apportionments_pop_2021_pred_2024.xlsx This is a dataset containing prediction apportionments of seats for the 2024 election of the European Parliament (EP). This prediction is based on population data from the 2021 census held by Eurostat. See our paper for the standard function, configurations of parameters, and d-rounding rules we used for calculation. Note: We recommend readers who are not so well informed about apportionment problems and rounding rules see https://www.census.gov/library/video/2021/what-is-apportionment.html or https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html.

Data interpretations for this dataset are as follows. 4 worksheets: all: prediction apportionment results of all configurations under the assumption that the membership remains unchanged and the total number of seats is between 705 (current total number of seats) and 750 (statutory threshold). no_lose: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State does not lose any seats from the current distribution of seats; (3) and the total number of seats is between 705 and 750. increase_no_lose: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State with an increasing population does not lose any seats from the current distribution of seats; (3) and the total number of seats is between 705 and 750. response: prediction apportionment results under the following assumptions: (1) the membership remains unchanged; (2) any Member State with an increasing population does not lose any seats from the current distribution of seats while any Member State with a decreasing population does not gain seats; (3) and the total number of seats is between 705 and 750. Meanings of column names: State: name of Member State of the European Union p_2011: population data from the 2011 census (data source: https://ec.europa.eu/eurostat/web/population-demography/population-housing-censuses/database) p_2021: population data from the 2021 census (data source: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Population_and_housing_census_2021_-_population_grids&stable=1#Distribution_of_European_population) stat_2020: current distribution of seats in the EP (data source: https://www.europarl.europa.eu/news/en/headlines/eu-affairs/20180126STO94114/infographic-how-many-seats-does-each-country-get-in-in-the-european-parliament) other columns: composed in the order of "a", "gamma", "d-rounding rule", and "the total number of seats (S)".

indexes_pop_2021_pred_2024.csv This is a dataset presenting the extent of the PSI-based inequality index (index based on Population Seat Index) and the conventional PSP-based index (index based on the proportion of seats to population) of all prediction apportionments of seats for the 2024 election of the European Parliament (EP). This prediction is based on population data from the 2021 census held by Eurostat. See our paper for the standard function, configurations of parameters, and d-rounding rules used for calculation and the PSI-based index and PSP-based index used for evaluation. Data interpretations for this dataset are as follows. Meanings of column names: a: configuration of the standard function gamma: configuration of the standard function rounding: d-rounding rule used for obtaining a whole number S: the total number of seats in the prediction x_min: the minimum number of seats in the prediction apportionment x_max: the maximum number of seats in the prediction apportionment inequality index: maximum of PSI divided by minimum of PSI psp_max/psp_min: maximum of PSP divided by minimum of PSP

Population and housing census dataset as of Census 2022 by Bangladesh Bureau of Statistics (BBS). The population and Housing census has two parts that include- (1) The Population Census, which provides socio-economic and demographic information on every person living in a country at a point in time, down to the smallest geographical unit. (2) The Housing Census, which provides data on all dwelling units prevailing in a country, their conditions, and facilities available, down to the smallest geographical unit. Although it covered smallest geographical unit of Bangladesh; till date it's available up to District (Admin 02) level.
All information contains this dataset is collated from Final Population and Housing census report published by BBS to ensure meaningful access. For more detail information and further query/questions it’s recommended to check the full report.

Please click in Below link to access the full report-
https://bbs.portal.gov.bd/sites/default/files/files/bbs.portal.gov.bd/page/b343a8b4_956b_45ca_872f_4cf9b2f1a6e0/2024-01-31-15-51-b53c55dd692233ae401ba013060b9cbb.pdf

About this dataset

Context

The dataset tabulates the House population by age. The dataset can be utilized to understand the age distribution and demographics of House.

Content

The dataset constitues the following three datasets

• House, NM Age Group Population Dataset: A complete breakdown of House age demographics from 0 to 85 years, distributed across 18 age groups
• House, NM Age Cohorts Dataset: Children, Working Adults, and Seniors in House - Population and Percentage Analysis
• House, NM Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

How many households are in the U.S.?

In 2023, there were 131.43 million households in the United States. This is a significant increase from 1960, when there were 52.8 million households in the U.S.

What counts as a household?

According to the U.S. Census Bureau, a household is considered to be all persons living within one housing unit. This includes apartments, houses, or single rooms, and consists of both related and unrelated people living together. For example, two roommates who share a living space but are not related would be considered a household in the eyes of the Census. It should be noted that group living quarters, such as college dorms, are not counted as households in the Census.

Household changes

While the population of the United States has been increasing, the average size of households in the U.S. has decreased since 1960. In 1960, there was an average of 3.33 people per household, but in 2023, this figure had decreased to 2.51 people per household. Additionally, two person households make up the majority of American households, followed closely by single-person households.

Vintage of boundaries and attributes: 2020, 2021Geography: BlockCoverage: Cuyahoga CountyDemographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)LEHD Data: Area Profile for Private Primary JobsData downloaded from: U.S. Census Bureau’s data.census.gov and OnTheMap sitesDate the Data was Downloaded: April 1, 2024This layer contains the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics information about total population counts by sex, age, and race groups, as well as 2021 Longitudinal Employer-Household Dynamics (LEHD) at the Block level for Cuyahoga County, Ohio.To see the full list of attributes available in this service, go to the "Data" tab above, and then choose "Fields" at the top right. Each attribute contains definitions, additional details, and the formula for calculated fields in the field description.This web layer can be used in a variety of planning and decision-making applications, including transportation planning, land use analysis, economic development, and social policy analysis. It provides valuable insights into the demographic, economic, and housing characteristics of Cuyahoga County, Ohio, which can inform planning and policy decisions at the local, regional, and state levels.Data Processing Notes: Census blocks with no population that occur in areas of water, such as oceans, are removed from this data service.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

SummaryThe data for this dashboard is from the California Department of Finance Demographic Research Unit's published E-5 Annual Report showing the changes in population and housing across California from the state, county, and city level from April 1, 2020 to January 1, 2024. These estimates observe 58 counties, 482 cities, and 57 unincorporated county areas. The purpose of this dashboard is to provide interactive analysis with data visualizations to complement the E-5 report released annually on May 1st.Please note, the changes from 2020 to 2021 reflect a nine-month change, not an annual change, as these estimates begin from the decennial census on April 1, 2020. Subsequent years' estimates reflect annual changes starting on January 1st.Dashboard User Tips To view the data, search and select a County/City using the side panel. Press "ctrl" + "+/-" to set the appropriate viewing extent for your device. To view more years within the featured data table, click the arrows at the top of the table.For more information on this report and others, visit the Forecasting webpages: Demographic Research Unit DRU DHUB Sources: Data used in estimation models come from administrative records of several state and federal government departments and agencies, and from the local jurisdictions for which Finance produces population estimates. Because timeliness and coverage in these series vary, corrections, smoothing, and other adjustments may be applied. Changes to 2020 P.L. 94-171 data in the classification of student housing on or near campus was necessary to remain consistent with the census group quarters definition. In only a few instances, some student housing (residence hall and apartment units) counted as household population in the census was redefined as group quarters student housing population. College dorm group quarters population is defined as student population living in residence halls and apartment units located on or near college campuses.Suggested Citation State of California, Department of Finance, California Population and Housing Estimates Dashboard (2020-2024) — January 1, 2021-2024. Sacramento, California, May 2024.

Abstract

The Government of Liberia and its Development Partners recognized agriculture as a pivotal sector in fostering economic growth, reducing poverty, and achieving food security. Since post-war, the Government in collaboration with development partners, has made substantial investments to develop and expand the agriculture sector. Over the years, policymakers and data users in the agriculture sector have experienced significant challenges in obtaining the requisite data needed to monitor and evaluate these interventions and make informed decisions on new interventions. To address these challenges, the Liberia Institute of Statistics and Geo-Information Services (LISGIS) and the Ministry of Agriculture (MoA) conducted several ad hoc agricultural surveys. While valuable, these surveys have often been limited in scope and unable to provide the comprehensive data needed for effective policymaking and planning. To support the sector more robustly, the government decided to undertake a comprehensive agricultural census. The Liberia Agriculture Census 2024, the second agricultural census in Liberia since 1971 and the first to be conducted digitally, aimed to collect structural and reliable data on various aspects of the agricultural sector.

The main objectives of the LAC-2024 was to: · Reduce the existing data gap in Liberia's agriculture sector. · Provide comprehensive data on the agriculture sector for policy formulation and evaluation of existing programs. · Enable LISGIS to establish an agriculture master sampling frame for the conduct of future agricultural surveys and research. · Identify the structural changes in the agriculture sector over time. · Provide information on crop, livestock, poultry, and aquaculture activities. · Determine the size, composition, practices and related characteristics of Liberia's agricultural holdings. · Generate disaggregated agriculture statistics. · Provide statistics for advocacy in Liberia's agriculture sector. · Identify agricultural practices and constraints at the community level.

To achieve these objectives, the LAC-2024 was designed to collect structural data at the household, non-household and community levels. The data collected at these three levels provide a wealth of information for understanding the state of agriculture in Liberia. This documentation provides a catalogue of information necessary for understanding how data was collected at the household level. The documentation also provides useful information for understanding the household anonymized dataset.

Geographic coverage

National coverage

Analysis unit

Agricultural households

Universe

The universe for the Liberia Agriculture Census 2024 household level data collection encompasses: All households in Liberia having atleast one member engaged in agriculture activity during the 2022/2023 farming season.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The Liberia Agriculture Census 2024 (LAC-2024) was a sampled census conducted in all 15 counties of Liberia. The sampling frame used for the LAC-2024 is based on the 2022 National Population and Housing Census (2022-NPHC), conducted by the LISGIS. The sample design for the census was a stratified cluster sampling with enumeration areas (EAs) as clusters and farming households as units of interest. In adequacy with budget availability, a large sample of 4,800 EAs was considered for the LAC-2024. These EAs had a total of 269,652 agricultural households in the frame. The sample was allocated in strata (districts, urban/rural) proportionally to the numbers of farming households computed in the frame. In total, about 78.8% of the sample was allocated to rural areas. The stratified sample of EAs was selected with a probability proportional to the number of farming households at EA level. A complete listing of all households (both agricultural and non-agricultural) was carried out in the selected EAs and detailed questions were addressed to all households that practiced agricultural activities during the 2022/2023 farming season. The results of the LAC-2024 are representative at the district level.

For more information on the LAC-2024 sampling methodology, see the methodology section of the Liberia Agriculture Census 2024 Household Report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The LAC-2024 employed three questionnaires: the Household Questionnaire, the Community Questionnaire and the Non-Household Questionnaire. These three questionnaires were based on the 50x2030 Initiative standard model questionnaires. The Liberia Agriculture Census Technical Working Group (LAC-TWG), comprising technical staff from LISGIS,Ministry of Agriculture (MOA), National Fishery and Aquaculture Authority (NaFAA), Cooperative Development Agency (CDA) and the Ministry of Internal Affairs (MIA) worked with technicians from the 50x2030 Initiative to adapt the questionnaires to Liberia's context and realities. Suggestions and inputs were solicited from various stakeholders representing government ministries, commissions and agencies (MACs), nongovernmental and international organizations as well as accademic institutions involved with agriculture issues. All questionnaires were finalized in English. Some questions in the questionnaires were translated into simple Liberian English, for the purpose of easy administration. The household questionnaire include type of agricultural activities practice, household members characteristics, housing conditions, hired labor practice, agricultural parcels and plots characteristics, types of crops and methods of crop cultivation, inputs, tools and equipment use, type and number of livestock and poultry. The household questionnaire was administered to the household head or an adult member of the household who had vast knowledge of the household and its agricultural activities. The primary respondent (i.e., the household member that provided most of the information for the questionnaire or a given module, household member, or crop) sometimes varies across modules.

Cleaning operations

The data was edited using CSpro programs, version 7.7.3. The appropriate edit rules were established by programmers and subject matter specialists at LISGIS and MOA. In few cases, manual editing techniques were applied to recode responses generated from other specify options. The SPSS software was used for this purpose.

Response rate

92.8%.

This layer shows basic population and housing context. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant housing type: owner-occupied, renter-occupied, or vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B01001, B03002, B05003, B05011, B19049, B25002, B25003, B25058, B25077 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Housing Assessment Resource Tools (HART) This dataset contains 2 tables and 5 files which draw upon data from the 2021 Census of Canada. The tables are a custom order and contain data pertaining to older adults and housing need. The 2 tables have 6 dimensions in common and 1 dimension that is unique to each table. Table 1's unique dimension is the "Ethnicity / Indigeneity status" dimension which contains data fields related to visible minority and Indigenous identity within the population in private households. Table 2's unique dimension is "Structural type of dwelling and Period of Construction" which contains data fields relating to the structural type and period of construction of the dwelling. Each of the two tables is then split into multiple files based on geography. Table 1 has two files: Table 1.1 includes Canada, Provinces and Territories (14 geographies), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); and Table 1.2 includes Canada and the CMAs of Canada (44). Table 2 has three files: Table 2.1 includes Canada, Provinces and Territories (14), CDs of NWT (6), CDs of Yukon (1) and CDs of Nunavut (3); Table 2.2 includes Canada and the CMAs of Canada excluding Ontario and Quebec (20 geographies); and Table 2.3 includes Canada and the CMAs of Canada that are in Ontario and Quebec (25 geographies). The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and data fields: Geography: - Country of Canada as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia) as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), as a whole as well as all census divisions (CDs) within the 3 territories - All 43 census metropolitan areas (CMAs) in Canada Data Quality and Suppression: - The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Universe: Full Universe: Population aged 55 years and over in owner and tenant households with household total income greater than zero in non-reserve non-farm private dwellings. Definition of Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing Need, regardless of their housing circumstances. Data Fields: Table 1: Age / Gender (12) 1. Total – Population 55 years and over 2. Men+ 3. Women+ 4. 55 to 64 years 5. Men+ 6. Women+ 7. 65+ years 8. Men+ 9. Women+ 10. 85+ 11. Men+ 12. Women+ Housing indicators (13) 1. Total – Private Households by core housing need status 2. Households below one standard only...

This layer shows basic population and housing context. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant housing type: owner-occupied, renter-occupied, or vacant. The size of the symbol represents the total count of housing units. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B01001, B03002, B05003, B05011, B19049, B25002, B25003, B25058, B25077 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoThe States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

About this dataset

Context

The dataset tabulates the Red House town population by age. The dataset can be utilized to understand the age distribution and demographics of Red House town.

Content

The dataset constitues the following three datasets

• Red House, New York Age Group Population Dataset: A complete breakdown of Red House town age demographics from 0 to 85 years, distributed across 18 age groups
• Red House, New York Age Cohorts Dataset: Children, Working Adults, and Seniors in Red House town - Population and Percentage Analysis
• Red House, New York Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

U.S. Census Bureau QuickFacts statistics for Montana. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

U.S. Census Bureau QuickFacts statistics for South Carolina. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

About this dataset

Context

The dataset tabulates the House population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for House. The dataset can be utilized to understand the population distribution of House by age. For example, using this dataset, we can identify the largest age group in House.

Key observations

The largest age group in House, NM was for the group of age 60 to 64 years years with a population of 16 (28.07%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in House, NM was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the House is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of House total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for House Population by Age. You can refer the same here

Graph and download economic data for Housing Inventory Estimate: Renter Occupied Housing Units in the United States (ERNTOCCUSQ176N) from Q2 2000 to Q4 2024 about inventories, housing, and USA.

The total population in Malaysia was estimated at 34.1 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Malaysia Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset tabulates the Charlotte Court House population by age. The dataset can be utilized to understand the age distribution and demographics of Charlotte Court House.

Content

The dataset constitues the following three datasets

• Charlotte Court House, VA Age Group Population Dataset: A complete breakdown of Charlotte Court House age demographics from 0 to 85 years, distributed across 18 age groups
• Charlotte Court House, VA Age Cohorts Dataset: Children, Working Adults, and Seniors in Charlotte Court House - Population and Percentage Analysis
• Charlotte Court House, VA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The average American household consisted of 2.51 people in 2023.

Households in the U.S.

As shown in the statistic, the number of people per household has decreased over the past decades.

The U.S. Census Bureau defines a household as follows: “a household includes all the persons who occupy a housing unit as their usual place of residence. A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live and eat separately from any other persons in the building and which have direct access from outside the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated persons who share living arrangements. (People not living in households are classified as living in group quarters.).”

The population of the United States has been growing steadily for decades. Since 1960, the number of households more than doubled from 53 million to over 131 million households in 2023.

Most of these households, about 34 percent, are two-person households. The distribution of U.S. households has changed over the years though. The percentage of single-person households has been on the rise since 1970 and made up the second largest proportion of households in the U.S. in 2022, at 28.88 percent.

In concordance with the rise of single-person households, the percentage of family households with own children living in the household has declined since 1970 from 56 percent to 40.26 percent in 2022.

U.S. Census Bureau QuickFacts statistics for Ohio. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.