This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

This dataset contains counts of voter registration and voter turnout for all counties in the United States for the years 2004-2022. It also contains measures of each county's Democratic and Republican partisanship, including six-year longitudinal partisan indices for 2006-2022.

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... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

This table contains data on the percent of adults (18 years or older) who are registered voters and the percent of adults who voted in general elections, for California, its regions, counties, cities/towns, and census tracts. Data is from the Statewide Database, University of California Berkeley Law, and the California Secretary of State, Elections Division. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Political participation can be associated with the health of a community through two possible mechanisms: through the implementation of social policies or as an indirect measure of social capital. Disparities in political participation across socioeconomic groups can influence political outcomes and the resulting policies could have an impact on the opportunities available to the poor to live a healthy life. Lower representation of poorer voters could result in reductions of social programs aimed toward supporting disadvantaged groups. Although there is no direct evidentiary connection between voter registration or participation and health, there is evidence that populations with higher levels of political participation also have greater social capital. Social capital is defined as resources accessed by individuals or groups through social networks that provide a mutual benefit. Several studies have shown a positive association between social capital and lower mortality rates, and higher self- assessed health ratings. There is also evidence of a cycle where lower levels of political participation are associated with poor self-reported health, and poor self-reported health hinders political participation. More information about the data table and a data dictionary can be found in the About/Attachments section.

This dataset contains voter registration data in Iowa by month and county starting with January 2000. It identifies the number of voters registered as Democrats, Republicans, other party or no party. Libertarians were reported separately March 2017 through January 2019, and beginning again in January 2023. The dataset also identifies the number of active and inactive voter registrations. Inactive voters are those to whom official mailings have been sent from the county auditor’s office, the notice was returned as undeliverable by the United States Postal Service and the voter has not responded to a follow up confirmation notice. [§48A.37]

The data set contains information about voter registration and election laws in the United states and Washington, D.C. for even-numbered years between 1972 and 1992. Information about elections themselves also is included in the data set. The data collection was conducted by Glenn Mitchell II and Christopher Wlezien.

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

Using this Data - IMPORTANT

This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

All registered voters in Oregon

Abstract

The L2 Voter and Demographic data is current as of May 31 2022. The data includes demographic and voter history tables for all 50 states and the District of Columbia. The dataset is built from publicly available government records about voter registration and election participation. These records indicate whether a person voted in an election or not, but they do not record whom that person voted for. Voter registration and election participation data are augmented by demographic information from outside data sources.

Methodology

To create this file, L2 processes registered voter data on an ongoing basis for all 50 states and the District of Columbia, with refreshes of the underlying state voter data typically at least every six months and refreshes of telephone numbers and National Change of Address processing approximately every 30 to 60 days. These data are standardized and enhanced with propriety commercial data and modeling codes and consist of approximately 185,000,000 records nationwide.

Usage

For each state, there are two main tables: demographic and voter history. The demographic and voter tables can be joined on the 'LALVOTERID' variable. One can also use the 'LALVOTERID' variable to link the L2 Voter and Demographic data with the L2 Commercial data.

The 'LALVOTERID' variable can also be used to validate the state. For example, let's look at the 'LALVOTERID' LALCA123456789. The characters in the fourth and fifth positions of this identifier are 'CA' (California). The second way to validate the state is by using the RESIDENCE_ADDRESSES_STATE, which should have a value of 'CA' (California).

The date appended to each table name represents when the data was last updated.

The demographic files use 691 consistent variables. For more information about these variables, see 2021-11-03-L2-Voter-Demographic-File-Layout.xlsx.

The voter history files have different variables depending on the state. The ***2022-05-31-L2-Voter-Data-Dictionaries.tar.gz ***file expands into a folder with .csv data dictionaries for each state's demographic and voter files. While the demographic file data dictionaries should complement the 2021-11-03-L2-Voter-Demographic-File-Layout.xlsx file, the voter file data dictionaries will be unique to each state.

**JOINED TABLES: **

Additionally, each state has a "COMPLETE-VM1" table which has demographics and vote history from multiple years stacked on top of each other. There is an additional column in these tables called "L2-filename" which has the L2 filename from which the data were drawn, and within this file name is the date of the voter file snapshot.

The Voter Participation indicator presents voter turnout in Champaign County as a percentage, calculated using two different methods.

In the first method, the voter turnout percentage is calculated using the number of ballots cast compared to the total population in the county that is eligible to vote. In the second method, the voter turnout percentage is calculated using the number of ballots cast compared to the number of registered voters in the county.

Since both methods are in use by other agencies, and since there are real differences in the figures that both methods return, we have provided the voter participation rate for Champaign County using each method.

Voter participation is a solid illustration of a community’s engagement in the political process at the federal and state levels. One can infer a high level of political engagement from high voter participation rates.

The voter participation rate calculated using the total eligible population is consistently lower than the voter participation rate calculated using the number of registered voters, since the number of registered voters is smaller than the total eligible population.

There are consistent trends in both sets of data: the voter participation rate, no matter how it is calculated, shows large spikes in presidential election years (e.g., 2008, 2012, 2016, 2020, 2024) and smaller spikes in intermediary even years (e.g., 2010, 2014, 2018, 2022). The lowest levels of voter participation can be seen in odd years (e.g., 2015, 2017, 2019, 2021, 2023).

This data primarily comes from the election results resources on the Champaign County Clerk website. Election results resources from Champaign County include the number of ballots cast and the number of registered voters. The results are published frequently, following each election.

Data on the total eligible population for Champaign County was sourced from the U.S. Census Bureau, using American Community Survey (ACS) 1-Year Estimates for each year starting in 2005, when the American Community Survey was created. The estimates are released annually by the Census Bureau.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because this data is not available for Champaign County, the eligible voting population for 2020 is not included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes datasets on Population by Sex and Population Under 18 Years by Age.

Sources: Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (24 November 2025).; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (10 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (5 October 2023).; Champaign County Clerk Historical Election Data; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (7 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using data.census.gov; (8 June 2021).; Champaign County Clerk Election History; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (13 May 2019).; U.S. Census Bureau; American Community Survey, American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (6 March 2017).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey 2012 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B05003; generated by CCRPC staff; using American FactFinder; (15 March 2016).

According to exit polling in ten key states of the 2024 presidential election in the United States, 46 percent of voters with a 2023 household income of 30,000 U.S. dollars or less reported voting for Donald Trump. In comparison, 51 percent of voters with a total family income of 100,000 to 199,999 U.S. dollars reported voting for Kamala Harris.

Although the secret ballot has been secured as a legal matter in the United States, formal secrecy protections are not equivalent to convincing citizens that they may vote privately and without fear of reprisal. We present survey evidence that those who have not previously voted are particularly likely to voice doubts about the secrecy of the voting process. We then report results from a field experiment where we mailed information about protections of ballot secrecy to registered voters prior to the 2010 general election. Consistent with our survey data, we find that these letters increased turnout for registered citizens without records of previous turnout, but did not appear to influence the behavior of citizens who had previously voted. The increase in turnout of more than three percentage points (20%) for those without previous records of voting is notably larger than the effect of a standard get-out-the-vote mailing for this group. Overall, these results suggest that although the secret ballot is a long-standing institution in the United States, beliefs about this institution may not match the legal reality. Providing basic information about ballot secrecy can cause former non-voters to participate.

This dataset contains voter registration data in Iowa by month and state senate district starting with June 2021. It identifies the number of voters registered as Democrats, Republicans, other party or no party. The dataset also identifies the number of active and inactive voter registrations. Inactive voters are those to whom official mailings have been sent from the county auditor’s office, the notice was returned as undeliverable by the United States Postal Service and the voter has not responded to a follow up confirmation notice. [§48A.37]

2020 Detailed Voter Registration and Turnout Data

Overview

Kensho's Team Impact is excited to partner with the American Voter Project (the non-profit that runs the Ohio Voter Project) to make this dataset on Georgia voters available via Kaggle.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F73968%2Fe929cb6eb3d7a11dbef5feee4b336f91%2FOVP-Kensho_1000.jpg?generation=1608150215724319&alt=media" alt="">

This dataset has two main components. The first is statewide Georgia voter lists for October, November, and December provided by the American Voter Project and originally sourced from the Georgia Secretary of State. The second is cartographic boundary files from the US Census.

Starter Notebook

Jump right in with a starter notebook that demonstrates reading the data, creating maps, and aggregating voter data.

https://www.kaggle.com/gabrielaltay/georgia-voter-list-starter

Voter File Descriptions

Voter files contain one row per person, are provided for October, November, and December of 2000, and use the following naming convention,

tbl_prod_GABUYYYYMM_sample.csv

The samples are defined as,

• all: all voters in the file provided by the secretary of state of Georgia for a given month
• dropped_records: voters that were in the all sample last month but are not in the all sample this month
• new_records: voters that are in the all sample this month but were not in the all sample last month
• address_change: voters with address info that changed from last month
• name_change: voters with name info that changed from last month
• voter_in_inactive: voters with voter_status = I in the all sample for this month
• voter_status_change: voters with voter_status that changed from last month

Due to privacy concerns we have removed names and addresses (except city, zipcode, and county) from the voter files.

Geographic File Descriptions

The geographic data we collected consists of geojson files that describe cartographic boundaries in the US. We obtained shapefiles from this website and converted them to geojson using geopandas. We follow the naming convention used for the census shape files,

cb_2019_us_entity_rr.geojson where,

entity = the geographic entity rr = resolution level (we use the 20m = 1:20,000,000 and 500k = 1:500,000 scale files)

Specifically, we include the following geographic entities,

cbsa: metropolitan / micropolitan statistical area cd116: congressional district (116th congress) county: county csa: combined statistical area division: national division (subdivisions of regions) nation: national outline region: national region (northeast, southeast, midwest, west) state: state and equivalent zcta510: 5-digit ZIP code tabulation area (Census 2010)

Usage Restrictions

A quote from the Georgia Secretary of State dataset website,

The Statewide Voter List is an electronic file that includes the date last voted for each registered voter in the state of Georgia.

By law, voter registration lists are available to the public and contain the following information: voter name, residential address, mailing address if different, race, gender, registration date and last voting date. The Statewide Voter List does not include telephone numbers, date of birth, Social Security number or Drivers License number. The Statewide Voter List includes Active and Inactive Voters.

Normal production time is 1-2 weeks upon receipt of order. The Statewide Voter List file will be provided to you electronically.

The pricing is set by the Secretary of State office. This data may not be used by any person for commercial purposes. O.C.G.A. § 21-2-225 ( c )

In accordance with O.C.G.A. § 21-2-601, any person who uses the list of electors provided for in O.C.G.A. § 21-2-225 for commercial purposes shall be guilty of a misdemeanor.

Acknowledgements

We would like to thank Steve Tingley-Hock in general for his years of work on behalf of voters and specifically for sharing this data. You can learn more about his work at the following links,

• (VICE) This Man Exposed 40K Voters Purged by Mistake
• (Wired) One IT Guy's Spreadsheet-Fueled Race to Restore Voting Rights
• (New York Times) Ohio Was Set to Purge 235,000 Voters. It Was Wrong About 20%

FWISD8 Early Voting & Ballot by Mail Data

Precincts, Voters, and Election Participation

By Jason Brown [source]

About this dataset

This dataset contains comprehensive information about early voting and ballot by mail for Fort Worth ISD District 8. It includes key data points such as the full name, address lines 1-4, city, state, zip/zip+4 code of the voter; precinct and sub-precinct; ballot style, ballot party and voter party; election code and phone area/prefix/number; early voting site (if applicable) plus firstname and lastname of the voter. In addition to this voting information, the dataset also includes a field for the date that each voter cast their vote or submitted their mail in ballot. All of this data can be used to identify trends in voting behavior within a precinct or across Fort Worth ISD District 8 as a whole. With it, researchers may gain valuable insights into what motivates voters to go out and cast their ballots as well as other key information that can be used to increase democratic experiences in future elections

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides information on the voting and ballot by mail process in Fort Worth ISD District 8. This data can be used to answer questions about who participated and how they voted, as well as which areas are most active when it comes to voting.

To get started, here are some tips for using the dataset: - Explore the data columns - The columns provided in this dataset include full name, address information (address line 1 through 4), city, state, zip code, precinct number/subdivision numbers for early voting sites and precinct locations , ballot style/party affiliations of voters , election code, phone numbers of registered voters (area code & prefix) and vote-by-mail site . As you explore different use cases of this data set you may find other interesting connections or patterns between one column or another that can help answer questions or provide insights into voting practices in the area. - Look at voter turnout - Using this dataset you can analyze voter turnout over a given period of time to identify trends in voter engagement both within one district but also across various districts across Texas. Pay attention to both early voting sites & traditional polling locations when making comparisons as they tend towards different kinds of participation among residents - people who prefer early voting might not prefer traditional polling locations vice versa so it's important to look at both types together..
- Understand Voter Motivation - Examine what most drives voter involvement in elections? Examining factors such as location (rural vs urban), age demographics etc., can tell us about what motivates voters either positively or negatively regarding engagement in elections held within their district . Comparing these numbers with actual votes casted provides rich insight into motivation behind why people may not have voted .

By understanding the existing patterns between these datasets using sophisticated analytics methods ,we could make highly accurate predictions about which areas will have higher levels of turnout at an upcoming election . With this knowledge we could implement policies that help increase interest/participation even further enabling a more open/fair democratic process for everyone involved!

Research Ideas

• Understanding Voter Behavior: Using this dataset, research organizations and political campaigns can gain insight into how certain demographics are voting and who they are voting for.
• Targeted Campaign Ads: With this dataset, marketing teams can create demographic-specific ads aimed at getting people to turn out to vote or targeting voters with a specific persuasion.
• Polling Place Location: Analyzing the data in regards to polling places could help cities identify where it is most beneficial to open and close polling locations, as well as the length of opening hours needed at each location depending on voter turnout or trend along party lines in a particular area

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: ev_vtrex_isdfw8.csv | Column name | Description | |:--------------------|:---------------------------------------------------------| | full_name | Full name of the voter. (String) | | addr_line1 ...

Research suggests that partisans are increasingly avoiding members of the other party—in their choice of neighborhood, social network, even their spouse. Leveraging a national database of voter registration records, we analyze 18 million households in the U.S. We find that three in ten married couples have mismatched party affiliations. We observe the relationship between inter-party marriage and gender, age, and geography. We discuss how the findings bear on key questions of political behavior in the US. Then, we test whether mixed-partisan couples participate less actively in politics. We find that voter turnout is correlated with the party of one’s spouse. A partisan who is married to a co-partisan is more likely to vote. This phenomenon is especially pronounced for partisans in closed primaries, elections in which non-partisan registered spouses are ineligible to participate.

Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%. Margins of error are estimated at the 90% confidence level.

Data Source: Current Population Survey (CPS) Voting Supplement, 2020

Why This Matters

Voting is one of the primary ways residents can have their voices heard by the government. By voting for elected officials and on ballot initiatives, residents help decide the future of their community.

For much of our nation’s history, non-white residents were explicitly prohibited from voting or discriminated against in the voting process. It was not until the Voting Rights Act of 1965 that the Federal Government enacted voting rights protections for Black voters and voters of color.

Nationally, BIPOC citizens and especially Hispanic and Asian citizens have consistently lower voter turnout rates and voter registration rates. While local DC efforts have been taken to remove these barriers, restrictive voter ID requirements and the disenfranchisement of incarcerated and returning residents act as institutionally racist barriers to voting in many jurisdictions.

The District's Response

The DC Board of Elections has lowered the barriers to participate in local elections through online voter registration, same day registration, voting by mail, and non-ID proof of residence.

Unlike in many states, incarcerated and returning residents in D.C. never lose the right to vote. Since 2024, DC has also extended the right to vote in local elections to residents of the District who are not citizens of the U.S.

Although DC residents pay federal taxes and can vote in the presidential election, the District does not have full representation in Congress. Efforts to advocate for DC statehood aim to remedy this.

Comprehensive dataset containing 32 verified Voter Registration locations in United States with complete contact information, ratings, reviews, and location data.

Context

This is part of the voter file verified by the North Carolina State Board of Elections. It includes the registered voters, but not the voting history.

The North Carolina State Board of Elections updates this data everyday. See https://dl.ncsbe.gov/ for the original files. Feel free to post questions here and I can try to answer them. I'm not certain as to what obligation the BOE has to provide technical support for this file

**Note on timeline of this data's collection: ** I specified the "Temporal coverage" as 1900-01-01 to 2020-02-29. The last date is correct, but I don't know the specific date for first value. I believe it would be the time of registration for the longest registered voter (without updates or re-registration) that the BOE cannot verify is dead. The specific designation has been highly politicized by voter suppression efforts of the last two decades. One would likely need to contact the BOE (or have a specialized law degree) to determine how that date is constructed, let alone to verify it.

Content

Below is documentation provided by the North Carolina State Board of Elections. I am not the author; I have copied it here for ease of use from the ncsbe.gov resource.

Updated 2/29/2020, 7:04:30 AM

layout ncvoter ncvhis.txt

/* *******************************************************************************
* name: layout_ncvoter_ncvhis.txt
* purpose: Instruction file with file layout for weekly Voter and Voter History text files
* updated: 10/24/2019
* files: 
  -- 1) ncvhis#.zip      This file contains voter history (does not contain 
  --              voter names).
  -- 2) ncvoter#.zip      This file contains all legally available voter information 
  --              and is linkable to the companion voter history 
  --              (ncvhis#) file. Birth date and ssn are confidential.
  -- 3) ncvhis_Statewide.zip  statewide voter history file
  -- 4) ncvoter_Statewide.zip statewide voter file
* note: # is the county id for the county in that file (id list is below)
* instructions: 
  -- Extract using a file archiving and compression program (eg. WinZip)
  -- Link data between files by ncid.
******************************************************************************* */


1) Voter History File (ncvhis#.txt):
  county_id        int    4
  county_desc       char   60
  voter_reg_num      char   12
  election_lbl      char   10 
  election_desc      char   230 
  voting_method      char   10

  voted_party_cd     char   3 
  voted_party_desc    char   60 
  pct_label        char   6 
  pct_description     char   60 
  ncid          char   12 
  voted_county_id     char   3 
  voted_county_desc    char   60 
  vtd_label        char   6 
  vtd_description     char   60 

2) Voter & Pre-registrations Files (ncvoter#.txt):
  county_id      smallint  2
  county_desc     varchar 15
  voter_reg_num        char  12
  status_cd      char  2
  voter_status_desc    varchar 25
  reason_cd      char  2
  voter_status_reason_desc  varchar 60
  absent_ind     char  1
  name_prefx_cd        char  4
  last_name      char  25
  first_name     char  20
  middle_name     char  20
  name_suffix_lbl       char  3
  res_street_address   varchar 63
  res_city_desc        varchar 60
  state_cd      char  2
  zip_code      char  9
  mail_addr1     varchar 40
  mail_addr2     varchar 40
  mail_addr3     varchar 40
  mail_addr4     varchar 40
  mail_city      varchar 30
  mail_state     char  2
  mail_zipcode        char  9
  full_phone_number    varchar 12
  race_code      char  3
  ethnic_code     char  3
  party_cd      char  3
  gender_code     varchar 1
  birth_age      int     4
  birth_state     char  2
  drivers_lic     char    1  (Y/N)
  registr_dt     char  10
  precinct_abbrv       char  6
  precinct_desc        varchar 60
  municipality_abbrv   char  6
  municipality_desc    varchar 60
  ward_abbrv     char  6
  ward_desc      varchar 60
  cong_dist_abbrv       char  6
  super_court_abbrv    char  6
  judic_dist_abbrv    char  6
  nc_senate_abbrv       char  6
  nc_house_abbrv       char  6
  county_commiss_abbrv    char  6
  county_commiss_desc   varchar 60
  township_abbrv       char  6
  township_desc        varchar 60
  school_dist_abbrv    char  6
  school_dist_desc    varchar 60
  fire_dist_abbrv       char  6
  fire_dist_desc       varchar 60
  water_dist_abbrv    char  6
  water_dist_desc       varchar 60
  sewer_dist_abbrv    char  6
  sewer_dist_desc       varchar 60
  sanit_dist_abbrv    char  6
  sanit_dist_desc       varchar 60
  rescue_dist_abbrv    char  6
  rescue_dist_desc    varchar 60
  munic_dist_abbrv    char  6
  munic_...