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) 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, 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).

In U.S. presidential elections since 1964, voter turnout among male and female voters has changed gradually but significantly, with women consistently voting at a higher rate than men since the 1980 election. 67 percent of eligible female voters took part in the 1964 election, compared to 72 percent of male voters. This difference has been reversed in recent elections, where the share of women who voted has been larger than the share of men by around four percent since 2004.

U.S. President general county level voter registration and turnout data for 1992-2022. Each level of data include the following: Total Population (state and county) Total Voting-Age Population (state only) Total Voter Registration (except ND, WI - these two states do not have voter registration.) Total Ballots Cast (for 2004, not yet available for NC, PA. WI doesn't publish this data) Total Vote Cast for President Voter Registration by Party (AK, AZ, CA, CO, CT, DE, DC, FL, IA, KS, KY, LA, MA, ME, MD, NE, NV, NH, NJ, NM, NY, NC, OK, OR, PA, SD, WV, WY). Remaining states do not have voter registration by party). The following worksheets are included in each file: National Summary - summarizes registration and turnout totals by state - with boundary file information (fips) Data by County - data for all counties of all states plus DC - with boundary file information (fips) Data by Town - data for New England towns (ME, MA, CT, RI, VT, NH) - with boundary file information (fips) Data Sources - a list of data sources used to compile the spreadsheet.

Provides demographic information on persons who did and did not register to vote. Also measures number of persons who voted and reasons for not registering.

Under this agreement the American Association of Motor Vehicles (AAMVA) will provide connectivity, billing services, and staff a help desk to the MVAs of States, District of Columbia, and territories of the US, for SSA. SSA will, through AAMVA's network, provide verification of certain voter registration information to the State MVAs for their use in the registration of voters for elections for Federal office. SSA is providing the verified information in accordance with the Help America Vote Act of 2002.

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.

Description to be added

This dataset represents the results of the 4-digit match performed using the Social Security - Help America Vote Verification (HAVV) system.

2020 Detailed Voter Registration and Turnout Data

Governments may have the capacity to flood social media with fake news, but little is known about the use of flooding by ordinary voters. In this work, we identify 2107 registered US voters that account for 80% of the fake news shared on Twitter during the 2020 US presidential election by an entire panel of 664,391 voters. We find that supersharers are important members of the network, reaching a sizable 5.2% of registered voters on the platform. Supersharers have a significant overrepresentation of women, older adults, and registered Republicans. Supersharers' massive volume does not seem automated but is rather generated through manual and persistent retweeting. These findings highlight a vulnerability of social media for democracy, where a small group of people distort the political reality for many. Methods This dataset contains aggregated information necessary to replicate the results reported in our work on Supersharers of Fake News on Twitter while respecting and preserving the privacy expectations of individuals included in the analysis. No individual-level data is provided as part of this dataset. The data collection process that enabled the creation of this dataset leveraged a large-scale panel of registered U.S. voters matched to Twitter accounts. We examined the activity of 664,391 panel members who were active on Twitter during the months of the 2020 U.S. presidential election (August to November 2020, inclusive), and identified a subset of 2,107 supersharers, which are the most prolific sharers of fake news in the panel that together account for 80% of fake news content shared on the platform. We rely on a source-level definition of fake news, that uses the manually-labeled list of fake news sites by Grinberg et al. 2019 and an updated list based on NewsGuard ratings (commercially available, but not provided as part of this dataset), although the results were robust to different operationalizations of fake news sources. We restrict the analysis to tweets with external links that were identified as political by a machine learning classifier that we trained and validated against human coders, similar to the approach used in prior work. We address our research questions by contrasting supersharers with three reference groups: people who are the most prolific sharers of non-fake political tweets (supersharers non-fake group; SS-NF), a group of average fake news sharers, and a random sample of panel members. In particular, we identify the distinct sociodemographic characteristics of supersharers using a series of multilevel regressions, examine their use of Twitter through existing tools and additional statistical analysis, and study supersharers' reach by examining the consumption patterns of voters that follow supersharers.

Boundaries of Orleans Parish voting precincts as defined by the New Orleans City Charter. New Orleans voting precincts are drawn according to the New Orleans Home Rule Charter as required by the State of Louisiana. A precinct is defined in the state of Louisiana's election code as the smallest political unit of a ward having defined geographical boundaries. Precinct boundaries were updated September 25, 2015, in order to satisfy population changes discovered by the Orleans Registrar of Voters Office. The changes have been made by the City of New Orleans and verified by the Louisiana Secretary of State's Office. Information about voter registation can be found here: https://www.sos.la.gov/ElectionsAndVoting/Pages/RegistrationStatisticsParish.aspx https://www.municode.com/library/la/new_orleans/codes/code_of_ordinances?nodeId=PTIICO_CH58EL_ARTIIELPRState LawRS 18:532. Establishment of precinctsA. Subject to the provisions of R.S. 18:532.1 and 1903, the governing authority of each parish shall establish precincts, define the territorial limits for which each precinct is established, prescribe their boundaries, and designate the precincts. The governing authority of each parish shall by ordinance adopt the establishment and boundaries of each precinct in accordance with the timetable as set forth herein and in accordance with R.S. 18:532.1.B.(1)(a) Each precinct shall be a contiguous, compact area having clearly defined and clearly observable boundaries coinciding with visible features readily distinguishable on the ground and approved extensions of such features, such as designated highways, roads, streets, rivers, or canals, and depicted on United States Bureau of the Census base maps for the next federal decennial census, except where the precinct boundary is coterminous with the boundary of a parish or an incorporated place when the boundaries of a single precinct contain the entire geographic area of the incorporated place. Except as otherwise provided in this Paragraph, on and after July 1, 1997, any precinct boundary which does not coincide with a visible feature shall be changed by the parish governing authority to coincide with a visible feature in accordance with R.S. 18:532.1.(b) For the purposes of this Paragraph, the term "approved extension" shall mean an extension of one visible feature to another visible feature which has been approved by the secretary of the Senate and the clerk of the House of Representatives or their designees and which is or which will be a census tabulation boundary.(2) No precinct shall be wholly contained within the territorial boundaries of another precinct, except that a precinct which contains the entire geographical area of an incorporated place and in which the total number of registered voters at the last general election was less than three hundred may be so contained.(3) No precinct shall contain more than two thousand two hundred registered voters within its geographic boundaries. Within thirty days after the completion of each canvass, the registrar of voters of each parish shall notify the parish governing authority of every precinct in the parish which contains more than two thousand two hundred registered voters within its geographic boundaries. Within sixty days of such notification, the parish governing authority shall divide such precincts by a visible feature in accordance with R.S. 18:532.1.(4)(a) No precinct shall contain less than three hundred registered voters within its geographical boundaries, except:(i) When necessary to make it more convenient for voters in a geographically isolated and unincorporated area to vote. A voter in a geographically isolated and unincorporated area shall mean a voter whose residen

Replication data and code for The Measurement of Partisan Sorting for 180 Million Voters by Jacob R Brown and Ryan D Enos, Nature Human Behavior 2021. Segregation across social groups is an enduring feature of nearly all human societies and is associated with numerous social maladies. In many countries, reports of growing geographic political polarization raise concerns about the stability of democratic governance. Here, using advances in spatial data computation, we measure individual partisan segregation by calculating the local residential segregation of every registered voter in the United States, creating a spatially weighted measure for more than 180 million individuals. With these data, we present evidence of extensive partisan segregation in the country. A large proportion of voters live with virtually no exposure to voters from the other party in their residential environment. Such high levels of partisan isolation can be found across a range of places and densities and are distinct from racial and ethnic segregation. Moreover, Democrats and Republicans living in the same city, or even the same neighbourhood, are segregated by party.

This repo contains the data introduced in

Immer, A.*, Kristof, V.*, Grossglauser, M., Thiran, P., Sub-Matrix Factorization for Real-Time Vote Prediction, KDD 2020

These data have been collected from OpenData.Swiss every two minutes on two different referendum vote days: May 19, 2019, and February 9, 2020. We use these data to make real-time predictions of the referenda outcome on www.predikon.ch. We publish here the raw data, as retrieved in JSON format from the API. We also provide a python script to help scraping the JSON files.

After unzipping the datasets, you can scrape the data by referendum vote day by doing:

from scraper import scrape_referenda

Scrape the data from February 2, 2020.

data_dir = 'path/to/2020-02-09' data = scrape_referenda(data_dir)

The data variable will be a list of datum dictionaries of the following structure:

{ "vote": 6290, "municipality": 1, "timestamp": "2020-02-09T15:23:10", "num_yes": 222, "num_no": 482, "num_valid": 704, "num_total": 709, "num_eligible": 1407, "yes_percent": 0.3153409090909091, "turnout": 0.503909026297086 }

The datum is as follows:

vote: vote ID as defined by OpenData.Swiss

municipality: municipality ID as defined by OpenData.Swiss

timestamp: date and time at which the JSON files has been published on OpenData.Swiss

num_yes: number of "yes" in the municipality

num_no: number of "no" in the municipality

num_valid: number of valid ballots (the ones counting for the results)

numb_total: total number of ballots (including invalid ones)

num_eligible: number of registered voters

yes_percent: percentage of "yes" (computed as num_yes / num_valid)

turnout: turnout to the vote (computed as num_total / num_eligible)

Don't hesitate to reach out to us if you have any questions!

To cite this dataset:

@inproceedings{immer2020submatrix, author = {Immer, Alexander and Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick}, title = {Sub-Matrix Factorization for Real-Time Vote Prediction}, year = {2020}, booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining}, }

This dataset shows voter registration totals by precinct, ward and district in Ramsey County.

March 1995: This survey was conducted by the Los Angeles Times from March 15-19, 1995 on a National sample of 1,007 registered voters. Major topics covered: Clinton job performance; party preference; US economy; Congress; Clinton in the 1996 election; abortion; Republican party politics; race relations; discrimination; affirmative action; Vietnam

October 1995: Right/wrong track; Bill Clinton job performance; party best able; Clinton vs. Congress; confidence in Congress; trusting government; government spending; economy; personal finances; political groups; favorability of political parties; two-party system; Hillary Rodham Clinton; welfare; environment; race relations; marriage; homosexual relations; Roe v. Wade; prayer in schools; moral climate; intolerance; immigration; United Nations; foreign policy; crime; assault weapons ban; own a handgun; on-line computer services; 1996 elections; third party; 1992 election; current employment; born again; the Bible.

February 1997: Direction of country; Bill Clinton job performance; congressional job performance; Clinton vs Republicans in Congress; issue with top priority; economy; Clinton impression; Newt Gingrich impression; State of the Union address; Clinton's proposals; balancing the budget; social security plans; financial health of the Medicare system; proposals to change Medicare; welfare reform bill; Clinton's ethics; Gingrich step down; Gingrich fine; Gingrich's punishment; Democratic National Committee; campaign finance reform bill; Clinton selling presidency; O.J. Simpson verdict; government medical insurance.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31093058 and https://doi.org/10.25940/ROPER-31093074. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

Replication data for the list of curating actors used in this work.

U.S. Public Records contains records from Bedford, Hillsborough, New Hampshire, USA by U.S., Public Records Index, 1950-1993, Volume 1; Ancestry.com. U.S., Public Records Index, 1950-1993, Volume 1 [database on-line]. Lehi, UT, USA: Ancestry.com Operations, Inc., 2010.; Original data: Voter Registration Lists, Public Record Filings, Historical Residential Records, and Other Household Database Listings. - .

This dataset contains data about domestic absentee voting, provisional balloting, poll books, polling place, precincts, poll workers, and voting technology used in the 2012 election cycle. The corresponding comprehensive report addresses voter registration, uniformed and overseas citizen voting, domestic absentee voting, provisional balloting, poll books, polling place, precincts, poll workers, and voting technology used in the 2012 election. The Election Administration and Voting Survey report is part of EAC's Election Administration and Voting Survey biennial project.

The precincts displayed on this map were developed based on the 2020 U.S Census blocks and revised by the City of Boston Election Commission to conform to the voting precinct guidelines. These precincts were adopted in 2022 by the Board of Election Commission and by the MA State Legislature. Sources: U.S. Census, voter Registration database, and Massachusetts Secretary of the Commonwealth. This map is intended for planning and visualization purposes only.

Results for the Massachusetts U.S. Senate Special Election Poll are based on telephone interviews with a random sample of 589 Massachusetts registered voters. Telephone interviews were conducted by landline (n=411) and cell phone (n=178). The survey was conducted by RKM Research and Communications (RKM). Interviews were conducted in English, March 2-5, 2013. The survey was administered using a computer-assisted telephone interviewing (CATI) system. The CATI system allows data to be entered directly into a computerized database as interviews are conducted. A central polling facility in Portsmouth, New Hampshire was used to administer the survey. All interviews were conducted by paid, trained and professionally supervised interviewers.