DOI: 10.7910/dvn/nknwbx
title: 2020 General Election Voting by US Census Block Group

description: PROBLEM AND OPPORTUNITY
In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected.

For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups:

are exclusive, and do not overlap
are adjacent, fully covering their corresponding state and potentially county
have roughly the same size in area, population and voter presence
Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure.

The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates.

DATA SOURCES
The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries.

The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here.

The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people.

Dataset Structure
The dataset's columns include:

Column Definition
BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates:
2 digit state
3 digit county within state
6 digit Census Tract identifier
1 digit Census Block Group identifier within tract
STATE State abbreviation, redundent with 2 digit state FIPS code above
REP Votes for Republican party candidate for president
DEM Votes for Democratic party candidate for president
LIB Votes for Libertarian party candidate for president
OTH Votes for presidential candidates other than Republican, Democratic or Libertarian
AREA square kilometers of area associated with this block group
GAP total area of the block group, net of area attributed to voting precincts
PRECINCTS Number of voting precincts that intersect this block group

ASSUMPTIONS, NOTES AND CONCERNS:
Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative.
50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency.
Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast.
Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia.
Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon.
In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct.
The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice is done to maintain statistical reliability in the estimates and to prevent accidental disclosure of individual respondents. As a result,
the shape files for state block groups may have additional block groups not available in demographic estimates.
ignoring the suppressed block groups will cause statistical bias for these smallest geographies
As written, this projection takes more than 6 days to complete on a familiar Intel-64 based laptop. Its performance would benefit from:
Running states in parallel rather than serially
Looking for intersecting precincts within the shared county rather than state level
Allocation details causes challenges in efforts to tie totals to state and national summaries. By allocating each of 233,866 detailed block groups based on area, many double precision proportions area applied to original integer counts. The allocations themselves then are not integers and may not sum exactly to the reported state election counts.

RECOGNITION
Special thanks to the meticulous efforts of:

The Voting and Election Science Team (University of Florida, Wichita State University) (https://dataverse.harvard.edu/dataverse/electionscience)

@data{DVN/K7760H_2020, author = {Voting and Election Science Team}, publisher = {Harvard Dataverse}, title = {{2020 Precinct-Level Election Results}}, year = {2020}, version = {V29}, doi = {10.7910/DVN/K7760H}, url = {https://doi.org/10.7910/DVN/K7760H} }

MIT's Election Data and Science Lab MEDSL (https://dataverse.harvard.edu/dataverse/medsl

“U.S. Census TIGER/Line Files for Block Groups 2021.” Index of /Geo/Tiger/TIGER2021/BG, 22 Sept. 2021, https://www2.census.gov/geo/tiger/TIGER2021/BG/.

LICENSE
This code is available subject to the MIT Open Source License

SUMMARY STATISTICS
State Republican Democrat Libertarian Other Precincts Block Groups
AL 1,441,170 849,624 25,176 7,312 1,972 3,925
AK 189,951 153,778 8,897 4,943 441 504
AZ 1,661,686 1,672,143 51,465 0 1,489 4,773
AR 760,647 423,932 13,133 21,357 2,591 2,294
CA 6,006,428 11,110,493 187,907 192,232 20,799 25,607
CO 1,364,607 1,804,352 52,460 35,561 3,215 4,058
CT 714,717 1,080,831 20,230 8,079 741 2,716
DE 200,603 296,268 5,000 2,139 434 706
DC 18,586 317,323 2,036 6,411 144 571
FL 5,668,731 5,297,045 70,324 54,769 6,010 13,388
GA 2,461,837 2,474,507 62,138 0 2,679 7,446
HI 196,864 366,130 5,539 5,936 262 1,083
ID 554,119 287,021 16,404 9,737 935 1,284
IL 2,446,891 3,471,915 66,544 48,088 10,083 9,898
IN 1,729,857 1,242,498 58,901 0 5,166 5,290
IA 897,672 759,061 19,637 14,501 1,661 2,703
KS 771,406 570,323 30,574 0 4,070 2,461
LA 1,255,776 856,034 21,645 14,607 3,753 4,294
ME 360,767 435,070 14,120 9,412 573 1,184
MD 976,414 1,985,023 33,488 42,106 2,043 4,079
MA 1,167,202 2,382,202 47,013 34,985 2,173 5,116
MI 2,649,859 2,804,036 60,406 23,907 4,756 8,386
MN 1,484,065 1,717,077 34,976 41,053 4,110 4,706
MS 756,764 539,398 8,026 9,571 1,764 2,445
MO 1,718,736 1,253,014 41,205 12,202 3,733 5,031
MT 343,602 244,786 15,252 0 666 900
NE 556,846 374,583 20,283 0 1,386 1,648
NV 669,890 703,486 14,783 17,217 2,094 1,963
NH 365,660 424,937 13,236 0 321 997
NJ 1,883,274 2,608,335 31,677 26,067 726 6,599
NM 401,894 501,614 12,585 7,872 1,917 1,614
NY 3,251,997 5,244,886 60,383 74,987 15,376 16,070
NC 2,758,775 2,684,292 48,678 33,059 2,662 7,111
ND 235,751 115,042 9,371 1,860 422 632
OH 3,154,834 2,679,165 67,569 18,812 8,941 9,472
OK 1,020,280 503,890 24,731 11,798 1,948 3,374
OR 958,448 1,340,383 41,582 33,908 1,331 2,970
PA 3,378,442 3,460,475 79,432 0 9,150 10,173
RI 199,922 307,486 5,053 5,296 423 792
SC 1,385,103 1,091,541 27,916 8,769 2,263 3,408
TN 1,852,475 1,143,711 29,877 26,926 1,962 4,562
TX 5,890,570 5,259,281 126,269 44,299 9,014 18,638
UT 865,140 560,282 38,447 42,773 2,424 2,020
VT 112,704 242,820 3,608 8,296 284 552
VA 1,962,430 2,413,568 64,761 19,765 2,477 5,963
WA 1,584,651 2,369,612 80,500 52,868 7,464 5,311
WI 1,610,184 1,630,866 38,491 18,500 7,090 4,692
WY 193,559 73,491 5,768 3,947 481 457
Total 72,091,786 80,127,630 1,817,496 1,055,927.00 166,419 233,866
description: Python, 3.9.7
description: geopandas, 0.10.2
description: https://github.com/OpenEnvironments/blockgroupvoting
description: The programming and other documentation for this dataset are available in the github.com repository https://github.com/OpenEnvironments/blockgroupvoting.
All feedback and peer review welcome to michael.bryan@openenvironments.com