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 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, ** percent of surveyed white voters reported voting for Donald Trump. In contrast, ** percent of Black voters 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.

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

Dave Leip's website

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

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

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

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

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.

Abstract

The L2 Voter and Demographic Dataset includes demographic and voter history tables for all 50 states and the District of Columbia. The dataset is built from publicly available government records about voter registration and election participation. These records indicate whether a person voted in an election or not, but they do not record whom that person voted for. Voter registration and election participation data are augmented by demographic information from outside data sources.

Methodology

To create this file, L2 processes registered voter data on an ongoing basis for all 50 states and the District of Columbia, with refreshes of the underlying state voter data typically at least every six months and refreshes of telephone numbers and National Change of Address processing approximately every 30 to 60 days. These data are standardized and enhanced with propriety commercial data and modeling codes and consist of approximately 185,000,000 records nationwide.

Usage

For each state, there are two available tables: demographic and voter history. The demographic and voter tables can be joined on the LALVOTERIDvariable. One can also use the LALVOTERIDvariable to link the L2 Voter and Demographic Dataset with the L2 Consumer Dataset.

In addition, the LALVOTERIDvariable can be used to validate the state. For example, let's look at the LALVOTERID = LALCA3169443. The characters in the fourth and fifth positions of this identifier are 'CA' (California). The second way to validate the state is by using the RESIDENCE_ADDRESSES_STATEvariable, which should have a value of 'CA' (California).

The date appended to each table name represents when the data was last updated. These dates will differ state by state because states update their voter files at different cadences.

The demographic files use 698 consistent variables. For more information about these variables, see 2025-01-10-VM2-File-Layout.xlsx.

The voter history files have different variables depending on the state. The ***2025-08-05-L2-Voter-Dictionaries.tar.gz file contains .csv data dictionaries for each state's demographic and voter files. While the demographic file data dictionaries should mirror the 2025-01-10-VM2-File-Layout.xlsx*** file, the voter file data dictionaries will be unique to each state.

***2025-04-24-National-File-Notes.pdf ***contains L2 Voter and Demographic Dataset ("National File") release notes from 2018 to 2025.

***2025-08-05-L2-Voter-Fill-Rate.tar.gz ***contains .tab files tracking the percent of non-null values for any given field.

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

About Dataset

Sri Lanka Parliamentary Election Results 2024

This dataset offers a comprehensive collection of official results from the 2024 Parliamentary Elections in Sri Lanka, sourced directly from the Election Commission of Sri Lanka. It provides detailed voting statistics at both the district and polling division levels, capturing the complete electoral landscape of the country.

Dataset Files

1. All Divisions

   • Contains detailed vote counts for each polling division, including party-wise vote distributions, valid and rejected votes, and voter turnout percentages.

2. Symbols

   • Includes information on party symbols, useful for visualizing and identifying parties in election results and reports.

3. All Island Results Summary.csv

   • Aggregated nationwide statistics, such as:
     • Total valid votes
     • Rejected votes
     • Total polled voters
     • Registered electors
     • Percentage breakdowns for turnout and rejected votes.

4. All Island Results.csv

   • Detailed district-level results, providing:
     • Total valid votes, rejected votes, and voter turnout.
     • Party-wise performance and vote shares.

5. District_Divisions.csv

   • Maps districts to their respective polling divisions, making it easier to navigate and analyze regional data.

Potential Uses

Political and Electoral Analysis:

• Study voter turnout and trends across districts.
• Analyze the performance of political parties in different regions.
• Compare results with historical elections to identify patterns.

Machine Learning Applications:

• Develop predictive models for election outcomes.
• Visualize voter turnout trends and regional voting patterns.
• Build clustering models for party performance or voter demographics.

Non-Machine Learning Applications:

• Create heatmaps for voter turnout and party performance.
• Perform sociopolitical research by integrating additional demographic or economic data.
• Generate reports or infographics for media or public use.

Additional Information

• Data Format:
  The dataset is clean, structured, and presented in CSV format, making it user-friendly for analysis and integration with other tools.

• Insights and Trends:
  Explore how different regions participated in the election, analyze the distribution of valid and rejected votes, and evaluate party-wise performances.

This dataset is a valuable resource for political analysts, researchers, data scientists, and journalists. Whether for academic research, media reporting, or practical applications, this dataset provides a robust foundation to explore the intricacies of Sri Lanka's 2024 parliamentary elections.

Feel free to use this dataset to uncover unique patterns, trends, and narratives in the electoral landscape of Sri Lanka.

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

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.

During the weeks leading up to the presidential election, early voting began in almost all states, with over ** million ballots being cast nationally as of Election Day. Although ** percent of mail-in and early in-person votes were cast by voters aged 65 or older, ** percent of those aged 18 to 29 years old voted early.

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.

This project crosses the results of the 2024 European elections with INSEE demographic data at IRIS level (2020), for the whole of metropolitan France.

This join is done geographically, thanks to the reconstruction of the geometry of the polling stations produced by this other project

The aim is to allow for fine-grained demographic statistics on recent political trends.

Github depot: https://github.com/raphaeljolivet/eu2024-stats-iris

This dataset contains a spreadsheet with the participant's alias together with some political and socio-demographic data and the anonymised pre-election transcripts. There are twenty-four transcripts in the dataset, one set are in Word and the other in OpenDocument Text. Files are named according to who participated in the focus group or interview. Each transcript contains a table with some basic sociodemographic and political data on the participants.

Participants participated in an icebreaker and were asked their opinions on the snap election, the party leaders, what considerations were going into their vote choice, and impressions of the campaign. If there was time or it was not discussed, they were asked which campaign messages stood out to them and their views on tactical voting.

All participants’ names were changed to a permanent alias that allows them to be tracked across elections and any direct or indirect identifiers removed to protect their anonymity.  The transcripts were then formatted to create two levels of headings: topics and aliases. This is designed to help researchers more easily find the information they need. Please be aware that while topic headings have been added to the transcripts, participants sometimes provide information that anticipates later questions or provide additional information to a prior question later in the in discussion. If you are interested in a particular topic, we encourage to review the entire transcript to capture all the relevant data.

The post-election dataset and leader’s evaluation answers data will be released in 2025.

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

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.

According to exit polling in ten key states of the 2024 presidential election in the United States, ** percent of voters with a 2023 household income of ****** U.S. dollars or less reported voting for Donald Trump. In comparison, ** percent of voters with a total family income of 100,000 to ******* U.S. dollars reported voting for Kamala Harris.

The table IN-Demographic-2025-07-03 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 4433072 rows across 698 variables.

The table AZ-Demographic-2025-08-01 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 4428765 rows across 698 variables.

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.