The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Monthly Population Estimates by Universe, Age, Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to December 1, 2020 // Source: U.S. Census Bureau, Population Division // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on this file. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. A time series of estimates is produced, beginning with the census. The reference date for all estimates is the first of the month. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

Context

This dataset expands on my earlier New York City Census Data dataset. It includes data from the entire country instead of just New York City. The expanded data will allow for much more interesting analyses and will also be much more useful at supporting other data sets.

Content

The data here are taken from the DP03 and DP05 tables of the 2015 American Community Survey 5-year estimates. The full datasets and much more can be found at the American Factfinder website. Currently, I include two data files:

1. acs2015_census_tract_data.csv: Data for each census tract in the US, including DC and Puerto Rico.
2. acs2015_county_data.csv: Data for each county or county equivalent in the US, including DC and Puerto Rico.

The two files have the same structure, with just a small difference in the name of the id column. Counties are political subdivisions, and the boundaries of some have been set for centuries. Census tracts, however, are defined by the census bureau and will have a much more consistent size. A typical census tract has around 5000 or so residents.

The Census Bureau updates the estimates approximately every year. At least some of the 2016 data is already available, so I will likely update this in the near future.

Acknowledgements

The data here were collected by the US Census Bureau. As a product of the US federal government, this is not subject to copyright within the US.

Inspiration

There are many questions that we could try to answer with the data here. Can we predict things such as the state (classification) or household income (regression)? What kinds of clusters can we find in the data? What other datasets can be improved by the addition of census data?

The U.S. Census Bureau TIGER/Line® files in this data collection were originally distributed by the Inter-university Consortium for Political and Social Research (ICPSR) through its TIGER/Line file web site, which was decommissioned in 2018 (archived version: https://web.archive.org/web/20090924181858/http://www.icpsr.umich.edu/TIGER/index.html). There, users could download various versions of the U.S. Census Bureau's TIGER (Topologically Integrated Geographic Encoding and Referencing) database. The TIGER/Line files do not include demographic data, but they do contain geographic information that can be linked to the Census Bureau’s demographic data. Due to file number limitations in openICPSR, the original data collections have been bundled into single zip packages. A single TIGER_directory.txt file listing the original files and the original directory structure is included with the root directory. Documentation files are also included as standalone subdirectories in each collection so users do not need to download entire zip bundles to view documentation. The TIGER/Line data are stored in compressed format in subdirectories by state name. There is one TIGER/Line file (in a compressed format) for each county or county equivalent. The file names consist of TGR + the 2-digit state FIPS (Federal Information Processing Standards) code + the 3-digit county FIPS code (i.e. TGR01031.ZIP for Coffee County, Alabama). Each state folder contains individual county files.The individual county files include one file for each record type included for that county with the following name convention: tgr01031.rt1. The convention follows the order described above with each file having a suffix which includes 'rt' (record type) followed by its designation (in this case record type 1). Each county file also contains its own metadata record.If present, documentation files for the TIGER/Line data are stored in a directory named '0docs' which is located in the 'Parent Directory'. This directory appears at the top of the index of state subdirectories for each edition of the TIGER/Line files. The documentation includes a complete list of FIPS state and county codes.

Abstract

This dataset includes all individuals from the 1870 US census.

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Documentation

This dataset was developed through a collaboration between the Minnesota Population Center and the Church of Jesus Christ of Latter-Day Saints. The data contain demographic variables, economic variables, migration variables and race variables. Unlike more recent census datasets, pre-1900 census datasets only contain individual level characteristics and no household or family characteristics, but household and family identifiers do exist.

The official enumeration day of the 1870 census was 1 June 1870. The main goal of an early census like the 1870 U.S. census was to allow Congress to determine the collection of taxes and the appropriation of seats in the House of Representatives. Each district was assigned a U.S. Marshall who organized other marshals to administer the census. These enumerators visited households and recorder names of every person, along with their age, sex, color, profession, occupation, value of real estate, place of birth, parental foreign birth, marriage, literacy, and whether deaf, dumb, blind, insane or “idiotic”.

Sources: Szucs, L.D. and Hargreaves Luebking, S. (1997). Research in Census Records, The Source: A Guidebook of American Genealogy. Ancestry Incorporated, Salt Lake City, UT Dollarhide, W.(2000). The Census Book: A Genealogist’s Guide to Federal Census Facts, Schedules and Indexes. Heritage Quest, Bountiful, UT

Starting in mid-July of 2020, despite many delays due to Covid-19, census takers began interviewing households who had not yet responded online or via the mail to the U.S. 2020 Census. The federal census, required by the United States’ Constitution, happens once every 10 years and each time, there are new variations in enumeration (counting) techniques and what statistical data to collect. There are processes around “how” to count and then also “what” to count; the data collected needs to be useful for governance and allocation yet also respectful of privacy and remain fair and impartial for the entire U.S. population. In 2019 and 2020, hundreds of thousands of temporary workers from local communities were hired to go out into the field as census takers as well as staff offices and provide supervision. This 22nd federal census count began in January 2020 with remote portions of Alaska, where the territory was still frozen and traversable. These employed citizens are just one aspect of how the census is truly a community event. Let’s dive into the history of the U.S. Census and also learn why this count is so important.

Abstract

The 2021 NPHC is tthe first census conducted under the federal structure of Nepal. The main census enumeration was originally scheduled to take place over 15 days- from June 8 to 22, 2021, but due to the COVID-19 pandemic, the enumeration was postponed for five months. Once the impact of the pandemic subsided, the enumeration was carried out according to a new work plan for a 15 dya period from November 11 to 25, 2021.

This report contains statistical tables at the national, provincial, district and municipal levels, derived from the topics covered in the census questionaires. The work of the analyzing the data in detail is still in progress. The report provides insights into the different aspects of the census operation, including its procedure, concepts, methodology, quality control, logistics, communication, data processing, challenges faced, and other management aspects.

This census slightly differs from the previous censuses mainly due to the following activities: i. three modes of data collection (CAPI, PAPI and e-census); ii. a full count of all questions instead of sampling for certain questions, as was done in the previous two censuses, iii. collaboration with Ministry of Health and Population to ascertain the likely maternal mortality cases reported in the census by skilled health personnel; iv. data processing within its premises; v. recuitment of fresh youths as supervisor and enumerators; and vi. using school teachers as master trainers, especially for the local level training of enumerators.

The objectives of the 2021 Population Census were:

a) to develop a set of benchmark data for different purposes. b) to provide distribution of population by demographic, social and economic characteristics. c) to provide data for small administrative areas of the country on population and housing characteristics. d) to provide reliable frames for different types of sample surveys. e) to provide many demographic indicators like birth rates, death rates and migration rates. f) to project population for the coming years.

The total population of Nepal, as of the census day (25 November 2021) is 29,164,578, of which the number of males is 14,253,551 (48.87 %) and the number of females is 14,911,027 (51.13 %). Accordingly, the sex ratio is 95.59 males per 100 females. Annual average population growth rate is 0.92 percent in 2021.

Geographic coverage

National Level, Ecological belt, Urban and Rural, Province, District, Municipality, Ward Level

Analysis unit

The census results provide information up to the ward level (the lowest administrative level of Nepal), household and indivisual.

Universe

The census covered all modified de jure household members (usual residents)

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f] and online

Research instrument

In this census three main questionnaires were developed for data collection. The first was the Listing Form deveoped mainly for capturing the basic household informatioin in each Enumeration area of the whole country. The second questionnaire was the main questionnaire with eight major Sections as mentioned hereunder.

Listing Questionaire Section 1. Introduction Section 2. House information Section 3. Household information Section 4. Agriculture and livestock information Section 5. Other information

Main Questionaire Section 1. Introduction Section 2. Household Information Section 3. Individual Information Section 4. Educational Information Section 5. Migration Section 6. Fertility Section 7.Disability Section 8. Economic Activity

For the first time, the NPHC, 2021 brougt a Community Questionnaire aiming at capturing the socio-economic and demographic characteristics of the Wards (the lowest administrative division under Rural/Urban Municipalities). The Community Questionnaire contains 6 Chapters. The information derived from community questionnaire is expected to validate (cross checks) certain information collected from main questionnaire.

Community questionaire Section 1. Introduction Section 2. Basic information of wards Section 3. Caste and mother tongue information Section 4. Current status of service within wards Section 5. Access of urban services and facilities within wards Section 6. Status of Disaster Risk

It is noteworty that the digital version of questionnare was applied in collecting data within the selected municipalities of Kathmandu Valley. Enumerators mobilized in Kathmandu Valley were well trained to use tablets. Besides, online mode of data collection was adpoted for all the Nepalese Diplomatic Agencies located abroad.

Cleaning operations

For the concistency of data required logics were set in the data entry programme. For the processing and analysis of data SPSS and STATA programme were employed.

Annual County Resident Population Estimates for 5 Race Groups (5 Race Alone or in Combination Groups) by Selected Age Groups, Sex, and Hispanic Origin // Source: U.S. Census Bureau, Population Division // Note: 'In combination' means in combination with one or more other races. The sum of the five race groups adds to more than the total population because individuals may report more than one race. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Monthly Population Estimates by Universe, Age, Sex, 6 Races, and Hispanic Origin for the United States: April 1, 2010 to July 1, 2013 // File: 7/1/2013 National Population Estimates // Source: U.S. Census Bureau, Population Division // Release Date: June 2014 // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2013) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Monthly Population Estimates by Universe, Age, Sex, 5 Races, and Hispanic Origin for the United States // Source: U.S. Census Bureau, Population Division // Note: 'In combination' means in combination with one or more other races. The sum of the five race groups adds to more than the total population because individuals may report more than one race. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // Persons on active duty in the Armed Forces were not enumerated in the 2010 Census. Therefore, variables for the 2010 Census civilian, civilian noninstitutionalized, and resident population plus Armed Forces overseas populations cannot be derived and are not available on these files. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2013) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Key Table Information.Table Title.Disability Characteristics.Table ID.ACSST1Y2024.S1810.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of hous...

Annual County Resident Population Estimates for 6 Race Groups (5 Race Alone Groups and Two or More Races) by Selected Age Groups, Sex, and Hispanic Origin // Source: U.S. Census Bureau, Population Division // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

2013-2023 Virginia Non-Single Occupancy Vehicle (SOV) Travel Percent by Census Urban Area. Contains estimates. Workers 16 years and over, commuting to work, who are NOT using a car, truck, or van when driving alone.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table DP03, Column DP03_0019PE Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

Documentation of the method to calculate Non-SOV is provided by the Federal Highway Administration (https://www.fhwa.dot.gov/tpm/guidance/hif18024.pdf) page 38 explains the calculation of the Non-SOV Travel measure.

Urban areas with values of -666,666,666 or 0 have blanks calculated for Non-SOV values.

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

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 roughly 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 ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Documentation

This dataset was created on 2020-01-10 22:52:11.461 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1930 households: This dataset includes all households from the 1930 US census.

IPUMS 1930 persons: This dataset includes all individuals from the 1930 US census.

IPUMS 1930 Lookup: This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1930 datasets.

Section 2

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1930 census data was collected in April 1930. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.

• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT, reconstructed using the variable SPLITHID, and the original count is found in the variable SPLITNUM.

• Coded variables derived from string variables are still in progress. These variables include: occupation and industry.

• Missing observations have been allocated and some inconsistencies have been edited for the following variables: SPEAKENG, YRIMMIG, CITIZEN, AGEMARR, AGE, BPL, MBPL, FBPL, LIT, SCHOOL, OWNERSHP, FARM, EMPSTAT, OCC1950, IND1950, MTONGUE, MARST, RACE, SEX, RELATE, CLASSWKR. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.

• Most inconsistent information was not edite

Annual Estimates of the Resident Population by Single Year of Age and Sex for the United States // Source: U.S. Census Bureau, Population Division // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program.// For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014.

Product: SAHIE File Layout Overview

Small Area Health Insurance Estimates Program - SAHIE

Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014

Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau.

Internet Release Date: May 2016

Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties.

For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of:

•5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64

•3 sex categories: both sexes, male, and female

•6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold

•4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race).

In addition, estimates for age category 0-18 by the income categories listed above are published.

Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured.

This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges.

We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response.

The SAHIE program models health insurance coverage by combining survey data from several sources, including:

•The American Community Survey (ACS)

•Demographic population estimates

•Aggregated federal tax returns

•Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program

•County Business Patterns

•Medicaid

•Children's Health Insurance Program (CHIP) participation records

•Census 2010

Margin of error (MOE). Some ACS products provide

an MOE instead of confidence intervals. An MOE is the

difference between an estimate and its upper or lower

confidence bounds. Confidence bounds can be created

by adding the margin of error to the estimate (for the

upper bound) and subtracting the margin of error from

the estimate (for the lower bound). All published ACS

margins of error are based on a 90-percent confidence

level.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Projected Births by Sex, Race, and Hispanic Origin for the United States: 2016-2060 // Source: U.S. Census Bureau, Population Division // There are four projection scenarios: 1. Main series, 2. High Immigration series, 3. Low Immigration series, and 4. Zero Immigration series. // Note: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. All projected births are considered native born. // For detailed information about the methods used to create the population projections, see https://www2.census.gov/programs-surveys/popproj/technical-documentation/methodology/methodstatement17.pdf. // Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. The Population Estimates and Projections Program provides additional information on its website: https://www.census.gov/programs-surveys/popproj.html.