This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-_location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau... Visit https://dataone.org/datasets/sha256%3A05707c1dc04a814129f751937a6ea56b08413546b18b351a85bc96da16a7f8b5 for complete metadata about this dataset.

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

This dataset contains counts of voter registration and voter turnout for all counties in the United States for the years 2004-2022. It also contains measures of each county's Democratic and Republican partisanship, including six-year longitudinal partisan indices for 2006-2022.

In U.S. presidential elections since 1964, voter turnout among male and female voters has changed gradually but significantly, with women consistently voting at a higher rate than men since the 1980 election. 67 percent of eligible female voters took part in the 1964 election, compared to 72 percent of male voters. This difference has been reversed in recent elections, where the share of women who voted has been larger than the share of men by around four percent since 2004.

In the fall of 2006 the American National Election Studies (ANES) carried out a pilot study after the 2006 mid-term elections in the United States. The 2006 ANES Pilot Study was conducted for the purpose of testing new questions and conducting methodological research to inform the design of future ANES studies. As such, it is not considered part of the ANES time series that has been conducted since 1948, and the pilot study only includes time series questions necessary to evaluate the new content. The election studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. This full release dataset contains all 675 interviews, with the survey portion of the interview lasting just over 37 minutes on average. The study had a re-interview rate of 56.25 percent. Respondents were asked questions over a variety of topics. They were queried on need for closure in various situations including unpredictable ones, how fast important decisions were made, and how often they could see that both people can be right when in disagreement. Respondents were asked many questions pertaining to their values. Some questions dealt with optimism and pessimism. Respondents were asked if they felt that were generally optimistic, pessimistic, or neither in regard to the future. They were asked specifically how they felt about the future of the United States. Respondents were also asked about their social networks, about who they talked to in the last six months, and how close they felt to them. Respondents were further queried about how many days in the last six months they talked to these people, their political views, interest in politics, and the amount of time it would take to drive to their homes. Other questions sought respondents' political attitudes including attentiveness to following politics, ambivalence, efficacy, and trust in government. Respondents were asked questions related to the media such as how much time and how many days during a typical week they watched or read news on the Internet, newspaper, radio, or television. Questions that dealt with abortion consisted of giving respondents various scenarios and asking if they favored or opposed it being legal for the women to have an abortion in that circumstance. The issue of justice was also included by asking respondents what percent of people of different backgrounds who are suspected of committing a crime in America are treated fairly. Respondents were also asked to give their opinion on gender in politics, specifically, whether gender played a role in how the respondent would vote for various political offices. Respondents were also queried on whether they would vote for Bill Clinton or George W. Bush and whether they had voted in the elections in November. Respondents were also asked if they approved of the way George W. Bush was handling his job as president, the way he was handling relations with foreign countries, and the way he was dealing with terrorism. Respondents were also asked how upsetting the thought of their own death was, and how likely it was that a majority of all people on Earth would die at once during the next 100 years because of a single event. Demographic variables include age, party affiliation, sex, religious preference, and political party affiliation.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12064410%2F466e70c347974ab1f64280395bb45974%2Fpolitical%20polarization%20flag.png?generation=1677875491440013&alt=media" alt="">

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 was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Replication data and code for "Your Typical Criminal: Why White Americans Hate Voter Fraud" (Public Opinion Quarterly), by Adriano Udani, David Kimball, and Anita Manion. Includes three data files (CES survey data modules) and code for the analyses.

Partisan polarization is a significant driver of stress and anxiety for the American public, and recent aggregate-level studies suggest polarization may be shaping Americans’ health. This individual-level study uses a new representative dataset of 2752 US residents surveyed between December 2019 and January 2020, testing why some Americans report more days of poor physical and mental health per month than others. Using negative binomial models, zero inflated models, and descriptive visualizations, we find evidence that polarization is detrimental to an individual's physical and mental health; the more distant they feel politically from the average voter in their state, the worse health outcomes they report. By uncovering the individual-level political drivers of health, this study aims to encourage further study and attention to the broader consequences of political polarization on American communities.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Quantile and mean comparisons of Republican and Democratic counties across select public-health measures.

Experiment 1: Mean change in voting preference for the favored candidate on an 11-point scale for the multiple exposure group.

Bivariable and multivariable multinomial logistic regression for factors associated with voting to abstain or voting “not in favor” during the final device approval recommendation vote.