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.

The current dataset captures voter registration counts and voter 'turnout', or the percentage of registered voters who voted in each election, since 2015. The data is aggregated at various levels including the political precinct (division), political ward, and city-wide and shows results for different elections (primary, general, special). Historical releases of this data prior to 2015 were separate datasets, one for voter turnout and one for voter registration.

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...

This data collection contains voter registration and turnout surveys. The files contain summaries at state, town, and county levels. Each level of data include: total population, total voting-age population, total voter registration (excluding ND, WI), total ballots cast, total votes cast for president, and voter registration by party. Note: see the documentation for information on missing data.

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php lists the available data. Files are occasionally updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS. These files were last updated on 9 JUL 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the Data and Reproduction Archive Version number in your citations for the full dataset.

For additional information on file layout, etc. see https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_turnout.html.

Similar data may be available at https://www.electproject.org/election-data/voter-turnout-data dating back to 1787.

Analysis of ‘US non-voters poll data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-non-voters-poll-datae on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains the data behind Why Many Americans Don't Vote.

Data presented here comes from polling done by Ipsos for FiveThirtyEight, using Ipsos’s KnowledgePanel, a probability-based online panel that is recruited to be representative of the U.S. population. The poll was conducted from Sept. 15 to Sept. 25 among a sample of U.S. citizens that oversampled young, Black and Hispanic respondents, with 8,327 respondents, and was weighted according to general population benchmarks for U.S. citizens from the U.S. Census Bureau’s Current Population Survey March 2019 Supplement. The voter file company Aristotle then matched respondents to a voter file to more accurately understand their voting history using the panelist’s first name, last name, zip code, and eight characters of their address, using the National Change of Address program if applicable. Sixty-four percent of the sample (5,355 respondents) matched, although we also included respondents who did not match the voter file but described themselves as voting “rarely” or “never” in our survey, so as to avoid underrepresenting nonvoters, who are less likely to be included in the voter file to begin with. We dropped respondents who were only eligible to vote in three elections or fewer. We defined those who almost always vote as those who voted in all (or all but one) of the national elections (presidential and midterm) they were eligible to vote in since 2000; those who vote sometimes as those who voted in at least two elections, but fewer than all the elections they were eligible to vote in (or all but one); and those who rarely or never vote as those who voted in no elections, or just one.

The data included here is the final sample we used: 5,239 respondents who matched to the voter file and whose verified vote history we have, and 597 respondents who did not match to the voter file and described themselves as voting "rarely" or "never," all of whom have been eligible for at least 4 elections.

If you find this information useful, please let us know.

License: Creative Commons Attribution 4.0 International License

Source: https://github.com/fivethirtyeight/data/tree/master/non-voters

This dataset was created by data.world's Admin and contains around 6000 samples along with Race, Q27 6, technical information and other features such as: - Q4 6 - Q8 3 - and more.

How to use this dataset

• Analyze Q10 3 in relation to Q8 6
• Study the influence of Q6 on Q10 4
• More datasets

Acknowledgements

If you use this dataset in your research, please credit data.world's Admin

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The table AL-Demographic-2025-03-12 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 3612139 rows across 698 variables.

Electoral registrations for parliamentary and local government elections as recorded in electoral registers for England, Wales, Scotland and Northern Ireland.

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.

This study contains files of Presidential election votes by State, County, and Town for each U.S. Presidential election year from 1964-2020. From Dave Leip, Atlas of U.S. Presidential Elections. Note: MIT posted similar publicly available data beginning with 1976 at https://doi.org/10.7910/DVN/42MVDX

Information available in each dataset

If you want to know what each Presidential Election dataset contains before downloading it, for easy reference, the CCSS Data Services team prepared a spreadsheet summarizing the contents of each dataset. You can view them in this Summary of contents and codebooks spreadsheet.

The summary spreadsheet contains the following: 1. A matrix table summarizing the information available in each Presidential election dataset 2. Codebook describing the variables in the Presidential Election vote data at the State level 3. Codebook describing the variables in the Presidential Election vote data at the County level 4. Codebook describing the variables in the Presidential Election vote data at the Town level 5. A matrix table listing the statistics and graphs included in each Presidential election dataset

Labels of the variables in the State, County, and Town data, as well as a description of each tab in the dataset, are also available here: https://uselectionatlas.org/BOTTOM/DOWNLOAD/spread_national.html

Dave Leip's website

The Dave Leip website here: https://uselectionatlas.org/BOTTOM/store_data.php has additional years of data available going back to 1912 but at a fee.

Sometimes the files are updated by Dave Leip, and new versions are made available, but CCSS is not notified. If you suspect the file you want may be updated, please get in touch with CCSS Data Discovery and Replication Services. These files were last checked for updates in June 2024.

Note that file version numbers are those assigned to them by Dave Leip's Election Atlas. Please refer to the CCSS Data and Reproduction Archive Version number in your citations for the full dataset.

Analysis of ‘Agency Voter Registration Activity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/998f045b-4270-4800-92aa-1434e6f98550 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Section 1057-a of the New York City Charter requires certain agencies to engage in and report on voter registration activities. This dataset captures how many voter registration applications each agency has distributed, how many applications agency staff sent to the Board of Elections, how many staff each agency trained to distribute voter registration applications, whether or not the agency hosts a link to voting.nyc on its website and if so, how many clicks that link received during the reporting period. Some agencies distribute voter registration applications during the course of a direct interaction with a member of the public and other agencies distribute applications passively, such as making the applications available in waiting rooms. This makes it difficult in some cases to ascertain the exact number of voter registration forms distributed. Applications sent to the Board of Elections only captures those sent by agency staff. Individuals may also choose to send in the application themselves. These applications are not counted towards the total number of applications sent to the Board of Elections. Data is reported by agencies to the Mayor’s Office of Operations twice a year. The first reporting period covers January 1 to June 30; the second reporting period covers July 1 to December 31.

--- Original source retains full ownership of the source dataset ---

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.

The product has been discontinued since: 08 Feb 2018.

The number of those who cast a vote or 'turn out' at an election includes those who cast blank or invalid votes. In Belgium, Luxembourg and Greece, voting is compulsory. In Italy, voting is a civic obligation (no penalty). The EU average was estimated by Eurostat on the basis of the trends observed in each of the Member States. The EU average refers to parliamentary elections for all countries, except for Cyprus (only presidential elections), France, Portugal and Romania (both parliamentary and presidential elections).

The table SD-Demographic-2025-03-18 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 611195 rows across 698 variables.

This dataset captures how many voter registration applications each agency has distributed, how many applications agency staff sent to the Board of Elections, how many staff each agency trained to distribute voter registration applications, whether or not the agency hosts a link to voting.nyc on its website and if so, how many clicks that link received during the reporting period.

The table OR-Demographic-2025-03-21 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 3384760 rows across 698 variables.

The table ME-Voter-History-2025-03-05 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 2226632 rows across 177 variables.

The Eighteenth-Century Political Participation & Electoral Culture (ECPPEC) project ran from 2020–2023 to explore how people participated in parliamentary elections in England in the period from 1695 to the Reform Act of 1832. Part of the project collected, transcribed and digitised polling records from 20 case study constituencies. This database contains those voting records (as at 07/11/24) in a single file that contains: Voter_id (an ID for each record, for internal use) Election_id (again, our internal ID) Election Year Election Month Constituency Type of Constituency (Borough, County, University) Election Type (by-election or general) All candidates running in that election Candidate(s) seated as result of election Voter surname Voter forename Occupation (if available) Candidates voter voted for

The table KS-Voter-History-2025-04-02 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 1882979 rows across 872 variables.

The Women's Voter Register Dataset is created from Election Department registers used to register women voters in 1920 after the passage of the 19th Amendment. The dataset contains information about newly registered women voters including name, address, place of birth, occupation, place of work, naturalization information, and closest male relative. This dataset is in progress and is updated periodically as additional voter registers are transcribed.

Context

With over 600 Million voters voting for 8500+ candidates across 543 constituencies, the general elections in the world's largest democracy are a potential goldmine of data. While there are existing separate datasets about the votes each candidate received and the personal information of each candidate, there was no comprehensive dataset that included both these information. Thus, this dataset will provide more usability than most existing datasets in this domain.

Content

I scraped the website of myneta.info to get the personal information of each candidate (as per their own sworn affidavits) and the website of Election Commission of India to get the data about the votes received. I merged both this datasets to create this comprehensive dataset. Only the candidates who secured at least 1% of the total votes polled in their constituency have been included.

Acknowledgements

I have collected the data from MyNeta.info maintained by the Association for Democratic Reforms and the website of Election Commission of India.

Inspiration

There are 2 main tasks that can be performed on this dataset: Exploratory Data Analytics to visualize the impact of each feature of the candidate and the use of machine learning to predict the chances of winning of a candidate.