Abstract

The L2 Voter and Demographic Dataset includes demographic and voter history tables for all 50 states and the District of Columbia. The dataset is built from publicly available government records about voter registration and election participation. These records indicate whether a person voted in an election or not, but they do not record whom that person voted for. Voter registration and election participation data are augmented by demographic information from outside data sources.

Methodology

To create this file, L2 processes registered voter data on an ongoing basis for all 50 states and the District of Columbia, with refreshes of the underlying state voter data typically at least every six months and refreshes of telephone numbers and National Change of Address processing approximately every 30 to 60 days. These data are standardized and enhanced with propriety commercial data and modeling codes and consist of approximately 185,000,000 records nationwide.

Usage

For each state, there are two available tables: demographic and voter history. The demographic and voter tables can be joined on the LALVOTERIDvariable. One can also use the LALVOTERIDvariable to link the L2 Voter and Demographic Dataset with the L2 Consumer Dataset.

In addition, the LALVOTERIDvariable can be used to validate the state. For example, let's look at the LALVOTERID = LALCA3169443. The characters in the fourth and fifth positions of this identifier are 'CA' (California). The second way to validate the state is by using the RESIDENCE_ADDRESSES_STATEvariable, which should have a value of 'CA' (California).

The date appended to each table name represents when the data was last updated. These dates will differ state by state because states update their voter files at different cadences.

The demographic files use 698 consistent variables. For more information about these variables, see 2025-01-10-VM2-File-Layout.xlsx.

The voter history files have different variables depending on the state. The ***2025-07-16-L2-Voter-Dictionaries.tar.gz file contains .csv data dictionaries for each state's demographic and voter files. While the demographic file data dictionaries should mirror the 2025-01-10-VM2-File-Layout.xlsx*** file, the voter file data dictionaries will be unique to each state.

***2025-04-24-National-File-Notes.pdf ***contains L2 Voter and Demographic Dataset ("National File") release notes from 2018 to 2025.

***2025-07-16-L2-Voter-Fill-Rate.tar.gz ***contains .tab files tracking the percent of non-null values for any given field.

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Summary File 4 is repeated or iterated for the total population and 335 additional population groups: 132 race groups,78 American Indian and Alaska Native tribe categories, 39 Hispanic or Latino groups, and 86 ancestry groups.Tables for any population group excluded from SF 2 because the group's total population in a specific geographic area did not meet the SF 2 threshold of 100 people are excluded from SF 4. Tables in SF 4 shown for any of the above population groups will only be shown if there are at least 50 unweighted sample cases in a specific geographic area. The same 50 unweighted sample cases also applied to ancestry iterations. In an iterated file such as SF 4, the universes households, families, and occupied housing units are classified by the race or ethnic group of the householder. The universe subfamilies is classified by the race or ethnic group of the reference person for the subfamily. In a husband/wife subfamily, the reference person is the husband; in a parent/child subfamily, the reference person is always the parent. The universes population in households, population in families, and population in subfamilies are classified by the race or ethnic group of the inidviduals within the household, family, or subfamily without regard to the race or ethnicity of the householder. Notes follow selected tables to make the classification of the universe clear. In any population table where there is no note, the universe classification is always based on the race or ethnicity of the person. In all housing tables, the universe classification is based on the race or ethnicity of the householder.

This data collection contains summary statistics on population and housing subjects derived from the responses to the 2010 Census questionnaire. Population items include sex, age, average household size, household type, and relationship to householder such as nonrelative or child. Housing items include tenure (whether a housing unit is owner-occupied or renter-occupied), age of householder, and household size for occupied housing units. Selected aggregates and medians also are provided. The summary statistics are presented in 71 tables, which are tabulated for multiple levels of observation (called "summary levels" in the Census Bureau's nomenclature), including, but not limited to, regions, divisions, states, metropolitan/micropolitan areas, counties, county subdivisions, places, ZIP Code Tabulation Areas (ZCTAs), school districts, census tracts, American Indian and Alaska Native areas, tribal subdivisions, and Hawaiian home lands. There are 10 population tables shown down to the county level and 47 population tables and 14 housing tables shown down to the census tract level. Every table cell is represented by a separate variable in the data. Each table is iterated for up to 330 population groups, which are called "characteristic iterations" in the Census Bureau's nomenclature: the total population, 74 race categories, 114 American Indian and Alaska Native categories, 47 Asian categories, 43 Native Hawaiian and Other Pacific Islander categories, and 51 Hispanic/not Hispanic groups. Moreover, the tables for some large summary areas (e.g., regions, divisions, and states) are iterated for portions of geographic areas ("geographic components" in the Census Bureau's nomenclature) such as metropolitan/micropolitan statistical areas and the principal cities of metropolitan statistical areas. The collection has a separate set of files for every state, the District of Columbia, Puerto Rico, and the National File. Each file set has 11 data files per characteristic iteration, a data file with geographic variables called the "geographic header file," and a documentation file called the "packing list" with information about the files in the file set. Altogether, the 53 file sets have 110,416 data files and 53 packing list files. Each file set is compressed in a separate ZIP archive (Datasets 1-56, 72, and 99). Another ZIP archive (Dataset 100) contains a Microsoft Access database shell and additional documentation files besides the codebook. The National File (Dataset 99) constitutes the National Update for Summary File 2. The National Update added summary levels for the United States as a whole, regions, divisions, and geographic areas that cross state lines such as Core Based Statistical Areas.

Key Table Information.Table Title.Nonemployer Statistics by Demographics series (NES-D): Statistics for Employer and Nonemployer Firms by Industry and Race for the U.S., States, Metro Areas, Counties, and Places: 2022.Table ID.ABSNESD2022.AB00MYNESD01C.Survey/Program.Economic Surveys.Year.2022.Dataset.ECNSVY Nonemployer Statistics by Demographics Company Summary.Source.U.S. Census Bureau, 2022 Economic Surveys, Nonemployer Statistics by Demographics.Release Date.2025-05-08.Release Schedule.The Nonemployer Statistics by Demographics (NES-D) is released yearly, beginning in 2017..Sponsor.National Center for Science and Engineering Statistics, U.S. National Science Foundation.Table Universe.Data in this table combines estimates from the Annual Business Survey (employer firms) and the Nonemployer Statistics by Demographics (nonemployer firms).Includes U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series).Includes U.S. employer firms estimates of business ownership by sex, ethnicity, race, and veteran status from the 2023 Annual Business Survey (ABS) collection. The employer business dataset universe consists of employer firms that are in operation for at least some part of the reference year, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees and annual receipts of $1,000 or more, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS), except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered.Data are also obtained from administrative records, the 2022 Economic Census, and other economic surveys. Note: For employer data only, the collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2023 ABS collection year produces statistics for the 2022 reference year. The "Year" column in the table is the reference year..Methodology.Data Items and Other Identifying Records.Total number of employer and nonemployer firmsTotal sales, value of shipments, or revenue of employer and nonemployer firms ($1,000)Number of nonemployer firmsSales, value of shipments, or revenue of nonemployer firms ($1,000)Number of employer firmsSales, value of shipments, or revenue of employer firms ($1,000)Number of employeesAnnual payroll ($1,000)These data are aggregated by the following demographic classifications of firm for:All firms Classifiable (firms classifiable by sex, ethnicity, race, and veteran status) Race White Black or African American American Indian and Alaska Native Asian Native Hawaiian and Other Pacific Islander Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White) Equally minority/nonminority Nonminority (Firms classified as non-Hispanic and White) Unclassifiable (firms not classifiable by sex, ethnicity, race, and veteran status) Definitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the NES-D and the ABS are companies or firms rather than establishments. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization..Geography Coverage.The 2022 data are shown for the total of all sectors (00) and the 2- to 6-digit NAICS code levels for:United StatesStates and the District of ColumbiaIn addition, the total of all sectors (00) NAICS and the 2-digit NAICS code levels for:Metropolitan Statistical AreasMicropolitan Statistical AreasMetropolitan DivisionsCombined Statistical AreasCountiesEconomic PlacesFor information about geographies, see Geographies..Industry Coverage.The data are shown for the total of all sectors ("00"), and at the 2- through 6-digit NAICS code levels depending on geography. Sector "00" is not an official NAICS sector but is rather a way to indicate a total for multiple sectors. Note: Other programs outside of ABS may use sector 00 to indicate when multiple NAICS sectors are being displayed within the same table and/or dataset.The following are excluded from the total of all sectors:Crop and Animal Production (NAICS 111 and 112)Rail Transportation (NAICS 482)Postal Service (NAICS 491)Monetary Authorities-Central Bank (NAICS 521)Funds, Trusts, and Other Financial Vehicles (NAICS 525)Office of Notaries (NAICS 541120)Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813)Private Households (NAICS 814)Public Administration (NAICS 92)For information about NAICS, see North American Industry Classification System..Sa...

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 IPUMS 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

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 1940 census data was collected in April 1940. 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 SPLIT40, reconstructed using the variable SERIAL40, and the original count is found in the variable NUMPREC40.
• Some variables are missing from this data set for specific enumeration districts. The enumeration districts with missing data can be identified using the variable EDMISS. These variables will be added in a future release.
• Coded variables derived from string variables are still in progress. These variables include: occupation, industry and migration status.
• Missing observations have been allocated and some inconsistencies have been edited for the following variables: Missing observations have been allocated and some inconsistencies have been edited for the following variables: SURSIM, SEX, SCHOOL, RELATE, RACE, OCC1950, MTONGUE, MBPL, FBPL, BPL, MARST, EMPSTAT, CITIZEN, OWNERSHP. 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 edited for this release, thus there are observations outside of the universe for many variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next r

The dataset contains COVID-19 data summary tables on confirmed cases of COVID-19, geographic distribution of cases, demographic distribution of confirmed cases, daily cases hospitalizations and deaths, and the geographic distribution of tests in Alaska.

The Decennial Census provides population estimates and demographic information on residents of the United States.

The Census Summary Files contain detailed tables on responses to the decennial census. Data tables in Summary File 1 provide information on population and housing characteristics, including cross-tabulations of age, sex, households, families, relationship to householder, housing units, detailed race and Hispanic or Latino origin groups, and group quarters for the total population. Summary File 2 contains data tables on population and housing characteristics as reported by housing unit.

Researchers at NYU Langone Health can find guidance for the use and analysis of Census Bureau data on the Population Health Data Hub (listed under "Other Resources"), which is accessible only through the intranet portal with a valid Kerberos ID (KID).

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Summary files include the following geographies: nation, all states (including DC and Puerto Rico), all metropolitan areas, all congressional districts (114th congress), all counties, all places, and all tracts and block groups. Summary files contain the most detailed cross-tabulations, many of which are published down to block groups. The data are population and housing counts. There are over 64,000 variables in this dataset.

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.

Summary File 1 Data Profile 1 (SF1 Table DP-1) for Census Tracts in the Minneapolis-St. Paul 7 County metropolitan area is a subset of the profile of general demographic characteristics for 2000 prepared by the U.S. Census Bureau.

This table (DP-1) includes: Sex and Age, Race, Race alone or in combination with one or more otehr races, Hispanic or Latino and Race, Relationship, Household by Type, Housing Occupancy, Housing Tenure

US Census 2000 Demographic Profiles: 100-percent and Sample Data

The profile includes four tables (DP-1 thru DP-4) that provide various demographic, social, economic, and housing characteristics for the United States, states, counties, minor civil divisions in selected states, places, metropolitan areas, American Indian and Alaska Native areas, Hawaiian home lands and congressional districts (106th Congress). It includes 100-percent and sample data from Census 2000. The DP-1 table is available as part of the Summary File 1 (SF 1) dataset, and the other three tables are available as part of the Summary File 3 (SF 3) dataset.

The US Census provides DP-1 thru DP-4 data at the Census tract level through their DataFinder search engine. However, since the Metropolitan Council and MetroGIS participants are interested in all Census tracts within the seven county metropolitan area, it was quicker to take the raw Census SF-1 and SF-3 data at tract levels and recreate the DP1-4 variables using the appropriate formula for each DP variable. This file lists the formulas used to create the DP variables.

Abstract (en): Summary File 4 (SF 4) from the United States 2000 Census contains the sample data, which is the information compiled from the questions asked of a sample of all people and housing units. Population items include basic population totals: urban and rural, households and families, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status, year of entry, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include basic housing totals: urban and rural, number of rooms, number of bedrooms, year moved into unit, household size and occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, monthly rent, and shelter costs. In Summary File 4, the sample data are presented in 213 population tables (matrices) and 110 housing tables, identified with "PCT" and "HCT" respectively. Each table is iterated for 336 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), 39 Hispanic or Latino groups, and 86 ancestry groups. The presentation of SF4 tables for any of the 336 population groups is subject to a population threshold. That is, if there are fewer than 100 people (100-percent count) in a specific population group in a specific geographic area, and there are fewer than 50 unweighted cases, their population and housing characteristics data are not available for that geographic area in SF4. For the ancestry iterations, only the 50 unweighted cases test can be performed. See Appendix H: Characteristic Iterations, for a complete list of characteristic iterations. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. All persons in housing units in Iowa in 2000. 2013-05-25 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 342 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 341 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 340 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 339 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 338 and flagged as study-level files, so that they will accompany all downloads. Because of the number of files per state in Summary File 4, ICPSR has given each state its own ICPSR study number in the range ICPSR 13512-13563. The study number for the national file is 13570. Data for each state are being released as they become available.The data are provided in 38 segments (files) per iteration. These segments are PCT1-PCT4, PCT5-PCT16, PCT17-PCT34, PCT35-PCT37, PCT38-PCT45, PCT46-PCT49, PCT50-PCT61, PCT62-PCT67, PCT68-PCT71, PCT72-PCT76, PCT77-PCT78, PCT79-PCT81, PCT82-PCT84, PCT85-PCT86 (partial), PCT86 (partial), PCT87-PCT103, PCT104-PCT120, PCT121-PCT131, PCT132-PCT137, PCT138-PCT143, PCT144, PCT145-PCT150, PCT151-PCT156, PCT157-PCT162, PCT163-PCT208, PCT209-PCT213, HCT1-HCT9, HCT10-HCT18, HCT19-HCT22, HCT23-HCT25, HCT26-HCT29, HCT30-HCT39, HCT40-HCT55, HCT56-HCT61, HCT62-HCT70, HCT71-HCT81, HCT82-HCT86, and HCT87-HCT110. The iterations are Parts 1-336, the Geographic Header File is Part 337. The Geographic Header File is in fixed-format ASCII and the table files are in comma-delimited ASCII format. A merged iteration will have 7,963 variables.For Parts 251-336, the part names contain numbers within parentheses that refer to the Ancestry Code List (page G1 of the codebook).

The AIAN Summary File contains data on population characteristics, such as sex, age, average household size, household type, and relationship to householder. The American Indian and Alaska Native Summary File (AIANSF) contains data on population characteristics, such as sex, age, average household size, household type, and relationship to householder. The file also includes housing characteristics, such as tenure (whether a housing unit is owner-occupied or renter- occupied) and age of householder for occupied housing units. Selected aggregates and medians also are provided. A complete listing of subjects in the AIANSF is found in Chapter 3, Subject Locator. The layout of the tables in the AIANSF is similar to that in Summary File 2 (SF 2). These data are presented in 47 population tables (identified with a "PCT") and 14 housing tables (identified with an "HCT") shown down to the census tract level; and 10 population tables (identified with a "PCO") shown down to the county level, for a total of 71 tables. Each table is iterated for the total population, the total American Indian and Alaska Native population alone, the total American Indian and Alaska Native population alone or in combination, and 1,567 detailed tribes and tribal groupings. Tribes or tribal groupings are included on the iterations list if they met a threshold of at least 100 people in the 2010 Census. In addition, the presentation of AIANSF tables for any of the tribes and tribal groupings is subject to a population threshold of 100 or more people in a given geography. That is, if there are fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area in the AIANSF. See Appendix H, Characteristic Iterations, for more information.

Abstract (en): Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner-occupied or renter- occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people, that is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. All persons in housing units in Colorado in 2000. 2013-05-24 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 256 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 255 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 254 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 253 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 252 and flagged as study-level files, so that they will accompany all downloads. Because of the number of files per state in Summary File 2, ICPSR has given each state its own ICPSR study number in the range ICPSR 13233-13284. Data for each state are being released as they become available.The data are provided in four segments (files) per iteration. These segments are PCT1-PCT4, PCT5-PCT19, PCT20-PCT36, and HCT1-HCT11. The iterations are Parts 1-250, the Geographic Header file is Part 251. The Geographic Header file is in fixed-format ASCII and the Table files are in comma-delimited ASCII format. The Geographic Header file has 85 variables, Segment 01 has 224 variables, Segment 02 has 240 variables, Segment 03 has 179 variables, and Segment 04 has 141 variables. When all the segments are merged there are 849 variables.

The American Community Survey (ACS) provides detailed demographic, social, economic, commuting and housing statistics based on continuous survey data collection. Data collected over the most recent 5 years are batched, summarized and published the following December.

These files contain summary data for Census Block Groups (CensusACSBlockGroup.xlsx), Tracts (CensusACSTract.xlsx), minor civil divisions (CensusACSMCD.xlsx), school districts (CensusACSSchoolDistrict.xlsx), and ZIP code tabulation areas (CensusACSZipCode.xlsx). No shapefiles are included, but these data files can be joined to associated shapefile datasets available elsewhere on this site. To facilitate this, the data files are also available as DBF tables and in a geodatabase.

Starting with the 2016-2020 data, tract and block group boundaries are those used in the 2020 Census. Starting with the 2017-2021 data, ZIP Code Tabulation Areas are those defined based on the 2020 Census. If you need the most recent ACS data for the tract and block group boundaries used in the 2010 Census, contact Matt Schroeder (information below).

Summary File 1 Data Profile 1 (SF1 Table DP-1) for cities and townships in Minnesota is a subset of the profile of general demographic characteristics for 2000 prepared by the U.S. Census Bureau.

This table includes: Sex and Age, Race, Race alone or in combination with one or more otehr races, Hispanic or Latino and Race, Relationship, Household by Type, Housing Occupancy, Housing Tenure

US Census 2000 Demographic Profiles: 100-percent and Sample Data

A profile includes four tables that provide various demographic, social, economic, and housing characteristics for the United States, states, counties, minor civil divisions in selected states, places, metropolitan areas, American Indian and Alaska Native areas, Hawaiian home lands and congressional districts (106th Congress). It includes 100-percent and sample data from Census 2000.

The Demographic Profile consists of four tables (DP-1 thru DP-4). For Census 2000 data, the DP-1 table is available as part of the Summary File 1 (SF 1) dataset, and the other three tables are available as part of the Summary File 3 (SF 3) dataset.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

Abstract

The primary objective of the 2017 Indonesia Dmographic and Health Survey (IDHS) is to provide up-to-date estimates of basic demographic and health indicators. The IDHS provides a comprehensive overview of population and maternal and child health issues in Indonesia. More specifically, the IDHS was designed to: - provide data on fertility, family planning, maternal and child health, and awareness of HIV/AIDS and sexually transmitted infections (STIs) to help program managers, policy makers, and researchers to evaluate and improve existing programs; - measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as residence, education, breastfeeding practices, and knowledge, use, and availability of contraceptive methods; - evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; - assess married men’s knowledge of utilization of health services for their family’s health and participation in the health care of their families; - participate in creating an international database to allow cross-country comparisons in the areas of fertility, family planning, and health.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women age 15-49 years resident in the household, and all men age 15-54 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 IDHS sample covered 1,970 census blocks in urban and rural areas and was expected to obtain responses from 49,250 households. The sampled households were expected to identify about 59,100 women age 15-49 and 24,625 never-married men age 15-24 eligible for individual interview. Eight households were selected in each selected census block to yield 14,193 married men age 15-54 to be interviewed with the Married Man's Questionnaire. The sample frame of the 2017 IDHS is the Master Sample of Census Blocks from the 2010 Population Census. The frame for the household sample selection is the updated list of ordinary households in the selected census blocks. This list does not include institutional households, such as orphanages, police/military barracks, and prisons, or special households (boarding houses with a minimum of 10 people).

The sampling design of the 2017 IDHS used two-stage stratified sampling: Stage 1: Several census blocks were selected with systematic sampling proportional to size, where size is the number of households listed in the 2010 Population Census. In the implicit stratification, the census blocks were stratified by urban and rural areas and ordered by wealth index category.

Stage 2: In each selected census block, 25 ordinary households were selected with systematic sampling from the updated household listing. Eight households were selected systematically to obtain a sample of married men.

For further details on sample design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 IDHS used four questionnaires: the Household Questionnaire, Woman’s Questionnaire, Married Man’s Questionnaire, and Never Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49, the Woman’s Questionnaire had questions added for never married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. The Household Questionnaire and the Woman’s Questionnaire are largely based on standard DHS phase 7 questionnaires (2015 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were included in the IDHS. Response categories were modified to reflect the local situation.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computer-identified errors. Data processing activities were carried out by a team of 34 editors, 112 data entry operators, 33 compare officers, 19 secondary data editors, and 2 data entry supervisors. The questionnaires were entered twice and the entries were compared to detect and correct keying errors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2017 IDHS.

Response rate

Of the 49,261 eligible households, 48,216 households were found by the interviewer teams. Among these households, 47,963 households were successfully interviewed, a response rate of almost 100%.

In the interviewed households, 50,730 women were identified as eligible for individual interview and, from these, completed interviews were conducted with 49,627 women, yielding a response rate of 98%. From the selected household sample of married men, 10,440 married men were identified as eligible for interview, of which 10,009 were successfully interviewed, yielding a response rate of 96%. The lower response rate for men was due to the more frequent and longer absence of men from the household. In general, response rates in rural areas were higher than those in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2017 Indonesia Demographic and Health Survey (2017 IDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 IDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 IDHS is a STATA program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix C of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix D of the survey final report.

Abstract

This dataset was curated for the digital humanities portion of the project "500 Years of Black History in South Florida" by Synatra Smith, Luling Huang, and Portia Hopkins.

Methodology

Data was curated at the U.S. Census Tract level for four counties in South Florida: Broward, Miami-Dade, Monroe, and Palm Beach.

There are two tables in this dataset:

• sociodem_enepov: This table contains several sociodemographic variables and energy burden level at the census tract level;
• airqua: This table contains monthly median air quality data at the census tract level for Year 2016.

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The sociodemographic data come from the American Community Survey (2020 5-year estimates). The variables include fraction of black population, median income, unemployment rate, and four education level variables for population 25 years or above: fraction of population below high school, fraction of population who had high school diploma only, fraction of population who had a college degree or equivalent only, and fraction of population who had a graduate degree. Here are the table numbers and relevant columns from the U.S. Census data portal:

• Education: S1501
• Black population: B01001B (column: B01001B_001E)
• Total population: B01001 (for calculating fraction of black population)
• Median income: S1903 (column: S1903_C03_001E)
• Employment: S2301 (column: S2301_C04_001E)

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The energy burden data come from the U.S. Department of Energy's Low-Income Energy Affordability Data (LEAD) tool. The air quality (PM2.5 concentration) data come from the U.S. Centers for Disease Control and Prevention's Daily Census Tract-Level PM2.5 Concentrations, 2016.

This project is conducted on behalf of the Association for the Study of African American Life and History and the National Park Service with additional funding from the Council on Library and Information Resources.

References

• Centers for Disease Control and Prevention. (n.d.). National Environmental Public Health Tracking Network [Data set]. www.cdc.gov/ephtracking

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• Ma, O. (2018). *Low-Income Energy Affordability Data - LEAD Tool - 2018 Update *[Data set]. U.S. Department of Energy. https://dx.doi.org/10.25984/1784729

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• U.S. Census Bureau. (2023). United States Census Bureau: Data [Data set]. U.S. Department of Commerce. https://data.census.gov/

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Usage

This dataset curates from data existing in the public domain and can be used for other purposes freely with attribution.

Summary File 3 Data Profile 4 (SF3 Table DP-4) for cities and townships in Minnesota is a subset of the profile of selected housing characteristics for 2000 prepared by the U. S. Census Bureau.

This table includes: Units in Structure, Year Structure Built, Rooms, Year Householder Moved into Unit, Vehicles Available, House Heating Fuel, Selected Characteristics, Occupants per Room, Value, Mortgage Status and Selected Monthly Owner Costs, Selected Monthly Owner Costs as a Percentage of Household Income in 1999, Gross Rent, Gross Rent as a Percentage of Household Income in 1999

US Census 2000 Demographic Profiles: 100-percent and Sample Data

A profile includes four tables that provide various demographic, social, economic, and housing characteristics for the United States, states, counties, minor civil divisions in selected states, places, metropolitan areas, American Indian and Alaska Native areas, Hawaiian home lands and congressional districts (106th Congress). It includes 100-percent and sample data from Census 2000.

The Demographic Profile consists of four tables (DP-1 thru DP-4). For Census 2000 data, the DP-1 table is available as part of the Summary File 1 (SF 1) dataset, and the other three tables are available as part of the Summary File 3 (SF 3) dataset.

Summary File 3 Data Profile 2 (SF3 Table DP-2) for cities and townships in Minnesota is a subset of the profile of selected social characteristics for 2000 prepared by the U.S. Census Bureau.

This table includes: School Enrollment, Educational Attainment, Marital Status, Grandparents as Caregivers, Veran Status, Disability Status of the Civilian Noninstitutionalized Population, Residence in 1995, Nativity and Place of Birth, Region of Birth of Foreign Born, Language Spoken At Home, Ancestry

US Census 2000 Demographic Profiles: 100-percent and Sample Data

A profile includes four tables that provide various demographic, social, economic, and housing characteristics for the United States, states, counties, minor civil divisions in selected states, places, metropolitan areas, American Indian and Alaska Native areas, Hawaiian home lands and congressional districts (106th Congress). It includes 100-percent and sample data from Census 2000.

The Demographic Profile consists of four tables (DP-1 thru DP-4). For Census 2000 data, the DP-1 table is available as part of the Summary File 1 (SF 1) dataset, and the other three tables are available as part of the Summary File 3 (SF 3) dataset.