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.

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 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.

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 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, 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.

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.

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.

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.

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 Protestant Christian voters reported voting for Donald Trump. In comparison, only ** percent of Jewish voters reported voting for Trump.

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

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

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.

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.

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.

On July 21, Biden announced he was ending his bid for reelection, later endorsing Kamala Harris, who is the official Democratic nominee as of the Democratic National Convention in August. Although approval of Harris was once generally low, favorability of the vice president has spiked since announcing her presidential bid. Although the race is certainly closer since Harris began her campaign, polling has fluctuated, with support for Trump increasing just days before the election. National polling indicated that the two presidential hopefuls were 0.1 percentage points apart on November 4, 2024, making it nearly impossible to predict the results. While presidential polls are generally reliable in measuring national trends, they are not infallible, particularly in close races or predictions of Electoral College outcomes.

Election Graphics Platforms Market Outlook

According to our latest research, the global Election Graphics Platforms market size reached USD 1.42 billion in 2024, reflecting the growing importance of real-time, visually engaging data presentation in modern electoral processes. The market is projected to expand at a robust CAGR of 11.8% from 2025 to 2033, reaching an estimated USD 3.65 billion by 2033. This growth is primarily driven by the increasing demand for dynamic and interactive visualizations in election coverage, coupled with the rapid adoption of advanced technologies by media organizations and political entities worldwide.

One of the primary growth factors propelling the Election Graphics Platforms market is the rising emphasis on data-driven storytelling during elections. As the complexity of electoral data increases and the need for instant, accurate information dissemination becomes paramount, broadcasters and digital platforms are investing heavily in sophisticated graphics solutions. These platforms enable real-time visualization of polling results, demographic trends, and predictive analytics, enhancing audience engagement and trust. The integration of artificial intelligence and machine learning into election graphics platforms further amplifies their capability to process and display large volumes of data with high accuracy and speed, making them indispensable tools for modern election coverage.

Another significant driver for market expansion is the proliferation of digital media channels and the shift in audience preferences toward interactive content. With the advent of social media and online news platforms, consumers now expect election updates to be delivered not just accurately but also in visually compelling formats. Election graphics platforms are evolving to meet these expectations, offering seamless integration with digital media workflows and enabling the creation of shareable, interactive content. This trend is particularly pronounced among younger demographics, who are more likely to consume election-related information on mobile devices and social platforms, further accelerating the adoption of these technologies across the globe.

Additionally, the global political landscape is witnessing an increase in the frequency and scale of elections, referendums, and political campaigns. This surge in electoral activities, combined with heightened public scrutiny and demand for transparency, has prompted political parties, government agencies, and educational institutions to invest in advanced graphics platforms for both internal analysis and public communication. The ability of these platforms to customize visuals for different audiences, languages, and regional contexts significantly broadens their appeal and applicability, contributing to sustained market growth over the forecast period.

From a regional perspective, North America continues to dominate the Election Graphics Platforms market, accounting for the largest share in 2024 due to its mature media ecosystem and high adoption rate of advanced broadcast technologies. However, Asia Pacific is emerging as the fastest-growing region, fueled by the increasing digitization of media, expanding democratic processes, and rising investments in election technology infrastructure. Europe also remains a key market, characterized by strong regulatory frameworks and a focus on innovation in political communication tools. Latin America and the Middle East & Africa are gradually catching up, driven by political modernization initiatives and the growing influence of digital media in electoral processes.

Component Analysis

The Election Graphics Platforms market is segmented by component into software and services, each playing a vital role in the overall value proposition. The software segment forms the backbone of the market, encompassing a wide array of solutions designed to generate, manage, and display election-related graphics. These software offerings range from standalone applications tailored for specific broadcast environments to comprehensive suites that integrate seamlessly with newsroom management systems, live data feeds, and social media platforms. The increasing demand for real-time data visualization and the ability to customize graphics for different electoral events are driving continuous innovation in this segment. Vendors are focusing on enhancing user interfaces, expanding template libraries, and incorporating advanced analytics to deliver more insightful and visually appealing graphics

This map service contains both polygon and linear features for the boundaries of the Massachusetts State Senate districts, which were signed into law on November 4, 2021, with Chapter 82 of the Acts of 2021. These boundaries began to be used with the fall 2022 elections and are based on demographic data from the 2020 U.S. Census.Member names with the results of the November 2022 election were entered in January 2023 and updated in February 2024.Member names from the results of the November 2024 election were populated in January 2025.See full metadataFeature service also available.