2020 Census data is delayed and will be affected by differential privacy. This package uses FCC block-level population estimates from 2010–2019, which are based on new roads and map data, along with decennial Census and ACS data, to estimate 2020 block populations, both overall and by major race/ethnicity categories (using iterative proportional fitting).
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("CoryMcCartan/blockpop")Usage
We start by downloading the FCC data locally (although you can skip this step if you just want estimates for one state).
library(blockpop)
bl_download_fcc("data-raw/fcc.csv")Then we can extract the data for the state we care about and construct the 2020 block estimates.
library(dplyr)
fcc_d = bl_load_state("WA", "data-raw/fcc.csv")
block_d = bl_est_2020(fcc_d)
print(block_d)
#> # A tibble: 195,574 x 4
#> state block pop2010 pop2020
#> <fct> <chr> <dbl> <dbl>
#> 1 WA 530019501001000 0 0
#> 2 WA 530019501001001 0 0
#> 3 WA 530019501001002 0 0
#> 4 WA 530019501001003 0 0
#> 5 WA 530019501001004 0 0
#> 6 WA 530019501001005 0 0
#> 7 WA 530019501001006 0 0
#> 8 WA 530019501001007 0 0
#> 9 WA 530019501001008 0 0
#> 10 WA 530019501001009 0 0
#> # … with 195,564 more rows
summarize(block_d, across(starts_with("pop"), sum))
#> # A tibble: 1 x 2
#> pop2010 pop2020
#> <dbl> <dbl>
#> 1 6724540 7715946.To add populations by race and ethnicity, we need to download ACS and 2010 Census data.
acs_d = bl_download_acs_vars("WA")
census_d = bl_download_2010_vars("WA")Then we call bl_harmonize_vars() to create block-level estimates that still total to 2020 block populations and are close to ACS estimates at the block group level.
bl_harmonize_vars(block_d, census_d, acs_d)
#> ℹ Joining tables.
#> ℹ Harmonizing counts.
#> # A tibble: 195,574 x 22
#> state block pop2010 pop2020 vap2010 vap2020 pop_aian pop_asian pop_black
#> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 WA 530019501… 0 0 0 0 0 0 0
#> 2 WA 530019501… 0 0 0 0 0 0 0
#> 3 WA 530019501… 0 0 0 0 0 0 0
#> 4 WA 530019501… 0 0 0 0 0 0 0
#> 5 WA 530019501… 0 0 0 0 0 0 0
#> 6 WA 530019501… 0 0 0 0 0 0 0
#> 7 WA 530019501… 0 0 0 0 0 0 0
#> 8 WA 530019501… 0 0 0 0 0 0 0
#> 9 WA 530019501… 0 0 0 0 0 0 0
#> 10 WA 530019501… 0 0 0 0 0 0 0
#> # … with 195,564 more rows, and 13 more variables: pop_hisp <dbl>,
#> # pop_nhpi <dbl>, pop_other <dbl>, pop_two <dbl>, pop_white <dbl>,
#> # vap_aian <dbl>, vap_asian <dbl>, vap_black <dbl>, vap_hisp <dbl>,
#> # vap_nhpi <dbl>, vap_other <dbl>, vap_two <dbl>, vap_white <dbl>
