alarm-redist/census-2020

Joined 2020 Census and election files for redistricting.

census-2020's Issues

Oregon — `ndv` and `nrv` are identical

more detailed readme.md

You've published your R codes. For people who don't know R though, can the readme.md please describe the code's logic in more detail? Thanks.

download_redistricting_file

Thanks for the great data and associated pkgs. If I'm not mistaken, the namespace of download() function was unclear in the original function. Also, I found that creating res objective is unnecessary. The downside of my take of the below function is it has additional dependencies like {here} and {RCurl}. But I think that the overall code is less dense and more readable.

download_redistricting_file <- function(abbr, folder) {

    abbr = tolower(abbr)

    url_vtd = paste0("https://raw.githubusercontent.com/alarm-redist/census-2020/",
                     "main/census-vest-2020/", abbr, "_2020_vtd.csv")

    url_block = paste0("https://raw.githubusercontent.com/alarm-redist/census-2020/",
                       "main/census-vest-2020/", abbr, "_2020_block.csv")

    if (RCurl::url.exists(url_vtd)) {

    path = here::here(folder, basename(url_vtd))

    download.file(url_vtd, path)

    } else {

        path = here(folder, basename(url_block))

        download.file(url_block, path)
    }

}

more votes in a VTD than population

In New Mexico, there are voting districts that have more votes than voting population (or than total population). This holds when joining back to the race-specific file. Some examples below where adrv_18 is the sum of the average democratic and average republican votes and sen_18 is the number of votes counted in the 2018 Senate race in New Mexico.

Should this be possible?

# A tibble: 52 × 7
       GEOID20   pop   vap adv_18 arv_18 adrv_18 sen_18
         <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl>
 1 35027000028   178   151   187    440.    627.   465.
 2 35001000567   197   151   244.   202.    446.   330.
 3 35001000368   421   339   476.   351.    827.   615.
 4 35049000123   413   396   822.   138.    960.   698.
 5 35051000009   153   123   146.   142.    288.   214.
 6 35001000380   491   387   458.   373.    831.   625.
 7 35049000078   835   693  1248    269.   1517.  1097.
 8 35049000120   554   495   847.   168.   1016.   735.
 9 35001000537   514   436   523    348.    871.   645.
10 35049000122   480   437   728.   114.    842    607 
# … with 42 more rows

Blocks with zero population in California

About 25 percent of rows in the CA data have 0 population. 11 percent of rows have 0 population and 0 election vote tallies. Every CA county has these zero blocks to some extent. What should we make of these blocks?

suppressPackageStartupMessages(library(dplyr))
library(readr)


gh_url <- "https://github.com/alarm-redist/census-2020/raw/main/"
ca_alarm <- read_csv(paste0(gh_url, "census-vest-2020/ca_2020_block.csv"),
                     show_col_types = FALSE)

nrow(ca_alarm) # total rows
#> [1] 519723

sum(ca_alarm$pop == 0) # rows with 0 population
#> [1] 142132
sum(ca_alarm$vap == 0) # rows with 0 vap
#> [1] 144154

# row number of rows with no pres data
no_pres_rows <- which(with(ca_alarm, (pre_16_rep_tru == 0 & pre_16_dem_cli == 0 &  pre_20_rep_tru == 0 & pre_20_dem_bid == 0)))
length(no_pres_rows) # rows with no election data
#> [1] 187930

sum(ca_alarm$pop[no_pres_rows] == 0) # rows with no election data AND no population
#> [1] 59249

^{Created on 2021-11-04 by the reprex package (v2.0.1)}

Oregon and Hawaii also have similar zero pop blocks, though to a lesser extent.

states missing 2020 results

VEST has 2020 election results from PA and VA, but they're missing in this repo.

States without 2016 Presidential vote

Only two states are missing 2016 Presidential vote:

• West Virginia
• Mississippi.

This is not really a bug because VEST, MGGG, openelections don't seem to have that data either. But just flagging here in case someone finds a solution. It would be great to have all 50-states.

It is worth noting the NYT Upshot has 2020 precinct results in GeoJSON for those WV and MS. They don't seem to have 2016 for those states, though.

Disaggregation method

Hello! I'm looking to learn about your disaggregation process from precincts to blocks a little more. In your code 00_build_vest.R it looks like you make the block file here at line 77 dec <- build_dec('block', state = state, geometry = FALSE, groups = 'all', year = 2010) for 2010 and then assign the votes down based on the 2010 VAP ratio here (lines 86-89) elec_at_2010 <- elec_at_2010 %>% mutate(!!election := estimate_down( value = vest[[election]], wts = dec[['vap']], group = match_list )).
I wanted to confirm that you do not take intra-Census data into account for the disag process (e.g. for 2018 elections, disaggregting on some sort of 2018 demographic, like a voter file)? As I understand it, you use one ratio per block, dependent on which Census year and demographic are used (in build_dec and estimate_down, respectively). If you do integrate intra-decennial data, please let me know where I could find it in the code!

SD 2020 missing column names

Minor thing but noticed SD final data does not have 2020 vote even though it exists in vest-2020. When I re-ran the code the VEST 2020 columns become blanks at this line

census-2020/R/00_build_vest.R

Line 98 in 4edefa3

rt <- pl_retally(elec_at_2010, crosswalk = vest_cw)

which lead them to it being dropped in the census-2020 output.

Update README with download script + description of election names