Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

This study was conducted under the auspices of the Center for Studies in Demography and Ecology at the University of Washington. It is a nationally representative sample of the population of the United States in 1900, drawn from the manuscript returns of individuals enumerated in the 1900 United States Census. Household variables include region, state and county of household, size of household, and type and ownership of dwelling. Individual variables for each household member include relationship to head of household, race, sex, age, marital status, number of children, and birthplace. Immigration variables include parents' birthplace, year of immigration and number of years in the United States. Occupation variables include occupation, coded by both the 1900 and 1950 systems, and number of months unemployed. Education variables include number of months in school, whether respondents could read or write a language, and whether they spoke English.

2020 Census data for the city of Boston, Boston neighborhoods, census tracts, block groups, and voting districts. In the 2020 Census, the U.S. Census Bureau divided Boston into 207 census tracts (~4,000 residents) made up of 581 smaller block groups. The Boston Planning and Development Agency uses the 2020 tracts to approximate Boston neighborhoods. The 2020 Census Redistricting data also identify Boston’s voting districts.

For analysis of Boston’s 2020 Census data including graphs and maps by the BPDA Research Division and Office of Digital Cartography and GIS, see 2020 Census Research Publications

For a complete official data dictionary, please go to 2020 Census State Redistricting Data (Public Law 94-171) Summary File, Chapter 6. Data Dictionary. 2020 Census State Redistricting Data (Public Law 94-171) Summary File

2020 Census Tracts In Boston

2020 Census Block Groups In Boston

Boston Neighborhood Boundaries Approximated By 2020 Census Tracts

Boston Voting District Boundaries

USE geoid TO JOIN DATA DOWNLOADED FROM DATA.CENSUS.GOV The TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) System (MTS). The TIGER/Line Shapefiles contain a standard geographic identifier (GEOID) for each entity that links to the GEOID in the data from censuses and surveys. The TIGER/Line Shapefiles do not include demographic data from surveys and censuses (e.g., Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program). Other, non-census, data often have this standard geographic identifier as well. Data from many of the Census Bureau’s surveys and censuses, including the geographic codes needed to join to the TIGER/Line Shapefiles, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/). Public Use Microdata Areas (PUMAs) are statistical geographic areas for the dissemination of decennial census and American Community Survey (ACS) Public Use Microdata Sample files in which the Census Bureau provides selected extracts of raw data from a small sample of census records that are screened to protect confidentiality. The ACS also uses the PUMAs as a tabulation geographic entity. For the 2020 Census, the State Data Centers in each state, the District of Columbia, and Puerto Rico are involved in the delineation of the 2020 PUMAs. Counties and census tracts are used to define PUMAs, and each PUMA must include at least 100,000 people based on the 2020 Census published counts. For the 2020 Census in Guam and the U.S. Virgin Islands, the Census Bureau establishes a single, separate PUMA for each of these two Island Areas. American Samoa and the Commonwealth of the Northern Mariana Islands do not have PUMAs, because the total population of each is under 100,000 people. Downloaded from https://www2.census.gov/geo/tiger/TIGER2022/PUMA/ on June 22, 2023

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Public Use Microdata Areas (PUMAs) are decennial census areas that permit the tabulation and dissemination of Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) data, and data from other censuses and surveys. For the 2020 Census, the State Data Centers (SDCs) in each state, the District of Columbia, and the Commonwealth of Puerto Rico had the opportunity to delineate PUMAS within their state or statistically equivalent entity. All PUMAs must nest within states and have a minimum population threshold of 100,000 persons. 2020 PUMAs consist of census tracts and cover the entirety of the United States, Puerto Rico and Guam. American Samoa, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands do not contain any 2020 PUMAs because the population is less than the minimum population requirement. Each PUMA is identified by a five-character numeric census code that may contain leading zeros and a descriptive name.

The numbers contained in this study are released pursuant to the order of the United States Court of Appeals for the Ninth Circuit in Carter v. Department of Commerce, 307 F.3d 1084. These numbers are not official Census 2000 counts. These numbers are estimates of the population based on a statistical adjustment method, utilizing sampling and modeling, applied to the official Census 2000 figures. The estimates utilized the results of the Accuracy and Coverage Evaluation (A.C.E.), a sample survey intended to measure net over- and undercounts in the census results. The Census Bureau has determined that the A.C.E. estimates dramatically overstate the level of undercoverage in Census 2000, and that the adjusted Census 2000 data are, therefore, not more accurate than the unadjusted data. On March 6, 2001, the Secretary of Commerce decided that unadjusted data from Census 2000 should be used to tabulate population counts reported to states and localities pursuant to 13 U.S.C. 141(c) (see 66 FR 14520, March 13, 2001). The Secretary's decision endorsed the unanimous recommendation of the Executive Steering Committee for A.C.E. Policy (ESCAP), a group of 12 senior career professionals within the Census Bureau. The ESCAP, in its recommendation against the use of the statistically adjusted estimates, had noted serious reservations regarding their accuracy. In order to inform the Census Bureau's planned October 2001 decision regarding the potential use of the adjusted estimates for non-redistricting purposes, the agency conducted extensive analyses throughout the summer of 2001. These extensive analyses confirmed the serious concerns the agency had noted earlier regarding the accuracy of the A.C.E. estimates. Specifically, the adjusted estimates were determined to be so severely flawed that all potential uses of these data would be inappropriate. Accordingly, the Department of Commerce deems that these estimates should not be used for any purpose that legally requires use of data from the decennial census and assumes no responsibility for the accuracy of the data for any purpose whatsoever. The Department, including the U.S. Census Bureau, will provide no assistance in the interpretation or use of these numbers. The collection contains four tables: (1) a count of all persons by race (Table PL1), (2) a count of Hispanic or Latino and a count of not Hispanic or Latino by race of all persons (Table PL2), (3) a count of the population 18 years and older by race (Table PL3), and (4) a count of Hispanic or Latino and a count of not Hispanic or Latino by race for the population 18 years and older (Table PL4).

This dataset contains Decennial Census data for Norfolk, Virginia. Every ten years ending in 0, the United States Census Bureau conducts a census to determine the number of people living in the United States. The release of data includes counts for population, race, ethnicity, group quarters, and housing units. This dataset contains data for all of Norfolk, VA.

Source: U.S. Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) For data about this dataset, please click on the below link: https://data.norfolk.gov/Government/Norfolk-Decennial-Census/dijs-dhze/about_data

The Bureau of Justice Statistics' (BJS) 2007 Census of Public Defender Offices (CPDO) collected data from public defender offices located across 49 states and the District of Columbia. Public defender offices are one of three methods through which states and localities ensure that indigent defendants are granted the Sixth and Fourteenth Amendment right to counsel. (In addition to defender offices, indigent defense services may also be provided by court-assigned private counsel or by a contract system in which private attorneys contractually agree to take on a specified number of indigent defendants or indigent defense cases.) Public defender offices have a salaried staff of full- or part-time attorneys who represent indigent defendants and are employed as direct government employees or through a public, nonprofit organization. Public defenders play an important role in the United States criminal justice system. Data from prior BJS surveys on indigent defense representation indicate that most criminal defendants rely on some form of publicly provided defense counsel, primarily public defenders. Although the United States Supreme Court has mandated that the states provide counsel for indigent persons accused of crime, documentation on the nature and provision of these services has not been readily available. States have devised various systems, rules of organization, and funding mechanisms for indigent defense programs. While the operation and funding of public defender offices varies across states, public defender offices can be generally classified as being part of either a state program or a county-based system. The 22 state public defender programs functioned entirely under the direction of a central administrative office that funded and administered all the public defender offices in the state. For the 28 states with county-based offices, indigent defense services were administered at the county or local jurisdictional level and funded principally by the county or through a combination of county and state funds. The CPDO collected data from both state- and county-based offices. All public defender offices that were principally funded by state or local governments and provided general criminal defense services, conflict services, or capital case representation were within the scope of the study. Federal public defender offices and offices that provided primarily contract or assigned counsel services with private attorneys were excluded from the data collection. In addition, public defender offices that were principally funded by a tribal government, or provided primarily appellate or juvenile services were outside the scope of the project and were also excluded. The CPDO gathered information on public defender office staffing, expenditures, attorney training, standards and guidelines, and caseloads, including the number and type of cases received by the offices. The data collected by the CPDO can be compared to and analyzed against many of the existing national standards for the provision of indigent defense services.

The 2020 Census State Redistrict Data Summary File pages 99 - 113 include the data dictionary reference name and table number and contents that identifies the data dictionary reference name.

The 1990 Public Use Microdata Sample Areas (PUMA) Boundary Files portion of the Archive of Census Related Products (ACRP) consists of 5% sample (apuma) and 1% sample (bpuma) areas for the mapping of 1990 PUMS data covering the continental United States, Alaska, and Hawaii. These boundary files are created based on equivalency files generated by the Geographic Correspondence Engine (GeoCorr). A national census tract to PUMA geography correspondence file is used in merging the two files resulting in the PUMA geographies. An additional file is also available consisting of geographic centroids for the PUMA coverages calculated by UIC (Urban Information Center/Office of Computing, University of Missouri). This portion of the ACRP is produced by the Center for International Earth Science Information Network (CIESIN).

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).

1960 Residence Census Data for Baltimore, Maryland. Refer to the 1960 codebook (codebook_1960.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Abstract

The 2005 Republic of Palau Census of Population and Housing will be used to give a snapshot of Republic of Palau's population and housing at the mid-point of the decade. This Census is also important because it measures the population at the beginning of the implementation of the Compact of Free Association. The information collected in the census is needed to plan for the needs of the population. The government uses the census figures to allocate funds for public services in a wide variety of areas, such as education, housing, and job training. The figures also are used by private businesses, academic institutions, local organizations, and the public in general to understand who we are and what our situation is, in order to prepare better for our future needs.

The fundamental purpose of a census is to provide information on the size, distribution and characteristics of a country's population. The census data are used for policymaking, planning and administration, as well as in management and evaluation of programmes in education, labour force, family planning, housing, health, transportation and rural development. A basic administrative use is in the demarcation of constituencies and allocation of representation to governing bodies. The census is also an invaluable resource for research, providing data for scientific analysis of the composition and distribution of the population and for statistical models to forecast its future growth. The census provides business and industry with the basic data they need to appraise the demand for housing, schools, furnishings, food, clothing, recreational facilities, medical supplies and other goods and services.

Geographic coverage

A hierarchical geographic presentation shows the geographic entities in a superior/subordinate structure in census products. This structure is derived from the legal, administrative, or areal relationships of the entities. The hierarchical structure is depicted in report tables by means of indentation. The following structure is used for the 2005 Census of the Republic of Palau:

Republic of Palau State Hamlet/Village Enumeration District Block

Analysis unit

Individuals Families Households General Population

Universe

The Census covered all the households and respective residents in the entire country.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable to a full enumeration census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2005 Palau Census of Population and Housing comprises three parts: 1. Housing - one form for each household 2. Population - one for for each member of the household 3. People who have left home - one form for each household.

Cleaning operations

Full scale processing and editing activiities comprised eight separate sessions either with or separately but with remote guidance of the U.S. Census Bureau experts to finalize all datasets for publishing stage.

Processing operation was handled with care to produce a set of data that describes the population as clearly and accurately as possible. To meet this objective, questionnaires were reviewed and edited during field data collection operations by crew leaders for consistency, completeness, and acceptability. Questionnaires were also reviewed by census clerks in the census office for omissions, certain inconsistencies, and population coverage. For example, write-in entries such as "Don't know" or "NA" were considered unacceptable in certain quantities and/or in conjunction with other data omissions.

As a result of this review operation, a telephone or personal visit follow-up was made to obtain missing information. Potential coverage errors were included in the follow-up, as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Subsequent to field operations, remaining incomplete or inconsistent information on the questionnaires was assigned using imputation procedures during the final automated edit of the collected data. Allocations, or computer assignments of acceptable data in place of unacceptable entries or blanks, were needed most often when an entry for a given item was lacking or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in previous censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in lace of blanks or unacceptable entries enhanced the usefulness of the data.

Another way to make corrections during the computer editing process is substitution. Substitution is the assignment of a full set of characteristics for a person or housing unit. Because of the detailed field operations, substitution was not needed for the 2005 Census.

Sampling error estimates

Sampling Error is not applicable to full enumeration censuses.

Data appraisal

In any large-scale statistical operation, such as the 2005 Census of the Republic of Palau, human- and machine-related errors were anticipated. These errors are commonly referred to as nonsampling errors. Such errors include not enumerating every household or every person in the population, not obtaining all required information form the respondents, obtaining incorrect or inconsistent information, and recording information incorrectly. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires.

To reduce various types of nonsampling errors, a number of techniques were implemented during the planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

All credit for variables in AHRQ_included_variables.csv is attributed to

• AHRF: Area Health Resources Files (AHRF) 2009-2018. US Department of Health and
  Human Services, Health Resources and Services Administration, Bureau of Health Workforce,
  Rockville, MD.
• AHRQ SDOH Database (v1), from American Hospital Association Annual Survey Data,
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• AHRQ SDOH Database (v1), from Health Resources and Services Administration. (2021, Nov 1).
  Area Health Resources Files [Database]. U.S. Department of Health and Human Services, Health
  Resources and Services Administration, Bureau of Health Workforce.
  https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from the Foundation for AIDS Research. Opioid & Health Indicators
  Database. https://opioid.amfar.org/about/download
• AHRQ SDOH Database (v1), from United States Census Bureau. (n.d.). County Adjacency File
  [Data file]. https://www.census.gov/geographies/reference-files/2010/geo/countyadjacency.html.
• AHRQ SDOH Database (v1), from United States Census Bureau. County Business Patterns [Data
  series]. U.S. Department of Commerce, Bureau of the Census, Data User Services Division.
  https://www.census.gov/programs-surveys/cbp/data/tables.html.
• AHRQ SDOH Database (v1), from Local Education Agency (School District) Finance Survey (F-33)
  Data, v.1a—Provisional Common Core of Data (CCD) - CCD Data Files (ed.gov). Retrieved March
  2021 from https://nces.ed.gov/ccd/files.asp#Fiscal:1,LevelId:5,Page:1.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, Division for Heart
  Disease and Stroke Prevention, Interactive Atlas of Heart Disease and Stroke,
  http://nccd.cdc.gov/DHDSPAtlas.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention. NCHHSTP
  AtlasPlus. Updated 2017. https://www.cdc.gov/nchhstp/atlas/index.htm.
• AHRQ SDOH Database (v1), from PLACES: Local Data for Better Health. Center for Disease
  Control and Prevention (2020 and 2021 Release). Retrieved January 2022 from:
  https://chronicdata.cdc.gov/browse?category=500+Cities+%26+Places&sortBy=newest&utf8.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, National Center for
  Health Statistics. Multiple Cause of Death, 1999–2019, in CDC WONDER Online Database. (2021).
  Data are from the Multiple Cause of Death Files, 1999–2019, as compiled from data provided by
  the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program.
  http://wonder.cdc.gov/mcd-icd10.html.
• United States Census Bureau. Cartographic Boundary Files – Shapefile.
  https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
• AHRQ SDOH Database (v1), from Robert Wood Johnson Foundation, & University of Wisconsin
  Population Health Institute. (2020). County Health Rankings & Roadmaps.
  https://www.countyhealthrankings.org.
• AHRQ SDOH Database (v1), from U.S. Department of Education. (n.d.). Civil Rights Data
  Collection. Retrieved September 2021, from https://ocrdata.ed.gov.
• AHRQ SDOH Database (v1), from Community Resilience Estimates Social, Economic, and
  Housing Statistics Division U.S. Census Bureau. Retrieved September 2021 from
  https://www.census.gov/programs-surveys/community-resilience-estimates.html.
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency. Outdoor Air
  Quality Data. Air Quality Statistics Report. Retrieved March 2022 from:
  https://www.epa.gov/outdoor-air-quality-data/air-quality-statistics-report
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency (EPA) National
  Walkability Index (2021). Retrieved June 2021 from: https://catalog.data.gov/dataset/walkabilityindex.
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture, Economic Research Service.
  2010 Rural-Urban Commuting Area codes, from https://www.ers.usda.gov/data-products/ruralurban-commuting-area-codes.
• AHRQ SDOH Database (v1), from Economic Research Service (ERS), U.S. Department of
  Agriculture (USDA). Food Access Research Atlas, Retrieved November 2021 from
  https://www.ers.usda.gov/data-products/food-access-research-atlas/
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture (USDA), Economic Research
  Service (ERS) 2020. Food Environment Atlas. Retrieved September 2021 from:
  https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-anddocumentation-downloads/.
• AHRQ SDOH Database (v1), from LTCFocus Public Use Data sponsored by the National Institute
  on Aging (P01 AG027296) through a cooperative agreement with the Brown University School of
  Public Health. Available at www.ltcfocus.org. https://doi.org/10.26300/h9a2-2c26.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Home Health
  Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• AHRQ SDOH Database (v1), from the Department of Homeland Security. Homeland Infrastructure
  Foundation-Level Data (HIFLD)—Urgent Care Facilities. 2018. Available: https://hifldgeoplatform.opendata.arcgis.com/datasets/urgent-care-facilities.
• AHRQ SDOH Database (v1), from the Indian Health Service. IHS facilities. 2021. Available:
  https://www.ihs.gov/locations/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June). MA
  State/County Penetration, from https://www.cms.gov/Research-Statistics-DataandSystems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/MA-State-CountyPenetration.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June).
  Medicare Geographic Variation Public Use File, from https://www.cms.gov/Research-StatisticsData-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention; U.S.
  Department of Health and Human Services; Agency for Toxic Substances and Disease Registry.
  Minority Health Social Vulnerability Index 2018. Retrieved July 2021 from
  https://www.minorityhealth.hhs.gov/minority-health-svi/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services, Office of Minority
  Health. (2020). Medicare Mapping Disparities Tool, from https://data.cms.gov/tools/mappingmedicare-disparities-by-population.
• AHRQ SDOH Database (v1), from the Health Resources and Services Administration. (2021).
  Medically Underserved Areas, from https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from Ingram, D. D., & Franco, S. J. (2014, April). 2013 NCHS urban–
  rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and
  Methods Research. Number 166, 1–73. https://pubmed.ncbi.nlm.nih.gov/24776070.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention. (2020).
  National Environmental Public Health Tracking Network from https://www.cdc.gov/ephtracking.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (n.d.). Nursing
  Home Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C.,
  Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for
  U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251.
  Retrieved Oct 29, 2021, from https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-
  0248.1.xml.
  AHRQ SDOH Database (v1), from Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke;
  Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014):
  NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center.
  doi:10.7289/V5M32STR. Retrieved from: https://www.ncei.noaa.gov/data/climdiv/access/.
• AHRQ SDOH Database (v1), from National Center for Environmental Information (NCEI), National
  Environmental Satellite, Data and Information Service (NESDIS) and National Oceanic and
  Atmospheric Administration (NOAA), Storm Events Database 2009-2020 Retrieved August 2021
  from https://www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/.
• AHRQ SDOH Database (v1), from U.S. Census Bureau, Opportunity Insights. Opportunity Atlas [All
  Outcomes by Census Tract Race, Gender, and Parental Income Percentile, All Outcomes by
  County Race, Gender, and Parental Income Percentile] Retrieved October 2021 from:
  https://www.c
• AHRQ SDOH Database (v1), from National Center for Education Statistics. U.S. Skills Map: State
  and County Indicators of Adult Literacy and Numeracy. Retrieved March 2021 from:
  https://nces.ed.gov/surveys/piaac/skillsmap/.
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  Compare. https://data.cms.gov/provider-data/topics/doctors-clinicians.
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  Services File. https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-PublicUse-Files/Provider-of-Services.
• AHRQ SDOH Database (v1), from Small Area Health Insurance Estimates. (n.d.). U.S. Census
  Bureau. https://www.census.gov/data/datasets/time-series/demo/sahie/estimates-acs.html
• AHRQ SDOH Database (v1), from Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D.,
  Jang, H., &

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Public Use Microdata Areas (PUMAs) are decennial census areas that permit the tabulation and dissemination of Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) data, and data from other censuses and surveys. For the 2020 Census, the State Data Centers (SDCs) in each state, the District of Columbia, and the Commonwealth of Puerto Rico had the opportunity to delineate PUMAS within their state or statistically equivalent entity. All PUMAs must nest within states and have a minimum population threshold of 100,000 persons. 2020 PUMAs consist of census tracts and cover the entirety of the United States, Puerto Rico and Guam. American Samoa, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands do not contain any 2020 PUMAs because the population is less than the minimum population requirement. Each PUMA is identified by a five-character numeric census code that may contain leading zeros and a descriptive name.

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted. These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities. The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package. The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or location because they do not fit well into the regional framework. Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values. The RMarkdown document SASAPWebsiteGraphicsCensus.Rmd is used to generate a variety of figures using these data, including the additional file Chignik_population.png. An additional set of 25 figures showing regional trends in population and income metrics are also included.

DATASET DESCRIPTION: Census Block Group polygons from the United States Census Bureau (2022) of the Austin area, including Hays, Travis and Williamson counties and a portion of western Bastrop County. Block groups are clusters of blocks within the same census tract that have the same first digit of their 4-character census block number (e.g., Blocks 3001, 3002, 3003 to 3999 in census tract 1210.02 belong to block group 3).

AUSTIN POLICE DEPARTMENT DATA DISCLAIMER 1. The data provided is for informational use only and may differ from official Austin Police Department data.

1. The Austin Police Department’s databases are continuously updated, and changes can be made due to a variety of investigative factors including but not limited to offense reclassification and dates.

2. The Austin Police Department does not assume any liability for any decision made or action taken or not taken by the recipient in reliance upon any information or data provided.

City of Austin Open Data Terms of Use - https://data.austintexas.gov/stories/s/ranj-cccq

The UK censuses took place on 21st April 1991. They were run by the Census Office for Northern Ireland, General Register Office for Scotland, and the Office of Population and Surveys for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.

Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics, and underpin funding allocation to provide public services.

The Northern Ireland Individual SAR is a 2% sample of individuals which was drawn from the full set of 1991 Census records. It was released to the then Census Microdata Unit (now the Centre for Census and Survey Research) in May 1994 who then undertook quality assurance work and produced documentation and additional derived variables.

The dataset contains 31,967 person records and 53 variables. A number of protections are in place to ensure the anonymity of cases in the data including the low sampling fraction, grouping of some rare categories, limitation of geographical detail and record reordering so that the cases are not ordered geographically.

Once the Household SAR (held under SN 7213) had been removed to avoid an overlap between the two files, remaining records were stratified into groups of 99 and two individuals were chosen from each group. Individuals in communal establishments were stratified geographically into groups of 50 people and one person was chosen at random from each group. Unlike Great Britain, 100% of Northern Ireland records were coded.

For many variables, the codes that are used in the Northern Ireland SARs differ from those used in the GB SARs. Where this is the case, the Northern Ireland coding follows on numerically from the GB coding but it will not necessarily start at 1 and have a value for every succeeding integer. For example, 'household family type' uses quite different coding to the nearest GB equivalent 'family type': the GB codes run from 00 through to 08, whilst the Northern Ireland codes pick up from 09 and run through to 23.

Further information, including guides and other documentation, may be found on the Cathie Marsh Centre for Survey Research Samples of Anonymised Records (SARS) website.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Public Use Microdata Areas (PUMAs) are decennial census areas that permit the tabulation and dissemination of Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) data, and data from other census and surveys. For the 2020 Census, the State Data Centers (SDCs) in each state, the District of Columbia, and the Commonwealth of Puerto Rico had the opportunity to delineate PUMAS within their state or statistically equivalent entity. All PUMAs must nest within states and have a minimum population threshold of 100,000 persons. 2020 PUMAs consist of census tracts and cover the entirety of the United States, Puerto Rico and Guam. American Samoa, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands do not contain any 2020 PUMAs because the population is less than the minimum population requirement. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name. The 2020 PUMAs will appear in the 2022 TIGER/Line Shapefiles.