat ZCTA5 state population white black asian GEOCOMP SUMLEV NAME
# 1: 86000US02108 -71.06485 42.35777 02108 NA 4049 3515 209 172 all 860 ZCTA5 02108
# 2: 86000US02109 -71.05063 42.36722 02109 NA 4015 3497 135 249 all 860 ZCTA5 02109
# 3: 86000US02110 -71.04785 42.36196 02110 NA 2124 1814 83 206 all 860 ZCTA5 02110Let’s examine another example: congressional districts (CD for 111th congress) and state legislative districts (SLDU for Upper Chamber year 1 and SLDL for Lower Chamber year 1). Both CD and SLDs descend from states but do not belong to each other. Usually SLDs are smaller than CD. So which SLDs are in or partially in each CD? Again, we can connect CD and SLD with census blocks using decennial census 2010 data.
vote_RI <- read_decennial(
year = 2010,
states = "RI",
geo_headers = c("CD", "SLDU", "SLDL"),
summary_level = "block"
) %>%
.[, .(SLDU = list(unique(SLDU)), SLDL = list(unique(SLDL))), by = CD]
# each CD contains a vector of SLDUs and a vector of SLDLs
# CD SLDU SLDL
# 1: 01 c(009,011,010,012,013,023, ...) c(066,067,069,068,072,075, ...)
# 2: 02 c(024,033,035,031,029,028, ...) c(040,026,028,025,029,027, ...)