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52,540 DAYS (March 4th, 1879 to January 7th, 2023)

This is a dataset that tracks political polarization in US Congress (46th to 117th) through proportions from 1879 to 2023.

All data are official figures from Voteview that have been compiled and structured by myself. Ideological positions are calculated using the DW-NOMINATE (Dynamic Weighted NOMINAl Three-step Estimation). This procedure was developed by Keith T. Poole and Howard Rosenthal in the 1980s and is a "scaling procedure", representing legislators on a spatial map. In this sense, a spatial map is much like a road map--the closeness of two legislators on the map shows how similar their voting records are. Using this measure of distance, DW-NOMINATE is able to recover the "dimensions" that inform congressional voting behavior.

Why did I create this dataset? In my personal opinion, political polarization is the greatest threat to democracy today, particularly in America. Polarization not only creates an "US VS THEM" situation, but also renders legislative bodies less effective at passing meaningful legislation. By uploading time-series data regarding American polarization over the past two centuries, I hope that the community will use my dataset to determine insightful statistical trends. Achieving a quantitative yet objective viewpoint of political polarization is crucial to understanding both its underlying causes and its everlasting effects.

IMPORTANT:

The first dimension picks up differences in ideolology, which is represented through the "liberal" vs. "conservative" (also referred to as "left" vs. "right") proportions throughout American history. The second dimension picks up differences within the major political parties over slavery, currency, nativism, civil rights, and lifestyle issues during periods of American history.

Data Sources

The primary data source used was Voteview's official website, which publishes ideological data pertaining to America. Considering the meticulous documentation of congressional activities by such an accredited organization, no other organization is more equipped to provide insight on US polarization.

1. Voteview's Realtime NOMINATE Ideology and Related Data - Voteview's download links for NOMINATE scores, tutorials for generating ancillary data, and other publicly available databases are stored here.
2. Voteview's "Polarization in Congress" Data Analysis - Jeff Lewis, the project lead of Voteview, published a data analysis of the available polarization data. The detailed work gave insight into the true analytical potential of the dataset, and inspired many elements of my accompanying notebook.
3. Voteview's "The Polarization of the Congressional Parties" - Voteview released a detailed overview of their polarization data, the methodology behind their data, and the proper definitions and terminologies for the variables tracked. The guide mainly provided essential contextual knowledge needed to create a meaningful dataset.

Statistics Being Tracked

• Chamber (House or Senate)
• Congress (Number)
• Year (First Year of Congress)
• Party.mean.diff.d1 (Difference in Party Means - First Dimension)
• Prop.moderate.d1 (Proportion Moderates)
• Prop.moderate.dem.d1 (Proportion of Moderate Democrats (-0.25 to +0.25))
• Prop.moderate.rep.d1 (Proportion of Moderate Republicans (-0.25 to +0.25))
• Overlap
• Chamber.mean.d1 (Chamber Mean - First Dimension)
• Chamber.mean.d2 (Chamber Mean - Second Dimension)
• Dem.mean.d1 (Democratic Party Mean - First Dimension)
• Dem.mean.d2 (Democratic Party Mean - Second Dimension)
• Rep.mean.d1 (Republican Party Mean - First Dimension)
• Rep.mean.d2 (Republican Party Mean - Second Dimension)
• North.rep.mean.d1 (Northern Republican Mean - First Dimension)
• North.rep.mean.d2 (Northern Republican Mean - Second Dimension)
• South.rep.mean.d1 (Southern Republican Mean - First Dimension)
• South.rep.mean.d2 (Southern Republican Mean - Second Dimension)
• North.dem.mean.d1 (Northern Democrat Mean - First Dimension)
• North.dem.mean.d2 (Northern Democrat Mean - Second Dimension)
• South.dem.mean.d1 (Southern Democrat Mean - First Dimension)
• South.dem.mean.d2 (Southern Democrat Mean - Second Dimension)

Dataset History

2023-03-03 - Dataset is created (52,595 days after temporal coverage start date).

GitHub Repository - The same data but on GitHub.

Polarization Plots Notebook

[Link to Notebook](h...

This dataset depicts the boundaries of the Bureau of Land Management's (BLM's) National Monuments (NMs) and National Conservation Areas (NCAs) in Arizona as part of the National Landscape Conservation System (NLCS) data standards. The NLCS NM NCA dataset also depicts similar land designations such as Cooperative Management and Protection Areas, Forest Reserves, and Outstanding Natural areas.

The NLCS NM NCA dataset is in progress as of April 30, 2019. Only the Gila Box Riparian NCA has completed the process of legal description, map creation, certification, and submittal to Congress. The remaining NMs and NCAs are awaiting the draft of legal descriptions. As legal descriptions are finalized and certified, the NLCS dataset may require updating to ensure that the spatial footprints of the NM and NCA boundary data match their respective legal descriptions. Once the boundaries are confirmed, no further changes to the NM/NCA boundary data should be made. Boundary changes can only be made through an amendment to the legal description and direct notification to Congress.

The Antiquities Act of 1906 grants the President authority to designate national monuments in order to protect “objects of historic or scientific interest.” NCAs and similarly designated lands are designated by Congress to conserve, protect, enhance, and manage public lands for the benefit and enjoyment of present and future generations. In June 2000, the Bureau of Land Management (BLM) responded to growing concern over the loss of open space by creating the NLCS. The NLCS brings into a single system some of the BLM's premier designations. By putting these lands into an organized system, the BLM hopes to increase public awareness of the scientific, cultural, educational, ecological, and other values of these NM/NCA boundaries.

The Public Law (P.L.) 101-628 established Gila Box Riparian NCA; P.L. 106-538 established the Las Cienegas NCA (also Sonoita Valley Acquisition Planning District), and P.L. 100-696 established San Pedro Riparian NCA. Each of these laws required the BLM to file boundary legal descriptions and maps to Congress for each NCA. As of April 30, 2019, only the Gila Box Riparian National Conservation Area has completed legal description, map depiction, certification, and submission to Congress. All other NM/NCA have gone through review by Arizona field offices. Draft maps have been completed and are awaiting legal descriptions to be drafted with the exception of the Grand Canyon-Parashant NM. All proposed boundaries are included in this dataset. When legal descriptions are finalized and certified, minor updates may be necessary to ensure that the geospatial depiction of the NM/NCA boundary data matches the legal descriptions, after which no further changes to the geospatial NM/NCA boundary data should be made.

The standards, format and language for the legal descriptions and boundary maps were developed during regular meetings of the NLCS Coordinator, geospatial specialists and the Cadastral Surveyors regarding Arizona NLCS data. Guidance was provided from Congressional required maps and legal boundary descriptions for the NLCS Designation Manual 6120 (March, 2010). Established through Presidential Proclamation, there is no requirement for BLM to file boundary legal descriptions or maps with Congress for the National Monument. However, the NLCS Coordinator and Cadastral Survey Chief decided that it was prudent to extend the boundary process to NMs.

The CongDist_Gen feature class is a generalized spatial representation of the United States Congressional Districts of the 118th Congress. The data was generalized using the Esri Generalize polygon function. The CongDist feature class is a US Forest Service Standard Reference Dataset and is the preferred spatial representation of US Congressional Districts. This feature class has been modified and should only be used for cartographic purposes at small scales. The purpose of the generalized CongDist_Gen dataset is to improve map display and drawing performance in both ArcGIS applications and ArcGIS Server map services. To satisfy both performance improvement and geometry representation, the following parameters and geoprocessing method were used. Simplify the input features using the Douglas-Peucker simplification algorithm with a specified maximum offset tolerance of 375 meters. The output features will contain a subset of the original input vertices. Min scale refers to what scale the layer turns off when zooming out. Max scale refers to the scale the layer turns off at when zooming in. Min scale: 0, no minimum scale set, Max Scale: 1:4622325.