Link to the Open Data site for the United States Census Bureau.

Find information using interactive applications to get statistics from multiple surveys.

Links to Audit Reports conducted on the U.S. Census

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).

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 map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Release Date: 2020-06-09.Release Schedule:.The data in this file come from the 2017 Economic Census data files released on a flow basis starting in September 2019. As such, preliminary U.S. totals released in September 2019 will be superseded with final totals, by sector, once data for all states have been released. Users should be aware that during the release of this consolidated file, data at more detailed North American Industry Classification System (NAICS) and geographic levels may not add to higher-level totals. However, at the completion of the economic census (once all the component files have been released), the detailed data in this file will add to the totals. For more information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.U.S. totals released in September 2019 will be superseded with final totals, by sector, once data for all states have been released..Includes only establishments and firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records: .Number of firms.Number of establishments.Sales, value of shipments, or revenue ($1,000).Annual payroll ($1,000).First-quarter payroll ($1,000).Number of employees.Range indicating percent of total sales, value of shipments, or revenue imputed.Range indicating percent of total annual payroll imputed.Range indicating percent of total employees imputed..Geography Coverage:.The data are shown for employer establishments and firms at the U.S., State, Combined Statistical Area, Metropolitan and Micropolitan Statistical Area, Metropolitan Division, Consolidated City, County (and equivalent), and Economic Place (and equivalent; incorporated and unincorporated) levels that vary by industry. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown at the 2- through 6-digit 2017 NAICS code levels. For information about NAICS, see Economic Census: Technical Documentation: Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector72/EC1772BASIC.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

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

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

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. Note: The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Graph and download economic data for Unemployment Rate in Mountain Census Division (CMTNURN) from Jan 1976 to May 2025 about Mountain Census Division, unemployment, rate, and USA.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The TIGER/Line Roads County-based dataset was released August 08, 2024, by the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). 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. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529082

VITAL SIGNS INDICATOR Income (EC4)

FULL MEASURE NAME Household income by place of residence

LAST UPDATED May 2019

DESCRIPTION Income reflects the median earnings of individuals and households from employment, as well as the income distribution by quintile. Income data highlight how employees are being compensated for their work on an inflation-adjusted basis.

DATA SOURCE U.S. Census Bureau: Decennial Census Count 4Pb (1970) Form STF3 (1980-1990) Form SF3a (2000) https://nhgis.org

U.S. Census Bureau: American Community Survey Form B19013 (2006-2017; place of residence) http://api.census.gov

Bureau of Labor Statistics: Consumer Price Index All Urban Consumers Data Table (1970-2017; specific to each metro area) http://data.bls.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Income data reported in a given year reflects the income earned in the prior year (decennial Census) or in the prior 12 months (American Community Survey); note that this inconsistency has a minor effect on historical comparisons (for more information, go to: http://www.census.gov/acs/www/Downloads/methodology/ASA_nelson.pdf). American Community Survey 1-year data is used for larger geographies – metropolitan areas and counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Quintile income for 1970-2000 is imputed from Decennial Census data using methodology from the California Department of Finance (for more information, go to: http://www.dof.ca.gov/Forecasting/Demographics/Census_Data_Center_Network/documents/How_to_Recalculate_a_Median.pdf). Bay Area income is the population weighted average of county-level income.

Income has been inflated using the Consumer Price Index specific to each metro area; however, some metro areas lack metro-specific CPI data back to 1970 and therefore adjusted data is unavailable for some historical data points. Note that current MSA boundaries were used for historical comparison by identifying counties included in today’s metro areas.

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

The need for a national census became obvious to the Census Office (Bureau of Statistics) during 1997 when a memo was submitted to government officials proposing the need for a national census in an attempt to update old socio-economic figures. The then Acting Director of the Bureau of Statistics and his predecessor shared a similar view: that the 'heydays' and 'prosperity' were nearing their end. This may not have been apparent, as it took until almost mid-2001 for the current Acting Government Statistician to receive instructions to prepare planning for a national census targeted for 2002. It has been repeatedly said that for adequate planning at the national level, information about the characteristics of the society is required. With such information, potential impacts can be forecast and policies can be designed for the improvement and benefit of society. Without it, the people, national planners and leaders will inevitably face uncertainties.

Geographic coverage

National coverage as the Population Census covers the whole of Nauru.

Analysis unit

• Household
• Individual (in a private household dwelling, institutions and non-private dwelling).

Universe

The Census covers all individuals living in private and non-private dwellings and institutions.

Kind of data

Census/enumeration data [cen]

Sampling deviation

There is no sampling for the population census, full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was based on the Pacific Islands Model Population and Housing Census Form and the 1992 census, and comprised two parts: a set of household questions, asked only of the head of household, and an individual questionnaire, administered to each household member. Unlike the previous census, which consisted of a separate household form plus two separate individual forms for Nauruans and non-Nauruans, the 2 002 questionnaire consisted of only one form separated into different parts and sections. Instructions (and skips) were desi

The questionnaire cover recorded various identifiers: district name, enumeration area, house number, number of households (family units) residing, total number of residents, gender, and whether siblings of the head of the house were also recorded. The second page, representing a summary page, listed every individual residing within the house. This list was taken by the enumerator on the first visit, on the eve of census night. The first part of the census questionnaire focused on housing-related questions. It was administered only once in each household, with questions usually asked of the household head. The household form asked the same range of questions as those covered in the 1992 census, relating to type of housing, structure of outer walls, water supply sources and storage, toilet and cooking facilities, lighting, construction materials and subsistence-type activities. The second part of the census questionnaire focused on individual questions covering all household members. This section was based on the 1992 questions, with notable differences being the exclusion of income-level questions and the expansion of fertility and mortality questions. As in 1992, a problem emerged during questionnaire design regarding the question of who or what should determine a ‘Nauruan’. Unlike the 1992 census, where the emphasis was on blood ties, the issue of naturalisation and citizenship through the sale of passports seriously complicated matters in 2 002. To resolve this issue, it was decided to apply two filtering processes: Stage 1 identified persons with tribal heritage through manual editing, and Stage 2 identified persons of Nauruan nationality and citizenship through designed skips in the questionnaire that were incorporated in the data-processing programming.

The topics of questions for each of the parts include: - Person Particulars: - name - relationship - sex - ethnicity - religion - educational attainment - Economic Activity (to all persons 15 years and above): - economic activity - economic inactive - employment status - Fertility: - Fertility - Mortality - Labour Force Activity: - production of cash crops - fishing - own account businesses - handicrafts. - Disability: - type of disability - nature of disability - Household and housing: - electricity - water - tenure - lighting - cooking - sanitation - wealth ownerships

Cleaning operations

Coding, data entry and editing Coding took longer than expected when the Census Office found that more quality-control checks were required before coding could take place and that a large number of forms still required attention. While these quality-control checks were supposed to have been done by the supervisors in the field, the Census Office decided to review all census forms before commencing the coding. This process took approximately three months, before actual data processing could begin. The amount of additional time required to recheck the quality of every census form meant that data processing fell behind schedule. The Census Office had to improvise, with a little pressure from external stakeholders, and coding, in conjunction with data entry, began after recruiting two additional data entry personnel. All four Census Office staff became actively involved with coding, with one staff member alternating between coding and data entry, depending on which process was dropping behind schedule. In the end, the whole process took almost two months to complete. Prior to commencing data entry, the Census Office had to familiarise itself with the data entry processing system. For this purpose, SPC’s Demography/Population Programme was invited to lend assistance. Two office staff were appointed to work with Mr Arthur Jorari, SPC Population Specialist, who began by revising their skills for the data processing software that had been introduced by Dr McMurray. This training attachment took two weeks to complete. Data entry was undertaken using the 2 .3 version of the US Census Bureau’s census and surveying processing software, or CSPro2.3. This version was later updated to CSPro2.4, and all data were transferred accordingly. Technical assistance for data editing was provided by Mr Jorari over a two-week period. While most edits were completed during this period, it was discovered that some batches of questionnaires had not been entered during the initial data capturing. Therefore, batch-edit application had to be regenerated. This process was frequently interrupted by power outages prevailing at the time, which delayed data processing considerably and also required much longer periods of technical support to the two Nauru data processing staff via phone or email (when available).

Data appraisal

Data was compared with Administrative records after the Census to review the quality and reliability of the data.

About this dataset

Context

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

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

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

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.

On the Census Data Hub, CSO Census 2022 and 2016 datasets have been combined with Tailte Éireann official boundary data. Almost 800 variables across 15 themes can be retrieved to make powerful visualisations for both statistical and statutory boundaries. This guide will show you how to view, and quickly visualise data, based on a variable of your choice.Topics covered include: View Census countsSearching for data Viewing Census count data on a map Filtering by location Multiple filters

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2020 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.