As an example we will explore changing household size between the 2000 and 2010 US census. First we need to build the metadata for our variables “H011001” for population and “H013001” for households.
variables=c(population="H011001",households="H013001")
meta <- c(2000,2010) %>%
lapply(function(year){
v <- variables %>% setNames(paste0(names(.),"_",year))
meta_for_additive_variables(paste0("dec",year),v)
}) %>%
bind_rows()
meta
#> # A tibble: 4 × 8
#> variable dataset label type aggregation rule geo_dataset parent
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 H011001 dec2000 population_2000 Manual Additive Additi… dec2000 NA
#> 2 H013001 dec2000 households_2000 Manual Additive Additi… dec2000 NA
#> 3 H011001 dec2010 population_2010 Manual Additive Additi… dec2010 NA
#> 4 H013001 dec2010 households_2010 Manual Additive Additi… dec2010 NAArmed with that we can call get_tongfen_us_census to
request the data on a common geography based on census tracts and
compute the change in household size.
census_data <- get_tongfen_us_census(regions = list(state="CA"), meta=meta, level="tract") %>%
mutate(change=population_2010/households_2010-population_2000/households_2000) census_data %>% names()
#> [1] "TongfenID" "TongfenUID" "geometry" "population_2000"
#> [5] "households_2000" "population_2010" "households_2010" "change"We bin the data for better plotting and zoom in on the Bay area.
census_data %>%
mutate(c=cut(change,c(-Inf,-0.5,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.5,Inf))) %>%
ggplot() +
geom_sf(aes(fill=c), size=0.05) +
scale_fill_brewer(palette = "RdYlGn") +
labs(title="Bay area change in average household size 2000-2010", fill=NULL) +
#geom_water() + geom_roads() +
coord_sf(datum=NA,xlim=c(-122.6,-121.7),ylim=c(37.2,37.9))
The same works across the 2010 and 2020 censuses. Two things change.
The 2020 census renamed the variables, population in occupied
housing units is H8_001N and households is
H3_002N. And those live in the Demographic and Housing
Characteristics file, whereas tidycensus reads the PL 94-171
redistricting file for 2020 by default, so we point it at the right one
via sumfile. It takes a single value for all censuses, or
one named by dataset as we do here.
meta_2020 <- bind_rows(
meta_for_additive_variables("dec2010",c(population_2010="H011001",
households_2010="H013001")),
meta_for_additive_variables("dec2020",c(population_2020="H8_001N",
households_2020="H3_002N")))census_data_2020 <- get_tongfen_us_census(regions = list(state="CA"), meta=meta_2020,
level="tract", sumfile=c(dec2020="dhc")) %>%
mutate(change=population_2020/households_2020-population_2010/households_2010)census_data_2020 %>%
mutate(c=cut(change,c(-Inf,-0.5,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.5,Inf))) %>%
ggplot() +
geom_sf(aes(fill=c), size=0.05) +
scale_fill_brewer(palette = "RdYlGn") +
labs(title="Bay area change in average household size 2010-2020", fill=NULL) +
coord_sf(datum=NA,xlim=c(-122.6,-121.7),ylim=c(37.2,37.9))
The Census Bureau relationship files these correspondences are built
from are geometric overlays, they list every place two censuses’
geographies intersect, including slivers along boundaries that only
shifted by a few metres. Chaining those together merges regions that
have nothing to do with each other, so min_area_share sets
how much area two regions have to have in common before they count as
related. The default of 0.01 works well, raising it gives
finer common geographies at the risk of separating regions that
genuinely did change. No region is ever dropped, if all of a region’s
parts fall below the cutoff its largest part is kept.
Correspondence tables reach back one census further than the data
does. The Census Bureau has retired the 1990 API endpoint, so tidycensus
cannot fetch 1990 data, but
get_tongfen_correspondence_us_census will match 1990 tracts
up with later censuses. To use it, get the 1990 data elsewhere, for
example from NHGIS via the ipumsr package, and hand it to
tongfen_aggregate along with the correspondence table.