According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of surveyed white voters reported voting for Donald Trump. In contrast, ** percent of Black voters reported voting for Kamala Harris.

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

According to exit polling in ten key states of the 2024 presidential election in the United States, Donald Trump received the most support from men between the ages of ** and **. In comparison, ** percent of women between the ages of ** and ** reported voting for Kamala Harris.

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.

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.

According to exit polling in ten key states of the 2024 presidential election in the United States, Donald Trump received the most support from white voters between the ages of ** and **. In comparison, ** percent of Black voters between the ages of ** and ** reported voting for Kamala Harris.

According to exit polling in *** key states of the 2024 presidential election in the United States, almost ********** of voters who had never attended college reported voting for Donald Trump. In comparison, a similar share of voters with ******** degrees reported voting for Kamala Harris.

Election Data Attribute Field Definitions | Wisconsin Cities, Towns, & Villages Data Attributes Ward Data Overview: January 2025 municipal wards were collected in January 2025 by LTSB through LTSB's GeoData Collector. Current statutes require each county clerk, or board of election commissioners, no later than January 15 and July 15 of each year, to transmit to the LTSB, in an electronic format (approved by LTSB), a report confirming the boundaries of each municipality, ward and supervisory district within the county as of the preceding “snapshot” date of January 1 or July 1 respectively. Population totals for 2025 wards were estimated by aggregating 2020 US Census PL94-171 population data. LTSB has NOT topologically integrated the data. Election Data Overview: The 2024 Wisconsin election data that is included in this file was collected by LTSB from the *Wisconsin Elections Commission (WEC) after the general election. A disaggregation process was performed on this election data based on the municipal ward layer that was available at the time of the election. Disaggregation of Election Data: Election data is first disaggregated from reporting units to wards, and then to census blocks. Next, the election data is aggregated back up to wards, municipalities, and counties. The disaggregation of election data to census blocks is done based on total population. Detailed Methodology:Data is disaggregated first from reporting unit (i.e. multiple wards) to the ward level proportionate to the population of that ward.The data then is distributed down to the block level, again based on total population.When data is disaggregated to block or ward, we restrain vote totals not to exceed population 18 numbers, unless absolutely required.This methodology results in the following: Election data totals reported to the WEC at the state, county, municipal and reporting unit level should match the disaggregated election data total at the same levels. Election data totals reported to the WEC at ward level may not match the ward totals in the disaggregated election data file.Some wards may have more election data allocated than voter age population. This will occur if a change to the geography results in more voters than the 2020 historical population limits.Other things of note… We use a static, official ward layer (in this case created in 2025) to disaggregate election data to blocks. Using this ward layer creates some challenges. New wards are created every year due to annexations and incorporations. When these new wards are reported with election data, an issue arises wherein election data is being reported for wards that do not exist in our official ward layer. For example, if "Cityville" has four wards in the official ward layer, the election data may be reported for five wards, including a new ward from an annexation. There are two different scenarios and courses of action to these issues: When a single new ward is present in the election data but there is no ward geometry present in the official ward layer, the votes attributed to this new ward are distributed to all the other wards in the municipality based on population percentage. Distributing based on population percentage means that the proportion of the population of the municipality will receive that same proportion of votes from the new ward. In the example of Cityville explained above, the fifth ward may have five votes reported, but since there is no corresponding fifth ward in the official layer, these five votes will be assigned to each of the other wards in Cityville according the percentage of population.Another case is when a new ward is reported, but its votes are part of reporting unit. In this case, the votes for the new ward are assigned to the other wards in the reporting unit by population percentage; and not to wards in the municipality as a whole. For example, Cityville’s ward five was given as a reporting unit together with wards 1, 4, and 5. In this case, the votes in ward five are assigned to wards one and four according to population percentage. Outline Ward-by-Ward Election ResultsThe process of collecting election data and disaggregating to municipal wards occurs after a general election, so disaggregation has occurred with different ward layers and different population totals. We have outlined (to the best of our knowledge) what layer and population totals were used to produce these ward-by-ward election results.Election data disaggregates from WEC Reporting Unit -> Ward [Variant year outlined below]Elections 1990 – 2000: Wards 1991 (Census 1990 totals used for disaggregation)Elections 2002 – 2010: Wards 2001 (Census 2000 totals used for disaggregation)Elections 2012: Wards 2011 (Census 2010 totals used for disaggregation)Elections 2014 – 2016: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2018: Wards 2018 (Census 2010 totals used for disaggregation)Elections 2020: Wards 2020 (Census 2020 totals used for disaggregation)Elections 2022: Wards 2022 (Census 2020 totals used for disaggregation)Elections 2024: Wards 2025 (Census 2020 totals used for disaggregation)Blocks -> Centroid geometry and spatially joined with Wards [All Versions]Each Block has an assignment to each of the ward versions outlined above.In the event that a ward exists now in which no block exists due to annexations, a block centroid was created with a population 0, and encoded with the proper Census IDs.Wards [All Versions] disaggregate -> Blocks This yields a block centroid layer that contains all elections from 1990 to 2024.Blocks [with all election data] -> Wards 2025 (then MCD 2025, and County 2025) All election data (including later elections) is aggregated to the Wards 2025 assignment of the blocks.Notes:Population of municipal wards 1991, 2001, 2011, 2020, 2022, and 2025 used for disaggregation were determined by their respective Census.Population and Election data will be contained within a county boundary. This means that even though MCD and ward boundaries vary greatly between versions of the wards, county boundaries have stayed the same, so data should total within a county the same between wards 2011 and wards 2025.Election data may be different for the same legislative district, for the same election, due to changes in the wards from 2011 and 2025. This is due to boundary corrections in the data from 2011 to 2025, and annexations, where a block may have been reassigned.*WEC replaced the previous Government Accountability Board (GAB) in 2016, which replaced the previous State Elections Board in 2008.

According to an October 2024 survey, young Americans were much more likely to vote for Kamala Harris in the November 2024 presidential elections. Of those between the ages of 18 and 29, 60 percent said they were planning on voting for Harris, compared to 33 percent who said they planned on voting for Trump. In contrast, Trump was much more popular among those between 45 and 64 years old.

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

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

Why This Matters

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

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

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

The District's Response

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

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

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

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

2024 Primary & General Elections VTDs Voting Tabulation Districts (VTDs), the census geographic equivalent of county election precincts, are created for the purpose of relating 2020 Census population data to election precinct data. VTDs can differ from actual election precincts because precincts do not always follow census geography. The VTDs currently included in the redistricting database closely correspond to the precincts in effect for the 2024 primary and general elections. On the occasion that a precinct is in two noncontiguous pieces, it is a suffixed VTD in the database. For example, if precinct 0001 had two non-contiguous areas, the corresponding VTD would be VTD 0001A and VTD 0001B. If an election precinct does not match any census geography, it is consolidated with an adjacent precinct and given that precinct's corresponding VTD number. There are 9,712 VTDs in the 2024 primary & general elections VTDs shapefile. GIS users can join the council's redistricting election datasets to the 2024 primary & general elections VTDs shapefile in this directory. Use the common field name 'VTDKEY' to join the data. GIS users can join 2020 Census population data (VTDs_24PG_Pop.zip) to the 2024 primary & general elections VTDs shapefile in this directory. Use the common field name 'VTDKEY' to join the data. The VTDs shapefile (.shp) is in a compressed file (.zip) format: VTDs_24PG.zip - 2024 Primary & General Elections VTDs CNTY (num) - County FIPS Census code COLOR (num) - Color assignment for symbology VTD (txt) - VTD name (2024 general election) CNTYKEY (num) - Unique code used to join to geographic data VTDKEY (num) - Unique code used to join to geographic data CNTYVTD (txt) - Unique code used to join geographic data (CNTYKEY + VTD) The population data file contains the 2020 Census population by VTD as comma-separated values: VTDs_24PG_Pop.zip (.txt file in compressed format) - 2024 primary & general elections VTD, 2020 Census population CountyFIPS (txt) - County FIPS Census Code County (txt) - County name CNTY (num) - County FIPS Census Code VTD (txt) - VTD name (2024 general election) CNTYVTD (txt) - Unique code used to join geographic data (CNTY + VTD) VTDKEY (num) - Unique code used to join to geographic data total (num) - Total Population

Description

This dataset contains detailed information on the candidates, parties, and voting statistics for the 2024 Lok Sabha elections in India. The data has been collated from multiple sources to provide a comprehensive overview of the election outcomes across different states and constituencies.

Dataset Files

phase_data.xlsx: Contains data segmented by election phases. GE India 2024.xlsx: Includes detailed election results. eci_data_2024.csv: Provides candidate-wise voting details.

Usage:

This dataset can be used for various analyses, including:

Election result prediction. Voter turnout analysis. Party performance assessment. Demographic influence on election outcomes.

Data Source

Data has been sourced from the Election Commission of India and other reliable sources to ensure accuracy and comprehensiveness.

According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of Protestant Christian voters reported voting for Donald Trump. In comparison, only ** percent of Jewish voters reported voting for Trump.

Abstract: This repository contains the full dataset and model implementation for the analysis of voting patterns in Romania's 2024 presidential elections, focusing on the relationship between territorial economic structures and electoral preferences. The models estimate vote dominance at LAU level using sectoral, demographic, and regional predictors, including spatial autoregression. Particular attention is given to the overrepresentation of Bucharest in national-level FDI statistics, which is corrected through a GDP-based imputation model. For reproducibility, the repository includes: Cleaned and structured input data (LAU, NUTS3), all modelling scripts in R, Tableau maps for visual analysis and public presentation.File DescriptionsLAU.csvThis dataset contains the local-level electoral and socio-economic data for all Romanian LAU2 units used in the spatial and statistical analyses. The file is used as the base for all models and includes identifiers for merging with the shapefile or spatial weights. It includes:- Electoral results by presidential candidate (2024, simulated),- Dominant vote type per locality,- Sectoral employment categories,- Demographic variables (ethnicity, education, age),- Regional and metropolitan classifications,- Weights for modelling.NUTS3.csvThis dataset provides county-level economic indicators (GDP and FDI) over the period 2011–2022. The file supports the construction of regional indicators such as FDI-to-GDP ratios and export structure. Notably, the file includes both original and corrected values of FDI for Bucharest, following the imputation procedure described in the model script.model.RThis R script contains the full modelling pipeline. The script includes both a model variant with Bucharest excluded and an alternative version using corrected FDI values, confirming the robustness of coefficients across specifications. It includes:- Pre-processing of LAU and NUTS3 data,- Imputation of Bucharest FDI using a linear model on GDP,- Survey-weighted logistic regression models for vote dominance per candidate,- Multinomial and hierarchical logistic models,- Seemingly Unrelated Regressions (SUR),- Spatial error models (SEM),- Principal Component Analysis on SEM residuals,- Latent dominance prediction using softmax transformation,- Export of predicted latent vote maps.Maps.twbxThis Tableau workbook contains all final cartographic representations.The workbook uses a consistent colour palette based on candidate-typified economic structures (industry, services, agriculture, shrinking).- Choropleth maps of dominant vote by candidate,- Gradients reflecting latent probabilities from spatial models,- Maps of residuals and ideological factor scores (PCA-derived),- Sectoral economic geographies per county and per locality,- Overlay of dominant vote and sectoral transformation types.

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).

Voting data for the November 2024 election in Roanoke County, Virginia, by voting district. Includes presidential, Senate, House of Representatives, and Town of Vinton results. Data from here.

This dataset presents comprehensive voter statistics for all 288 Assembly Constituencies of Maharashtra, based on the 2024 Maharashtra Legislative Assembly Elections. It offers deep insights into the electoral landscape, including demographic details, voting patterns, special electors, and poll percentages — making it a valuable resource for data analysts, political scientists, journalists, and civic researchers.

The Maharashtra Assembly Elections or Maharashtra Vidhan Sabha is the lower house of the state legislature. A total of 288 members are directly elected from single-seat constituencies. The 15th Maharashtra Legislative Assembly was constituted following the 2024 state elections, with the results announced on 23 November 2024. A party or coalition must secure at least 145 seats to form a majority government.

This is a dedicated data collection implemented on a voluntary basis, based on agreement between Eurostat and the national statistical institutes of the EU Member States. This data collection concerns persons eligible to vote in the 2024 European Parliament elections.

The 2024 European Parliament elections took place on 6-9 June 2024. This dataset shows national estimates of the number of eligible voters; it does not show the number of persons who used their rights to vote.

Given the high public interest, Eurostat launched a dedicated data collection to receive the available or estimated statistics from the competent National Authorities.

The organisation of the 2024 European Parliament elections is under national responsibility. This includes the preparation of electoral lists. Regarding the data on eligible voters, according to Council Decision (EU, Euratom) 2018/994 of 13 July 2018, each Member State shall designate a contact authority responsible for exchanging data on voters with its counterparts in the other Member States.

According to results on November 6, 2024, former President Donald Trump had received *** 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 *** electoral votes. Candidates need *** votes to become the next President of the United States.