U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. The units of geography used for the 2010 Census maps displayed here are the Zip Code Tabulation Area (ZCTA). ZCTAs are statistical geographic areas produced by the Census Bureau by aggregating census blocks to create generalized areas closely resembling the U.S. Postal Service's postal zip codes. The data collected on the short form survey are general demographic characteristics such as age - race - ethnicity - household relationship - housing vacancy and tenure (owner/renter).Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Demographics/MD_CensusData/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. The units of geography used for the 2010 Census maps displayed here are the Census tracts. 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. Census tract boundaries generally follow visible and identifiable features. 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. The data collected on the short form survey are general demographic characteristics such as age - race - ethnicity - household relationship - housing vacancy and tenure (owner/renter).Feature Service Link:http://geodata.md.gov/imap/rest/services/Demographics/MD_CensusData/FeatureServer/0 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
The Decennial Census provides population estimates and demographic information on residents of the United States.
The Census Summary Files contain detailed tables on responses to the decennial census. Data tables in Summary File 1 provide information on population and housing characteristics, including cross-tabulations of age, sex, households, families, relationship to householder, housing units, detailed race and Hispanic or Latino origin groups, and group quarters for the total population. Summary File 2 contains data tables on population and housing characteristics as reported by housing unit.
Researchers at NYU Langone Health can find guidance for the use and analysis of Census Bureau data on the Population Health Data Hub (listed under "Other Resources"), which is accessible only through the intranet portal with a valid Kerberos ID (KID).
The 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.
Layers and tables include the following: Tahoe BlockGroup 2022 TDC Values, Tahoe BlockGroup 2021 TDC Values, Tahoe Tract 2020 Values, Tahoe Tract 2010 Values, Tahoe Census Geography, Tahoe Census Unified School District, Tahoe Census Zip, Tahoe Census Community 2010 to 2020 Change, Community Priority Zones, Demographic Summaries, Census Data, School Enrollment, Visitation Numbers, Water Use Statistics.
https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain
This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about
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 Block Groups In Boston
Boston Neighborhood Boundaries Approximated By 2020 Census Tracts
https://www.icpsr.umich.edu/web/ICPSR/studies/21742/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/21742/terms
This data collection contains information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), urban/rural status, household relationship, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry into the United States, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, occupation and industry, class of worker, income, and poverty status. Housing items include vacancy status, tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, household size, occupants per room, number of units in structure, year structure was built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent. With subject content identical to that provided in Summary File 3, the information is presented in 813 tables that are tabulated for every geographic unit represented in the data. There is one variable per table cell, plus additional variables with geographic information. The data cover more than a dozen geographic levels of observation (known as "summary levels" in the Census Bureau's nomenclature) based on the 108th Congressional Districts, e.g., the 108th Congressional Districts, themselves, Census tracts within the 108th Congressional Districts, and county subdivisions within the 108th Congressional Districts. There are 77 data files for each state, the District of Columbia, and Puerto Rico. The collection is supplied in 54 ZIP archives. There is a separate ZIP file for each state, the District of Columbia, and Puerto Rico, and for the convenience of those who need all of the data, a separate ZIP archive with all 4,004 data files. The codebook and other documentation are located in the last ZIP archive.
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.
This data collection is a component of Summary Tape File (STF) 3, which consists of four sets of data files containing detailed tabulations of the nation's population and housing characteristics produced from the 1980 Census. The STF 3 files contain sample data inflated to represent the total United States population. The files also contain 100-percent counts and unweighted sample counts of persons and housing units. All files in the STF 3 series are identical, containing 321 substantive data variables organized in the form of 150 "tables," as well as standard geographic identification variables. Population items tabulated for each person include demographic data and information on schooling, Spanish origin, language spoken at home and ability to speak English, labor force status in 1979, residency in 1975, number of children ever born, means of transportation to work, current occupation, industry, and 1979 details on occupation, hours worked, and income. Housing items include size and condition of the housing unit as well as information on value, age, water, sewage and heating, number of vehicles, and monthly owner costs (e.g., sum of payments for real estate taxes, property insurance, utilities, and regular mortgage payments). Selected aggregates and medians are also provided. Each dataset in STF 3 provides different geographic coverage. Summary Tape File 3B provides summaries for each 5-digit ZIP-code area within a state, and for 5-digit ZIP-code areas within states that were contained within Standard Metropolitan Statistical Areas (SMSAs), portions of SMSAs, or within counties, county portions, or county equivalents. All persons and housing units in the United States were sampled. Population and housing items include household relationship, sex, race, age, marital status, Hispanic origin, number of units at address, complete plumbing facilities, number of rooms, whether owned or rented, vacancy status, and value for noncondominiums. The Census Bureau's machine-readable data dictionary for STF 3 is also available through CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: CENSUS SOFTWARE PACKAGE (CENSPAC) VERSION 3.2 WITH STF4 DATA DICTIONARIES (ICPSR 7789), the software package designed specifically by the Census Bureau for use with the 1980 Census data files. (Source: downloaded from ICPSR 7/13/10)
Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR08318.v1. We highly recommend using the ICPSR version as they made this dataset available in multiple data formats.
Historical population counts from the US Census Bureau census counts of 1970, 1980, 1990, 2000, 2010, and 2020. Certified population estimates prepared by the State Demographer beginning 1981. Population density for selected years. The certified population estimates are as estimated for the given vintage year and may be different from the revised estimates and the intercensal (smoothed) estimates also produced by the State Demographer. Census counts for April 1 of given year, population estimates for July 1 of given year.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Babylon town 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 Babylon town. The dataset can be utilized to understand the population distribution of Babylon town by age. For example, using this dataset, we can identify the largest age group in Babylon town.
Key observations
The largest age group in Babylon Town, New York was for the group of age 55 to 59 years years with a population of 17,508 (8.04%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Babylon Town, New York was the 85 years and over years with a population of 4,471 (2.05%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Babylon town Population by Age. You can refer the same here
Residential addresses actively receiving mail by ZIP code and parish for the New Orleans metro area and other selected parishes.
Monthly data for July 2005 and August 2006-Present month for Orleans, Jefferson, Plaquemines, St. Bernard, St. Charles, St. John the Baptist, and St. Tammany.
Monthly data for January 2016-Present month for Ascensions, East Baton Rouge, and Livingston.
Monthly data for August 2017-Present month for St. James.
Note: Trends in residential addresses actively receiving mail can serve as a useful indicator of the rate of change in occupied housing units. However, active residential addresses are not the same as occupied housing units. For the decennial census, the Census Bureau defines an occupied housing unit as the usual place of residence for an individual or group of individuals on Census Day (April 1). The U.S. Postal Service (USPS) defines a residential address as actively receiving mail if mail has been picked up within the previous 90 days. The USPS counts of active residential addresses may be higher than Census Bureau counts of occupied households in cities, and lower in rural areas. In cities, the USPS counts may be higher because they include each room in group quarters such as college dormitories, military quarters, and single room occupancies (residences for formerly homeless persons). The Census Bureau does not include group quarters in its count of occupied housing units. In rural areas, mail is frequently delivered to rural route boxes and P.O. boxes rather than street addresses, and thus may lead to an undercount compared to Census Bureau occupied housing units.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Primrose 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 Primrose. The dataset can be utilized to understand the population distribution of Primrose by age. For example, using this dataset, we can identify the largest age group in Primrose.
Key observations
The largest age group in Primrose, NE was for the group of age 50 to 54 years years with a population of 19 (27.14%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Primrose, NE was the 20 to 24 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Primrose Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Aransas Pass, TX population pyramid, which represents the Aransas Pass population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Aransas Pass Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Texas County population by age. The dataset can be utilized to understand the age distribution and demographics of Texas County.
The dataset constitues the following three datasets
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.
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/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Philadelphia County 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 Philadelphia County. The dataset can be utilized to understand the population distribution of Philadelphia County by age. For example, using this dataset, we can identify the largest age group in Philadelphia County.
Key observations
The largest age group in Philadelphia County, PA was for the group of age 30 to 34 years years with a population of 151,121 (9.55%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Philadelphia County, PA was the 80 to 84 years years with a population of 25,123 (1.59%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Philadelphia County Population by Age. You can refer the same here
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. The units of geography used for the 2010 Census maps displayed here are the Zip Code Tabulation Area (ZCTA). ZCTAs are statistical geographic areas produced by the Census Bureau by aggregating census blocks to create generalized areas closely resembling the U.S. Postal Service's postal zip codes. The data collected on the short form survey are general demographic characteristics such as age - race - ethnicity - household relationship - housing vacancy and tenure (owner/renter).Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Demographics/MD_CensusData/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.