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.

Description

This dataset contains the county-wise vote share of the United States presidential election of 2020, and in the future 2024, the main advantage of the dataset is that it contains various important county statistics such as the counties racial composition, median and mean income, income inequality, population density, education level, population and the counties occupational distribution.

_Imp: this dataset will be updated as the 2024 results come in, I will also be adding more county demographic data, if you have any queries or suggestions please feel free to comment _

Motivation

The reasons for constructing this dataset are many, however the prime reason was to aggregate all the data on counties along with the election result data for easy analysis in one place. I noticed that Kaggle contains no datasets with detailed county information, and that using the US census bureau site is pretty difficult and time consuming to extract data so it would be better to have a pre-prepared table of data

Columns

• The first columns contain information on the county and state
• The next columns contain the 2020 vote both raw and %
• The next columns contain the education level of the county population
• Following that we have information about the income and income inequality in the county
• Then we have the county racial composition
• The counties population and population density
• The final columns contain information about the distribution of occupations in the county

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.

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.

These data are derived from CANDIDATE NAME AND CONSTITUENCY TOTALS, 1788-1990 (ICPSR 0002). They consist of returns for two-thirds of all elections from 1788 to 1823 to the offices of president, governor, and United States representative, and over 90 percent of all elections to those offices since 1824. They also include information on United States Senate elections since 1912. Returns for one additional statewide office are included beginning with the 1968 election. This file provides a set of derived measures describing the vote totals for candidates and the pattern of contest in each constituency. These measures include the total number of votes cast for all candidates in the election, each candidate's percentage of the vote received, and several measures of the relative performance of each candidate. They are appended to the individual candidate records and permit extensive analysis of electoral contests over time. This dataset contains returns for all parties and candidates (as well as scattering vote) for general elections and special elections, including information on elections for which returns were available only at the constituency level. Included in this edition are data from the District of Columbia election for United States senator and United States representative. The offices of two senators and one representative were created by the "District of Columbia Statehood Constitutional Convention Initiative," which was approved by District voters in 1980. Elections for these offices were postponed until the 1990 general election. The three offices are currently local District positions, which will turn into federal offices if the District becomes a state.

EPILOGUE: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F360751%2Fa5eefdb31428bd5ce99cdf76fa484a63%2Fmap.jpg?generation=1733007717460285&alt=media" alt="">

FINAL UPDATE: It's election night, and the results are coming in. The final update includes the latest poll data from 538, which is from two days ago. Thanks all for following the development of this dataset.

OCTOBER UPDATE: The past month has been typical of the final weeks before the election - rallies, interviews, and advertising. This update includes a transcript of the VP debate between Walz and Vance, and the latest poll summaries.

SEPTEMBER UPDATE: Trump and Harris had their first debate. This update includes the transcript and recent poll results. Also, there was a second attempt to kill former President Trump! No shots fired though on this one. You'll see aerial diagrams of both attempts in the dataset.

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LATE AUGUST UPDATE: The Democratic Party replaced President Biden with his VP, Kamala Harris. It's now Trump v Harris along with one nominee from each of the smaller factions.

https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fmedia.cnn.com%2Fapi%2Fv1%2Fimages%2Fstellar%2Fprod%2F240122181719-trump-kamala-vpx-split-2.jpg%3Fc%3D16x9%26q%3Dw_850%2Cc_fill&f=1&nofb=1&ipt=984b6cf55cf55e1539003ca1c1beaa359625f6e5b08b511b3b018c9d2c959ae5&ipo=imagesg">

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AUGUST UPDATE: This election season just gets crazier and crazier. You'll see new data related to the assassination attempt on former President Trump. There are transcripts of Secret Service hearings and an annotated image of the rally area.

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JULY UPDATE: Added the transcript of the debate between Trump and Biden.

MAY UPDATE: Added some new polls and also a meta-poll assessing the quality of select pollsters.

APRIL UPDATE : The dataset now contains approval ratings for sitting presidents, which includes Biden and Trump.

MARCH UPDATE: As of last week, the presumptive nominees are Joe Biden(D) and Donald Trump(R). They also ran against each other in 2020. Robert F Kennedy Jr is running as an independent.

Presidential elections occur quadrennially in years evenly divisible by 4, on the first Tuesday after November 1. Presidential candidates from the major political parties usually declare their intentions to run as early as the spring of the previous calendar year before the election. The two major parties each nominate one candidate through a process of primary elections and nominating conventions during the election year. (source: Wikipedia)

This dataset contains data on candidates, primary/caucus results, polls, and debate transcripts. Updates and additional data will be added as the landscape develops.

Note: Version 3 of this dataset contains previous coverage of the 2022 Congressional Mid-term Elections.

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.

This data collection consists of national files containing county-level returns for elections to the offices of president, United States senator and representative, and governor. Also included are returns for one additional statewide office (usually attorney general or secretary of state) for those states that elected state offices in 1970 through 1990.

The US presidential election of 1932 was contested between incumbent President Herbert Hoover and Franklin D. Roosevelt of the Democratic Party. The election took place during the Great Depression, and although Hoover's administration presided over the outbreak of the depression, he received little competition for his party's nomination. Roosevelt was the overwhelming favorite to receive the Democratic nomination, however was not officially nominated until the fourth ballot of the convention. Republican decline The Republican Party had won 14 out of 18 elections since Lincoln's victory in 1860, and they oversaw a decade of significant prosperity in the 1920s, however the effects of the Wall Street Crash in October 1929 led to economic downturn and the breakdown of Republican leadership. Much of Roosevelt's campaign was based on highlighting the failures of Hoover's administration in the wake of the depression, as well as promising to repeal Prohibition, which was becoming increasingly unpopular (Democrats also argued that the taxation of alcohol sales would aid in economic recovery). Many prominent Republicans were openly critical of Hoover's leadership, with some going as far as supporting the opposite party, while the Democrats were more united and organized than they had been in decades. Results Following one of the most decisive victories in US presidential election history in 1928, Hoover was then on the receiving end of the largest swings in US history, losing 34 of the forty states he won in his previous election. Roosevelt won in a landslide, taking over 57 percent of the popular vote, and almost 89 percent of the electoral vote. Hoover's share of the popular vote was just forty percent, which gave him just 11 percent of the electoral vote, and this was eighteen percent fewer popular votes and 73 percent fewer electoral votes than in 1928. The third party candidate with the most votes was Norman Thomas of the Socialist Party, who received 2.2 percent of the popular vote. Roosevelt was inaugurated as the 32nd President of the United States in March 1933, and would win the next three elections, making him the only US president to spend more than two terms in the white house.

Every four years in the United States, the electoral college system is used to determine the winner of the presidential election. In this system, each state has a fixed number of electors based on their population size, and (generally speaking) these electors then vote for their candidate with the most popular votes within their state or district. Since 1964, there have been 538 electoral votes available for presidential candidates, who need a minimum of 270 votes to win the election. Because of this system, candidates do not have to win over fifty percent of the popular votes across the country, but just win in enough states to receive a total of 270 electoral college votes. Popular results From 1789 until 1820, there was no popular vote, and the President was then chosen only by the electors from each state. George Washington was unanimously voted for by the electorate, receiving one hundred percent of the votes in both elections. From 1824, a popular vote has been conducted among American citizens (with varying levels of access for women, Blacks, and poor voters), to help electors in each state decide who to vote for (although the 1824 winner was chosen by the House of Representatives, as no candidate received over fifty percent of electoral votes). Since 1924, the difference in the share of both votes has varied, with several candidates receiving over 90 percent of the electoral votes while only receiving between fifty and sixty percent of the popular vote. The highest difference was for Ronald Reagan in 1980, where he received just 50.4 percent of the popular vote, but 90.9 percent of the electoral votes. Unpopular winners Since 1824, there have been 51 elections, and in 19 of these the winner did not receive over fifty percent of the popular vote. In the majority of these cases, the winner did receive a plurality of the votes, however there have been five instances where the winner of the electoral college vote lost the popular vote to another candidate. The most recent examples of this were in 2000, when George W. Bush received roughly half a million fewer votes than Al Gore, and in 2016, where Hillary Clinton won approximately three million more votes than Donald Trump.

Description:

This dataset contains comprehensive voting data for the 2024 US elections, focusing on general ballot measures. This information includes voting results from various sources and tracking public opinion about political parties and candidates across states and demographic groups. Each item in the dataset represents a specific poll. Along with detailed information about the dates of the polls. Survey organization, sample size, margin of error, Percentage of respondents supporting each political party or candidates

Key Features:

Poll Date:The date when the poll was conducted.

Polling Organization: The name of the organization that conducted the poll.

Sample Size: The number of respondents in the poll.

Margin of Error: The statistical margin of error for the poll results.

Party/Candidate Support: Percentage of respondents who support each political party or candidate.

State/Demographics: Geographic and demographic breakdowns of the polling data.

Use Cases:

Analyzing trends in public opinion leading up to the 2024 U.S. elections. Comparing support for different political parties and candidates over time. Studying the impact of key events on voter preferences. Informing political strategies and campaign planning.

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.

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).

This dataset contains the tweet ids of approximately 280 million tweets related to the 2016 United States presidential election. They were collected between July 13, 2016 and November 10, 2016 from the Twitter API using Social Feed Manager. These tweet ids are broken up into 12 collections. Each collection was collected either from the GET statuses/user_timeline method of the Twitter REST API or the POST statuses/filter method of the Twitter Stream API. The collections are: Candidates and key election hashtags (Twitter filter): election-filter[1-6].txt Democratic candidates (Twitter user timeline): democratic-candidate-timelines.txt Democratic Convention (Twitter filter): democratic-convention-filter.txt Democratic Party (Twitter user timeline): democratic-party-timelines.txt Election Day (Twitter filter): election-day.txt First presidential debate (Twitter filter): first-debate.txt GOP Convention (Twitter filter): republican-convention-filter.txt Republican candidates (Twitter user timeline): republican-candidate-timelines.txt Republican Party (Twitter user timeline): republican-party-timelines.txt Second presidential debate (Twitter filter): second-debate.txt Third presidential debate (Twitter filter): third-debate.txt Vice Presidential debate (Twitter filter): vp-debate.txt There is also a README.txt file for each collection containing additional documentation on how it was collected. The GET statuses/lookup method supports retrieving the complete tweet for a tweet id (known as hydrating). Tools such as Twarc or Hydrator can be used to hydrate tweets. When hydrating be aware that: Twitter limits hydration to 900 requests of 100 tweet ids per 15 minute window per set of user credentials. This works out to 8,640,000 tweets per day, so hydrating this entire dataset will take 32 days. The Twitter API will not return tweets that have been deleted or belong to accounts that have been suspended, deleted, or made private. You should expect a large number of these tweets to be unavailable. There may be duplicate tweets across collections. Also, according to the Twitter documentation, duplicate tweets are possible for tweets collected from the Twitter filter stream. For tweets collected from the Twitter filter stream, this is not a complete set of tweets that match the filter. Gaps may exist because: Twitter limits the number of tweets returned by the filter at any point in time. Social Feed Manager stops and starts the Twitter filter stream every 30 minutes. In Social Feed Manager, collecting is turned off while a user is making changes to the collection criteria. There were some operational issues, e.g., network interruptions, during the collection period. Since some of the terms used to collect from the Twitter filter stream were broad (e.g., “election”), it may contain tweets from elections other than the U.S. presidential election, including state elections, local elections, or elections in other countries. Per Twitter’s Developer Policy, tweet ids may be publicly shared; tweets may not. Questions about this dataset can be sent to sfm@gwu.edu. George Washington University researchers should contact us for access to the tweets. This work is supported by grant #NARDI-14-50017-14 from the National Historical Publications and Records Commission.

Description to be added

Scholarship on women voters in the United States has focused on the gender gap showing that women are more likely to vote for Democratic Party candidates than men since the 1980s. The persistence of the gender gap has nurtured the conclusion that women are Democrats. This article presents evidence upending that conventional wisdom. Data from the American National Election Study are analyzed to demonstrate that white women are the only group of female voters who support Republican Party candidates for president. They have done so by a majority in all but 2 of the last 18 elections. The relevance of race for partisan choice among women voters is estimated with data collected in 2008, 2012, and 2016, and the significance of being white is identified after accounting for political party identification and other predictors.

This study contains selected electoral and aggregate economic, ecological, and demographic data at the congressional district level for districts of the 87th and 88th Congresses in the period 1961-1965. Data are provided for the number of votes cast for the Democratic and the Republican parties, and the percentage of votes cast for the majority party in the biennial elections for United States Representatives in the period 1952-1962, as well as the total votes cast for the office of president, and the number of votes cast for each party's presidential candidate in the 1952, 1956, and 1960 election. Data are also provided for population and housing characteristics, including total population by household, group quarters, institutions, age group, gender, marital status, race, nationality, and urban and rural residency. Additional demographic variables describe the congressional districts in terms of education, income, employment status and occupation, veteran status, births, deaths, and marriages.

Please read the collection notes below; there are many points to be aware of for this collection prior to analysis. This collection of historical election data contains state files that list county-level returns for over 90 percent of all elections to the offices of president, governor, United States senator, and United States representative from 1824 through 1968. The data files include returns for all parties and candidates (as well as write-in and scattering votes if available for individual states), and for special elections as well as regularly-scheduled contests. Over 1,000 individual party names and many additional unaffiliated candidates are included.