High-resolution election data.

ElectionPAD is a cloud-hosted analytics platform (CTA PAD) that provides census block-level election results for every U.S. state legislative, statewide, and federal general election since 2012 - ideal for rigorous longitudinal academic research and political strategy. 

The most detailed U.S. election data – every primary and general statewide and federal race since 2012, imputed down to the census block. Over 3000 races, 1 billion rows, and hundreds of columns. 

Enriched with demographics and vote history – current to each year

Year-over-year comparability – stable census block boundaries mean true apples-to-apples analysis

Aggregate anywhere – from blocks to districts to custom regions

Works with your tools – R, Python, SQL, and more in CTA PAD GCP

Expert support included - from both CTA on the platform side, and Targetsmart on the data side.

Bring all your datasets together.

PAD allows institutions, like Tufts and Harvard Kennedy School, to bring together datasets from many sources via a unique ingestion process, and then allows analysts direct access.

Our partners are then able to:

Perform detailed research - on subjects like voter registration trends,

Combine data with voterfiles - Leveraging polling, voter registration, or demographic data, partners can create datasets that include data on registered voters directly from voter rolls (voterfiles).

Visualize Insights - PAD’s access to Looker Studio, Tableau, and other visualization platforms, allows partners to quickly and easily create beautiful and insightful new ways to display and interpret their data and analyses.

PAD provides access to a variety of FREE datasets, in addition to our partner’s proprietary and licensed data.

PAD allows researchers to:

Analyze massive datasets with ease – PAD lets researchers work with terabytes or petabytes of data, without the hassle of managing infrastructure.

Collaborate seamlessly – Share and query data across universities and research consortia in real time, improving reproducibility and accelerating discovery.

Integrate with familiar tools – Connect directly to Python, R, Jupyter notebooks, and visualization platforms, or build models with BigQuery ML.

Lower costs, expand access – Cloud-based, pay-as-you-go pricing makes advanced research affordable and scalable for institutions of all sizes.