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).

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

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.

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

This is 2020 decennial census data at the county level. Technical documentation for the 2020 census is available here: https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_NationalTechDoc.pdf

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).

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 dataset contains model-based census tract level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 29 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

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

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

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 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 .

Selected variables from the most recent ACS Community Survey (Released 2023) aggregated by Community Area. Additional years will be added as they become available. The underlying algorithm to create the dataset calculates the % of a census tract that falls within the boundaries of a given community area. Given that census tracts and community area boundaries are not aligned, these figures should be considered an estimate. Total population in this dataset: 2,647,621 Total Chicago Population Per ACS 2023: 2,664,452 % Difference: -0.632% There are different approaches in common use for displaying Hispanic or Latino population counts. In this dataset, following the approach taken by the Census Bureau, a person who identifies as Hispanic or Latino will also be counted in the race category with which they identify. However, again following the Census Bureau data, there is also a column for White Not Hispanic or Latino. Code can be found here: https://github.com/Chicago/5-Year-ACS-Survey-Data Community Area Shapefile: https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6 Census Area Python Package Documentation: https://census-area.readthedocs.io/en/latest/index.html

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

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

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Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

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Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

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Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

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.

Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file.

All previously released versions of this data are available at the DCP Website: BYTES of the BIG APPLE.

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. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2010 Census.

U.S. Census Populated Place Areas represents the 2020 U.S. Census populated place areas of the United States that include incorporated places, cities, and census designated places identified by the U.S. Census Bureau.This layer is updated annually. The geography is sourced from U.S. Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2020 total population from the U.S. Census Public Law 94 data. The Population Class field values represent population ranges as follows:Population from 0 - 249Population from 250 - 499Population from 500 - 999Population from 1,000 - 2,499Population from 2,500 - 9,999Population from 10,000 - 49,999Population from 50,000 - 99,999Population from 100,000 - 249,999Population from 250,000 - 499,999Population 500,000 and overThis ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

About this dataset

Context

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

Content

The dataset constitues the following three datasets

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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