In the last few decades, the Democratic Party has often pulled ahead of the Republican Party in terms of party identification. However, 2022 saw a shift in party identification, with slightly more Americans identifying with the Republican Party for the first time since 2011, when both parties stood at ** percent in 2011. These values include not only those surveyed who identified with a major political party, but also those who identified as independent, but have leanings towards one party over another.

Whites have become decreasingly likely to support the Democratic Party. I show this shift is being driven by two mechanisms. The first mechanism is the process of ideological sorting. The Democratic Party has lost support among conservative whites because the relationships between partisanship, voting behavior, and policy orientations have strengthened. The second mechanism relates to demographic changes. The growth of liberal minority populations has shifted the median position on economic issues to the left and away from the median white citizen’s position. The parties have responded to these changes by shifting their positions and whites have become less likely to support the Democratic Party as a result. I test these explanations using 40 years of ANES and DW-NOMINATE data. I find that whites have become 7.7-points more likely vote for the Republican Party and mean white partisanship has shifted .25 points in favor of the Republicans as a combined result of both mechanisms.

This dataset was designed to provide information on the personal and political backgrounds, political attitudes, and relevant behavior of party leaders. The data pertain to Democratic and Republican party elites holding office during the election year of 1984 and include County and State Chairs, members of the Democratic and Republican National Committees, and delegates to the 1984 National Conventions. These data focus on the "representativeness" of the party elites on a variety of dimensions and also permit a comparison of party leaders from the local, state, and national organizational levels. Special emphasis is placed on the presidential election, the presidential nominations system, public policy issues current in the 1984 campaign, and the future of the political parties. In addition, special note was taken of the views of women and minorities and the problem of providing them with representation in the parties. The question of whether their policy views and ideologies differed from other political party elites was also explored. Specific variables include characterization of respondent's political beliefs on the liberal-conservative scale, length of time the respondent had been active in the party, and the respondent's opinions on minorities in the party, party unity, national- and local-level party strength, and party loyalty. Respondents were also queried on attitudes toward important national problems, defense spending, and inflation. In addition, their opinions were elicited on controversial provisions instituted by their parties and on the directions their parties should take in the future. Demographic characteristics are supplied as well.

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...

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Democratic Republic of the Congo data available from WorldPop here.

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.

This dataset provides detailed information for the 2024 US Presidential Election, offering a valuable resource for political analysis and research. It includes a variety of data types, such as profiles of candidates, primary/caucus results, poll data, and debate transcripts. Key updates have been integrated throughout the election season, including the latest poll figures, transcripts from the Vice-Presidential debate between Walz and Vance, and the debate between Trump and Harris.

Significant events covered within the dataset include an annotated image and transcripts related to an assassination attempt on former President Trump. The political landscape evolved with the Democratic Party replacing President Biden with Kamala Harris in late August, setting up a contest between Trump and Harris, alongside nominees from smaller factions. The dataset also features approval ratings for sitting presidents, including Biden and Trump, and details on candidates like Robert F Kennedy Jr, who is running as an independent. This collection is regularly updated to reflect developments as the election cycle progresses, making it a current and dynamic source for understanding the 2024 US Presidential Election.

Columns

The dataset contains information on candidates with formal bids for the presidency, including the following columns:

• party: The political party affiliation of the candidate (e.g., republican, democrat, other).
• name: The full name of the candidate.
• date_announced: The date on which the candidate formally announced their intention to run for president. This column contains 21 unique values, ranging from 16 November 2022 to 1 July 2024.
• date_withdrawn: The date on which the candidate withdrew their bid for the presidency.

The data indicates that 62% of the candidate entries are Republican, 19% are Democrat, and 19% represent other parties.

Distribution

The data file is typically provided in a CSV format. A sample file will be made available separately on the platform. The dataset is listed as Version 1.0 and has a quality rating of 5 out of 5. While specific row or record counts are not currently available, the dataset is structured to facilitate analysis of various aspects of the 2024 US Presidential Election. It is available globally and offered as a free dataset. The data types included are tabular and text.

Usage

This dataset is an ideal resource for a multitude of applications and use cases, including:

• Political analysis and research into election trends.
• Government and civic record analysis.
• Natural Language Processing (NLP) tasks, utilising the debate transcripts for sentiment analysis, topic modelling, or text generation.
• Artificial Intelligence (AI) and Machine Learning (ML) model development for election outcome prediction, voter behaviour analysis, or candidate performance assessment.
• Studying the historical context and evolution of presidential campaigns.
• Developing visualisations and dashboards for public understanding of the election.

Coverage

The dataset focuses on the 2024 US Presidential Election and its related events, primarily covering the United States. The time range for data updates spans from March through to the final election night update, with candidate announcement dates beginning as early as November 2022 and extending into July 2024. This includes critical periods such as primary elections, nominating conventions, and general election campaigning. While primarily focused on the 2024 cycle, Version 3 of this dataset previously included coverage of the 2022 Congressional Mid-term Elections. The dataset provides insights into various demographic aspects through its focus on candidates from different political parties (Republican, Democrat, and other factions) and covers key figures like Joe Biden, Donald Trump, Kamala Harris, Robert F Kennedy Jr, Walz, and Vance.

License

CC By 4.0

Who Can Use It

This dataset is suitable for a wide range of users, including:

• Data analysts and scientists seeking to model election outcomes or understand political dynamics.
• Researchers and academics in political science, sociology, and media studies.
• Journalists and media organisations for reporting, investigative journalism, and creating data-driven stories about the election.
• Students undertaking projects or coursework related to American politics and elections.
• Developers and engineers creating applications or tools for political engagement or data visualisation.
• Government agencies or civic organisations interested in public opinion and electoral processes.

Dataset Name Suggestions

• 2024 US Presidential Election Data
• US Presidential Campaign 2024 Analytics
• American Election Data: 2024 Cycle
• US Presidential Candidate & Poll Data

Attributes

Original Data Source: [

Analysis of ‘🗳 Primary Candidates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/primary-candidatese on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This folder contains the data behind the stories:

• We Researched Hundreds Of Races. Here’s Who Democrats Are Nominating.
• How’s The Progressive Wing Doing In Democratic Primaries So Far?
• We Looked At Hundreds Of Endorsements. Here’s Who Republicans Are Listening To.

This project looks at patterns in open Democratic and Republican primary elections for the U.S. Senate, U.S. House and governor in 2018.

dem_candidates.csv contains information about the 811 candidates who have appeared on the ballot this year in Democratic primaries for Senate, House and governor, not counting races featuring a Democratic incumbent, as of August 7, 2018.

rep_candidates.csv contains information about the 774 candidates who have appeared on the ballot this year in Republican primaries for Senate, House and governor, not counting races featuring a Republican incumbent, through September 13, 2018.

Here is a description and source for each column in the accompanying datasets.

dem_candidates.csv and rep_candidates.csv include:

Column	Description
Candidate	All candidates who received votes in 2018’s Democratic primary elections for U.S. Senate, U.S. House and governor in which no incumbent ran. Supplied by Ballotpedia.
State	The state in which the candidate ran. Supplied by Ballotpedia.
District	The office and, if applicable, congressional district number for which the candidate ran. Supplied by Ballotpedia.
Office Type	The office for which the candidate ran. Supplied by Ballotpedia.
Race Type	Whether it was a “regular” or “special” election. Supplied by Ballotpedia.
Race Primary Election Date	The date on which the primary was held. Supplied by Ballotpedia.
Primary Status	Whether the candidate lost (“Lost”) the primary or won/advanced to a runoff (“Advanced”). Supplied by Ballotpedia.
Primary Runoff Status	“None” if there was no runoff; “On the Ballot” if the candidate advanced to a runoff but it hasn’t been held yet; “Advanced” if the candidate won the runoff; “Lost” if the candidate lost the runoff. Supplied by Ballotpedia.
General Status	“On the Ballot” if the candidate won the primary or runoff and has advanced to November; otherwise, “None.” Supplied by Ballotpedia.
Primary %	The percentage of the vote received by the candidate in his or her primary. In states that hold runoff elections, we looked only at the first round (the regular primary). In states that hold all-party primaries (e.g., California), a candidate’s primary percentage is the percentage of the total Democratic vote they received. Unopposed candidates and candidates nominated by convention (not primary) are given a primary percentage of 100 but were excluded from our analysis involving vote share. Numbers come from official results posted by the secretary of state or local elections authority; if those were unavailable, we used unofficial election results from the New York Times.
Won Primary	“Yes” if the candidate won his or her primary and has advanced to November; “No” if he or she lost.

dem_candidates.csv includes:

Column	Description
Gender	“Male” or “Female.” Supplied by Ballotpedia.
Partisan Lean	The FiveThirtyEight partisan lean of the district or state in which the election was held. Partisan leans are calculated by finding the average difference between how a state or district voted in the past two presidential elections and how the country voted overall, with 2016 results weighted 75 percent and 2012 results weighted 25 percent.
Race	“White” if we identified the candidate as non-Hispanic white; “Nonwhite” if we identified the candidate as Hispanic and/or any nonwhite race; blank if we could not identify the candidate’s race or ethnicity. To determine race and ethnicity, we checked each candidate’s website to see if he or she identified as a certain race. If not, we spent no more than two minutes searching online news reports for references to the candidate’s race.
Veteran?	If the candidate’s website says that he or she served in the armed forces, we put “Yes.” If the website is silent on the subject (or explicitly says he or she didn’t serve), we put “No.” If the field was left blank, no website was available.
LGBTQ?	If the candidate’s website says that he or she is LGBTQ (including indirect references like to a same-sex partner), we put “Yes.” If the website is silent on the subject (or explicitly says he or she is straight), we put “No.” If the field was left blank, no website was available.
Elected Official?	We used Ballotpedia, VoteSmart and news reports to research whether the candidate had ever held elected office before, at any level. We put “Yes” if the candidate has held elected office before and “No” if not.
Self-Funder?	We used Federal Election Committee fundraising data (for federal candidates) and state campaign-finance data (for gubernatorial candidates) to look up how much each candidate had invested in his or her own campaign, through either donations or loans. We put “Yes” if the candidate donated or loaned a cumulative $400,000 or more to his or her own campaign before the primary and “No” for all other candidates.
STEM?	If the candidate identifies on his or her website that he or she has a background in the fields of science, technology, engineering or mathematics, we put “Yes.” If not, we put “No.” If the field was left blank, no website was available.
Obama Alum?	We put “Yes” if the candidate mentions working for the Obama administration or campaign on his or her website, or if the candidate shows up on this list of Obama administration members and campaign hands running for office. If not, we put “No.”
Dem Party Support?	“Yes” if the candidate was placed on the DCCC’s Red to Blue list before the primary, was endorsed by the DSCC before the primary, or if the DSCC/DCCC aired pre-primary ads in support of the candidate. (Note: according to the DGA’s press secretary, the DGA does not get involved in primaries.) “No” if the candidate is running against someone for whom one of the above things is true, or if one of those groups specifically anti-endorsed or spent money to attack the candidate. If those groups simply did not weigh in on the race, we left the cell blank.
Emily Endorsed?	“Yes” if the candidate was endorsed by Emily’s List before the primary. “No” if the candidate is running against an Emily-endorsed candidate or if Emily’s List specifically anti-endorsed or spent money to attack the candidate. If Emily’s List simply did not weigh in on the race, we left the cell blank.
Gun Sense Candidate?	“Yes” if the candidate received the Gun Sense Candidate Distinction from Moms Demand Action/Everytown for Gun Safety before the primary, according to media reports or the candidate’s website. “No” if the candidate is running against an candidate with the distinction. If Moms Demand Action simply did not weigh in on the race, we left the cell blank.
Biden Endorsed?	“Yes” if the candidate was endorsed by Joe Biden before the primary. “No” if the candidate is running against a Biden-endorsed candidate or if Biden specifically anti-endorsed the candidate. If Biden simply did not weigh in on the race, we left the cell blank.
Warren Endorsed?	“Yes” if the candidate was endorsed by Elizabeth Warren before the primary. “No” if the candidate is running against a Warren-endorsed candidate or if Warren specifically anti-endorsed the candidate. If Warren simply did not weigh in on the race, we left the cell blank.
Sanders Endorsed?	“Yes” if the candidate was endorsed by Bernie Sanders before the primary. “No” if the candidate is running against a Sanders-endorsed candidate or if Sanders specifically anti-endorsed the candidate. If Sanders simply did not weigh in on the race, we left the cell

Congo, The Democratic Republic of the CD: Birth Rate: Crude: per 1000 People data was reported at 42.280 Ratio in 2016. This records a decrease from the previous number of 42.809 Ratio for 2015. Congo, The Democratic Republic of the CD: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 46.094 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 46.903 Ratio in 1965 and a record low of 42.280 Ratio in 2016. Congo, The Democratic Republic of the CD: Birth Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Democratic Republic of the Congo: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

This study seeks to explain state adoptions of same-day registration, with a focus on determining whether the Democratic (Republican) Party’s support of (resistance to) this impactful voting reform is driven by strategic electoral considerations. I find that states have an increased probability of enacting the reform when legislative Democrats are in the precarious position that comes with having just experienced minority status in one or both chambers. Relatedly, I demonstrate that the presence of a Republican legislature does not make adoption less likely until the size of the Black population reaches a certain threshold. In fact, provided the Black population is small enough, Republican control of the legislature encourages reform. The results offer conflicting evidence, however, that large Latino populations deter the GOP from establishing same-day registration. Considered together, the results cast doubt on the claim that either party’s position is informed by principle alone.

More than 93 percent of parents place high importance on sex education in both middle and high school. Sex education in middle and high school is widely supported by parents regardless of their political affiliation. Using data from a large diverse sample of 1,633 parents of children aged 9 to 21 years, we examined whether views on sex education differed by parents’ political affiliation. More than 89 percent of parents that identified as Republicans or Democrats support including a wide range of topics in sex education including puberty, healthy relationships, abstinence, sexually transmitted diseases (STDs) and birth control in high school. In middle school, 78 percent or more of both parents that identified as Republicans and Democrats support the inclusion of those topics. Controlling for key demographic factors, parents that identified as Democrats are more likely than those that identified as Republicans to support the inclusion of the topics of healthy relationships, birth control, STDs, and sexual orientation in both middle and high school. However, a strong majority of Republican parents want all these topics included in sex education. Sex education which includes a broad set of topics represents an area of strong agreement between parents of both political parties.

Historical dataset showing Democratic Republic of Congo crime rate per 100K population by year from N/A to N/A.

This dataset was designed to provide information on the personal and political backgrounds, political attitudes, and relevant behavior of party leaders. The data pertain to Democratic and Republican party elites holding office during the election year of 1980 and include County and State Chairs, members of the Democratic and Republican National Committees, and delegates to the National Conventions. These data focus on the "representativeness" of the party elites on a variety of dimensions and also permit a comparison of party leaders from the local, state, and national organizational levels. Other issues explored include the party reform era, the effects of the growing body of party law, and the nationalization of the political parties. Specific variables include characterization of respondent's political beliefs on the liberal-conservative scale, length of time the respondent had been active in the party, and the respondent's opinions on minorities in the party, party unity, national- and local-level party strength, and party loyalty. Respondents were also queried on attitudes toward important national problems, defense spending, and inflation. In addition, their opinions were elicited on controversial provisions in their parties' charters and on the directions their parties should take in the future. Demographic characteristics are supplied as well.

This dataset contains voter registration data in Iowa by month and state house district starting with June 2021. It identifies the number of voters registered as Democrats, Republicans, other party or no party. The dataset also identifies the number of active and inactive voter registrations. Inactive voters are those to whom official mailings have been sent from the county auditor’s office, the notice was returned as undeliverable by the United States Postal Service and the voter has not responded to a follow up confirmation notice. [§48A.37]

Congo, The Democratic Republic of the CD: Population: Total data was reported at 81,339,988.000 Person in 2017. This records an increase from the previous number of 78,736,153.000 Person for 2016. Congo, The Democratic Republic of the CD: Population: Total data is updated yearly, averaging 32,954,798.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 81,339,988.000 Person in 2017 and a record low of 15,248,251.000 Person in 1960. Congo, The Democratic Republic of the CD: Population: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Population and Urbanization Statistics. Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Sum; Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.

Contains data from UNESCO's data portal covering various indicators.

DPR Korea administrative level 0 (country), 1 (province, special city), and 2 (county, city, special city) 2008 population statistics

REFERENCE YEAR: 2008

These population statistics tables are suitable for database or GIS linkage to the DPR Korea administrative level 0-2 boundaries. Note, however the caveat below that two administrative level 2 features in the COD-AB do not have corresponding records in the COD-PS table.

Congo, The Democratic Republic of the CD: Population Living in Slums: % of Urban Population data was reported at 74.800 % in 2014. This records an increase from the previous number of 61.700 % for 2009. Congo, The Democratic Republic of the CD: Population Living in Slums: % of Urban Population data is updated yearly, averaging 71.950 % from Dec 2005 (Median) to 2014, with 4 observations. The data reached an all-time high of 76.400 % in 2005 and a record low of 61.700 % in 2009. Congo, The Democratic Republic of the CD: Population Living in Slums: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Democratic Republic of Congo – Table CD.World Bank: Population and Urbanization Statistics. Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.; ; UN HABITAT, retrieved from the United Nation's Millennium Development Goals database. Data are available at : http://mdgs.un.org/; Weighted Average;