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.

The 2016 U.S. presidential election was contested by Donald J. Trump of the Republican Party, and Hillary Rodham Clinton of the Democratic Party. Clinton had been viewed by many as the most likely to succeed President Obama in the years leading up to the election, after losing the Democratic nomination to him in 2008, and entered the primaries as the firm favorite. Independent Senator Bernie Sanders soon emerged as Clinton's closest rival, and the popularity margins decreased going into the primaries. A few other candidates had put their name forward for the Democratic nomination, however all except Clinton and Sanders had dropped out by the New Hampshire primary. Following a hotly contested race, Clinton arrived at the Democratic National Convention with 54 percent of pledged delegates, while Sanders had 46 percent. Controversy emerged when it was revealed that Clinton received the support of 78 percent of Democratic superdelegates, while Sanders received just seven percent. With her victory, Hillary Clinton became the first female candidate nominated by a major party for the presidency. With seventeen potential presidential nominees, the Republican primary field was the largest in US history. Similarly to the Democratic race however, the number of candidates thinned out by the time of the New Hampshire primary, with Donald Trump and Ted Cruz as the frontrunners. As the primaries progressed, Trump pulled ahead while the remainder of the candidates withdrew from the race, and he was named as the Republican candidate in May 2016. Much of Trump's success has been attributed to the free media attention he received due to his outspoken and controversial behavior, with a 2018 study claiming that Trump received approximately two billion dollars worth of free coverage during the primaries alone. Campaign The 2016 presidential election was preceded by, arguably, the most internationally covered and scandal-driven campaign in U.S. history. Clinton campaigned on the improvement and expansion of President Obama's more popular policies, while Trump's campaign was based on his personality and charisma, and took a different direction than the traditional conservative, Republican approach. In the months before the election, Trump came to represent a change in how the U.S. government worked, using catchy slogans such as "drain the swamp" to show how he would fix what many viewed to be a broken establishment; painting Clinton as the embodiment of this establishment, due to her experience as First Lady, Senator and Secretary of State. The candidates also had fraught relationships with the press, although the Trump campaign was seen to have benefitted more from this publicity than Clinton's. Controversies Trump's off the cuff and controversial remarks gained him many followers throughout the campaign, however, just one month before the election, a 2005 video emerged of Trump making derogatory comments about grabbing women "by the pussy". The media and public's reaction caused many high-profile Republicans to condemn the comments (for which he apologized), with many calling for his withdrawal from the race. This controversy was soon overshadowed when it emerged that the FBI was investigating Hillary Clinton for using a private email server while handling classified information, furthering Trump's narrative that the Washington establishment was corrupt. Two days before the election, the FBI concluded that Clinton had not done anything wrong; however the investigation had already damaged the public's perception of Clinton's trustworthiness, and deflected many undecided voters towards Trump. Results Against the majority of predictions, Donald Trump won the 2016 election, and became the 45th President of the United States. Clinton won almost three million more votes than her opponent, however Trump's strong performance in swing states gave him a 57 percent share of the electoral votes, while Clinton took just 42 percent. The unpopularity of both candidates also contributed to much voter abstention, and almost six percent of the popular vote went to third party candidates (despite their poor approval ratings). An unprecedented number of faithless electors also refused to give their electoral votes to the two main candidates, instead giving them to five non-candidates. In December, it emerged that the Russian government may have interfered in this election, and the 2019 Mueller Report concluded that Russian interference in the U.S. election contributed to Clinton's defeat and the victory of Donald Trump. In total, 26 Russian citizens and three Russian organizations were indicted, and the investigation led to the indictment and conviction of many top-level officials in the Trump campaign; however Trump and the Russian government both strenuously deny these claims, and Trump's attempts to frame the Ukrainian government for Russia's involvement contributed to his impeachment in 2019.

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.

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.

This dataset was used to conduct the NYC Campaign Finance Board's voter participation research, published in the 2019-2020 Voter Analysis Report. Each row contains information about an active voter in 2018 and their voting history dating back to 2008, along with geographical information from their place of residence for each year they were registered voters. Because this dataset contains only active voters in the year 2018, this dataset cannot be used to calculate election turnout.

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.

This dataset represents the results of the 4-digit match performed using the Social Security - Help America Vote Verification (HAVV) system. Report for 2024.

Voting DistrictsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), depicts Voting Districts (VTDs) in the United States and Puerto Rico. Per the USCB, "VTDs refer to the generic name for geographic entities, such as precincts, wards, and election districts, established by state governments for the purpose of conducting elections.”Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Voting Districts) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: Not availableGeoplatform: Not availableFor more information, please visit: Voting and RegistrationFor feedback please contact: Esri_US_Federal_Data@esri.comThumbnail image courtesy of Mrs. GemstoneNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

This contains data relevant for the 2016 US Presidential Election, including up-to-date primary results.

Exploration Ideas

• What candidates within the Republican party have results that are the most anti-correlated?
• Which Republican candidate is Hillary Clinton most correlated with based on county voting patterns? What about Bernie Sanders?
• What insights can you discover by mapping this data?

Do you have answers or other exploration ideas? Add your ideas to this forum post and share your insights through Kaggle Scripts!

Do you think that we should augment this dataset with more data sources? Let us know here!

Data Description

The 2016 US Election dataset contains several main files and folders at the moment. You may download the entire archive via the "Download Data" link at the top of the page, or interact with the data in Kaggle Scripts through the ../input directory.

• PrimaryResults.csv: main primary results file
  • State: state where the primary or caucus was held
  • StateAbbreviation: two letter state abbreviation
  • County: county where the results come from
  • Party: Democrat or Republican
  • Candidate: name of the candidate
  • Votes: number of votes the candidate received in the corresponding state and county (may be missing)
  • FractionVotes: fraction of votes the president received in the corresponding state, county, and primary
• database.sqlite: SQLite database containing the PrimaryResults table with identical data and schema
• county_shapefiles: directory containing county shapefiles at three different resolutions for mapping

Original Data Sources

• Iowa County-Level Results
• New Hampshire County-Level Results
• County Shapefiles

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.

Abstract (en): This instructional package includes a student manual containing six exercises, an instructor's guide, and four subsets of data required for use in conjunction with the manual's exercises. The package's major purpose is to enable students to examine certain substantive questions about electoral behavior through analysis of political survey data. The manual avoids instruction in methodology, per se, hence the student is taken no further than the analysis of straightforward variables in percentagized tables with and without controls, and is introduced to epsilon, the percentage difference measures based on 2 X 2 tables, but offered no elaborate discussion of measures of association. The six structured exercises introduce the basic analytic techniques necessary for coping with survey data in the expectation that the students will then move on to their own topics. The datasets were designed to be both substantively and analytically interesting, as students are forced continually to make choices and judgments about which variables to use and how to combine code categories. Beyond this, the exercises serve a more complex purpose: to help the student gain a better understanding of the existing scholarly literature by going through steps similar to those of the original analysts. In some instances, the students can readily appreciate how close their work is to the analysis in assigned reading. The instructor's guide has two purposes: first, to help instructors use the student manual effectively, and second, to suggest various ways to depart from the six exercises and to develop essentially new manuals. The subsets (Parts 1-4) contain data from every presidential election survey that was conducted by the Survey Research Center (SRC) and Center for Political Studies (CPS) (at the University of Michigan) from 1952 to 1980. Part 4 contains an extensive set of variables drawn exclusively from the CPS's AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763). This is the only deck needed to complete the exercises in Exercises l-5. Part 1 includes small sets of comparable variables from each SRC/CPS presidential election study from 1952-1972. The variables in these decks were selected with the intention of providing students with a range of interesting possibilities for original research topics for term papers. Part 2 includes variables and respondents from panel surveys contained in AMERICAN NATIONAL ELECTION SERIES: 1972, 1974, 1976 (ICPSR 7607). This dataset may be used with Exercise 6. Supplementing the panel file is the data in Part 3, based on the cross-section survey, AMERICAN NATIONAL ELECTION STUDY, 1976 (7381). It repeats the variables from the 1976 component of the panel, with a much larger N. The AMERICAN NATIONAL ELECTION STUDY, 1976 (7381) may be used independently, as with the AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763), or it may be used in exercises comparing cross-section with panel data. Data used for the exercises were made available by ICPSR. The major analyses of these data have appeared in two publications: (1) University of Michigan. Survey Research Center. THE AMERICAN VOTER. New York, NY: Wiley, 1960, and (2) Campbell, Angus, Philip Converse, Warren Miller, and Donald Stokes. ELECTIONS AND THE POLITICAL ORDER. New York, NY: Wiley, 1966. 2006-01-12 All files were removed from dataset 6 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 5 and flagged as study-level files, so that they will accompany all downloads. The codebooks, Student Manual for All Parts and the Guide to Instruction for All Parts, are provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Terms of Access: By downloading the data, you agree to use the data only for academic research, agree not to share the data with outside parties, and agree not to attempt to re-identify individuals in the data set. We require this in order to protect the privacy of individuals in the data set and to comply with agreements made with TargetSmart. Abstract: We present the results of a large, $8.9 million campaign-wide field experiment, conducted among 2 million moderate and low-information “persuadable” voters in five battleground states during the 2020 US Presidential election. Treatment group subjects were exposed to an eight-month-long advertising program delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden. We found no evidence the program increased or decreased turnout on average. We find evidence of differential turnout effects by modeled level of Trump support: the campaign increased voting among Biden leaners by 0.4 percentage points (SE: 0.2pp) and decreased voting among Trump leaners by 0.3 percentage points (SE: 0.3pp), for a difference-in-CATES of 0.7 points that is just distinguishable from zero (t(1035571) = −2.09, p = 0.036, DIC = 0.7 points, 95% CI = [−0.014, −0.00]). An important but exploratory finding is that the strongest differential effects appear in early voting data, which may inform future work on early campaigning in a post-COVID electoral environment. Our results indicate that differential mobilization effects of even large digital advertising campaigns in presidential elections are likely to be modest.

The data we use comes from government agencies and mainstream media. We fill the missing data using regression imputation. The pandemic data comes from the Center for Diseases Control and Prevention (CDC) data tracker. As of Nov. 25, 2020, the death toll reached 261k in the U.S.

Adult Ranking is used to assess the possible effects of the national result on its voters’ mental wellness. States that are ranked 1-13 have a lower prevalence of mental illness and higher rates of access to care for adults. The states with higher rank (39-51) indicate that adults have a higher prevalence of mental illness and lower rates of access to care. There are seven measures that makeup Adult Ranking, including any mental illness, disorder, suicided, etc. Physical scientist (2018) is used as the representative of the College group. We extract the data from the U.S. Bureau of Labor Statistics. The spirit of independence and freedom in thinking is particularly important in academic studies and research. The underlying assumption of choosing this factor

is the higher number of physical scientists, the freer academic atmosphere in which people can pursue truth, exercise reasoning, and respect science. Horse racing is one of the oldest of all sports that have taken place in the United States, which provides normous economic, employment, and social contribution for entertainment sector. It represents the traditional economy in the analysis. We use steed sales from USDA (United States Department of Agriculture) as an indicator of the horse racing prosperity, in other prospects, traditional economy.

Proxy voting records for the New York City Retirement Systems. This data set sets forth how the New York City Retirement Systems have voted their shares in the companies in which they hold stock. These records reflect how the the Systems voted on such issues as board of director elections and company and shareholder proposals.

According to results on November 6, 2024, former President Donald Trump had received 277 Electoral College votes in the race to become the next President of the United States, securing him the presidency. With all states counted, Trump received a total of 312 electoral votes.

Candidates need 270 votes to become the next President of the United States.

This dataset lists the total population 18 years and older by census block in Connecticut before and after population adjustments were made pursuant to Public Act 21-13. PA 21-13 creates a process to adjust the U.S. Census Bureau population data to allow for most individuals who are incarcerated to be counted at their address before incarceration. Prior to enactment of the act, these inmates were counted at their correctional facility address.

The act requires the CT Office of Policy and Management (OPM) to prepare and publish the adjusted and unadjusted data by July 1 in the year after the U.S. census is taken or 30 days after the U.S. Census Bureau’s publication of the state’s data.

A report documenting the population adjustment process was prepared by a team at OPM composed of the Criminal Justice Policy and Planning Division (OPM CJPPD) and the Data and Policy Analytics (DAPA) unit. The report is available here: https://portal.ct.gov/-/media/OPM/CJPPD/CjAbout/SAC-Documents-from-2021-2022/PA21-13_OPM_Summary_Report_20210921.pdf

Note: On September 21, 2021, following the initial publication of the report, OPM and DOC revised the count of juveniles, reallocating 65 eighteen-year-old individuals who were incorrectly designated as being under age 18. After the DOC released the updated data to OPM, the report and this dataset were updated to reflect the revision.

Locations of voting/poll sites through the city.

The 1992 US presidential election was contested by incumbent President George H. W. Bush of the Republican Party, the Democratic Party's Bill Clinton, and independent candidate Ross Perot. Bush won his party's re-nomination easily, however the the nature of his opponents (which included David Duke; Grand Wizard of the Klu Klux Klan) pushed him to take a more conservative approach throughout the remainder of his campaign, alienating many moderates in the process. Due to the Bush's popularity following the US' success in the Gulf War, many prominent Democrats decided not to run against him in the 1992 election, which meant that most of the candidates were relatively unknown. There was no clear frontrunner by the time of the first primary elections, but Clinton, despite widespread accusations of an extramarital affair, eventually secured the required number of delegates as the other candidates dropped out. Campaign The weakened economy and federal budget deficit led to some dissatisfaction with Bush's administration, and independent billionaire Ross Perot capitalized on the economic concerns of the public by launching his own campaign. In spring 1992, Perot was leading in the polls, with Bush in second place. Shortly after the race began, Perot dropped out as he feared that his involvement would prevent any of the candidates from securing a majority of electoral votes. Clinton, with Al Gore as his running mate, campaigned all over the country, promising to repair the wealth gap that had appeared under the Reagan and Bush administrations. Bush proceeded to repeat the accusations of infidelity against Clinton, as well as highlighting how Clinton dodged the Vietnam War draft. The economic decline, however, meant that Bush's ratings continued to fall, and neither his foreign policy successes nor the end of the Cold War could rescue his numbers. As Clinton moved ahead, Perot re-entered the race, and while his numbers were initially low, his performance in the three-way televised debates saw his ratings increase at Clinton's expense. In the final days of the election, Bush and Perot again began to attack Clinton personally, accusing him of adultery, draft dodging and using drugs (which led to Clinton's famous claim that he had once pretended to smoke marijuana, but did not inhale). Results Clinton emerged victorious from the election, winning in 32 states (plus DC) and taking over two thirds of the electoral vote. In spite of his victory, this was the lowest share of the popular vote by a winning candidate since 1912 (which was also a three-way race). Ross Perot's impact was unprecedented, and because of his involvement, only Clinton's home state of Arkansas and Washington DC actually gave the majority of their votes to one candidate (Bush and Perot were both from Texas). Although Perot failed to win any electoral college votes, he won the largest share of the popular vote by any third party candidate since Theodore Roosevelt's tally in 1912. George H. W. Bush was the last president to have been voted out of office after just one term. His son, George W. Bush, would go on to succeed Clinton, with his victory in the 2000 US presidential election.

The 2020 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Voting district is the generic name for geographic entities such as precincts, wards, and election districts established by State governments for the purpose of conducting elections. States participating in the 2020 Census Redistricting Data Program as part of Public Law 94-171 (1975) provided the Census Bureau with boundaries, codes, and names for their VTDs. Each VTD is identified by a 1- to 6-character alphanumeric census code that is unique within county.