Large Dallas city council district 13 detailed map. The information represented are (total population, total district area, trail milage, park acreage, and household income.)

About this dataset

Context

The dataset tabulates the District of Columbia population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of District of Columbia. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 460,903 (68.58% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the District of Columbia population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in District of Columbia is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the District of Columbia is shown in the following column.

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 District of Columbia Population by Age. You can refer the same here

Population by Ethnicity by Community College District from the Series 13 Regional Growth Forecast

US Census American Community Survey (ACS) 2013, 5-year estimates of the key social characteristics of Congressional Districts (113th US Congress) geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2013 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

This data collection is a component of Summary Tape File (STF) 3, which consists of four sets of computer-readable data file 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, ethnicity, labor force status, and number of children, as well as details on occupation and income. Housing items include size and condition of the housing unit as well as information on value, age, water, sewage and heating, vehicles, and monthly owner costs. Each dataset provides different geographic coverage. STF 3D provides summaries for state or state equivalent, congressional district (as constituted for the 98th Congress), county or county equivalent, places of 10,000 or more people, and minor civil division/census county division. There are 51 separate files, one for each state and the District of Columbia. 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)

US Census American Community Survey (ACS) 2016, 5-year estimates of the key social characteristics of Congressional Districts (115th US Congress) geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2016 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census American Community Survey (ACS) 2018, 5-year estimates of the key social characteristics of Congressional Districts (116th US Congress) geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2018 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Population: Residents: Age 10 to 13 Years data was reported at 11,554.000 Person th in 2023. This records a decrease from the previous number of 11,588.000 Person th for 2022. Population: Residents: Age 10 to 13 Years data is updated yearly, averaging 12,117.500 Person th from Dec 2012 (Median) to 2023, with 12 observations. The data reached an all-time high of 13,321.000 Person th in 2012 and a record low of 11,554.000 Person th in 2023. Population: Residents: Age 10 to 13 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.GAA003: Population: Residents: by Region and Age.

Population: Residents: Northeast: Pernambuco: Metropolitan Region of Recife: Age 10 to 13 data was reported at 206.000 Person th in 2023. This records an increase from the previous number of 195.000 Person th for 2022. Population: Residents: Northeast: Pernambuco: Metropolitan Region of Recife: Age 10 to 13 data is updated yearly, averaging 215.500 Person th from Dec 2012 (Median) to 2023, with 12 observations. The data reached an all-time high of 271.000 Person th in 2012 and a record low of 179.000 Person th in 2020. Population: Residents: Northeast: Pernambuco: Metropolitan Region of Recife: Age 10 to 13 data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA018: Population: by States and Age: Northeast: Pernambuco.

These data were developed by the Research & Analytics Group 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 2018-2022 Data Manifest. 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:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (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)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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

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

These data were developed by the Research & Analytics Group 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 2018-2022 Data Manifest. 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:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (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)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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

Brazil Population: Non Literate: Central West: 10 Years to 14 Years: 13 Years data was reported at 3,671.000 Person in 2010. This records a decrease from the previous number of 4,838.000 Person for 2000. Brazil Population: Non Literate: Central West: 10 Years to 14 Years: 13 Years data is updated yearly, averaging 4,838.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 14,993.000 Person in 1991 and a record low of 3,671.000 Person in 2010. Brazil Population: Non Literate: Central West: 10 Years to 14 Years: 13 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD058: Population: Non Literate: by Region.

Using data from the 2009-13 ACS 5 Year Estimates at the Census Designated Place level (CDP), calculate total percentage of minority population and total percentage of households in poverty for each CDPAlso using census tract data from the 2009-13 ACS 5 Year Estimates, tabulate percentage of minority population and total percentage of households in poverty for each City of Los Angeles Community Planning Area (CPAs)Intersect CPAs with Census Tracts and tabulate new totals for partial CPA/Census Tracts based on spatial interpolationSum total Poverty, households, minority, and population values for each CPAMerge CDPs and City of Los Angeles Community Planning Areas to create a single “Place” file for the entire SCAG region. Remove the City of Los Angeles CDP from layer. Tabulate % of households in poverty and % of minority population for each “Place”Using ranked sorting, select the places that are in the upper third in the SCAG region for both % of households in poverty (x > 0.169156) and% minority (x > 0.768549)Identify those places and export to new shapefile – “Communities_of_Concern”Union “Communities_of_Concern” shapefile with Tier2 TAZ file and tabulate % of each tract that falls in “Communities_of_Concern”Calculate total square meters in Tier2 TAZ shapefileUnion shapefile with “Communities_of_Concern”Tabulate new square meters in Tier 2 TAZ shapefileExport attribute table to DBFLoad DBF in excel and use pivot tables to tabulate total acreage by TAZ only for tracts that intersect with “Communities_of_Concern”. Create new DBF with results and load into ArcMapJoin new DBF with Tier2 TAZ shapefile and calculate % of TAZ that falls in “Communities_of_Concern” only for the records that join. All other TAZs remain 0%, if they do not intersect.

These data were developed by the Research & Analytics Group 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 2018-2022 Data Manifest. 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:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (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)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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

This data layer produced by the National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program provides a geographic locale framework that classifies all U.S. territory into twelve categories ranging from Large Cities to Remote Rural areas. NCES uses this framework to describe the type of geographic area where schools and school districts are located. The criteria for these classifications are defined by NCES and rely on standard geographic areas developed and maintained by the U.S. Census Bureau. The NCES Locale boundaries are based on geographic areas represented in Census TIGER/Line. For more information about the NCES locale framework, and to download the data, see: https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries. The classifications include:City - Large (11): Territory inside an Urban Area with a population of 50,000 or more and inside a Principal City with population of 250,000 or more.City - Midsize (12): Territory inside an Urban Area with a population of 50,000 or more and inside a Principal City with population less than 250,000 and greater than or equal to 100,000.City - Small (13): Territory inside an Urban Area with a population of 50,000 or more and inside a Principal City with population less than 100,000.Suburb – Large (21): Territory outside a Principal City and inside an Urban Area with population of 250,000 or more.Suburb - Midsize (22): Territory outside a Principal City and inside an Urban Area with population less than 250,000 and greater than or equal to 100,000.Suburb - Small (23): Territory outside a Principal City and inside an Urban Area with population less than 100,000. Town - Fringe (31): Territory inside an Urban Area with a population less than 50,000 that is less than or equal to 10 miles from an Urban Area with a population of 50,000 or more.Town - Distant (32): Territory inside an Urban Area with a population less than 50,000 that is more than 10 miles and less than or equal to 35 miles from an Urban Area with a population of 50,000 or more.Town - Remote (33): Territory inside an Urban Area with a population less than 50,000 that is more than 35 miles of an Urban Area with a population of 50,000 or more.Rural - Fringe (41): Census-defined rural territory that is less than or equal to 5 miles from an Urban Area of 50,000 or more, as well as rural territory that is less than or equal to 2.5 miles from an Urban Area with a population less than 50,000.Rural - Distant (42): Census-defined rural territory that is more than 5 miles but less than or equal to 25 miles from an Urban Area with a population of 50,000 or more, as well as rural territory that is more than 2.5 miles but less than or equal to 10 miles from an Urban Area with a population less than 50,000.Rural - Remote (43): Census-defined rural territory that is more than 25 miles from an Urban Area with a population of 50,000 or more and is also more than 10 miles from an Urban Area with a population less than 50,000.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

US Census American Community Survey (ACS) 2020, 5-year estimates of the key social characteristics of Congressional Districts (116th US Congress) geographic level in Orange County, California. The data contains 500 fields for the variable groups S01: Households by type (universe: total households, table X11, 17 fields); S02: Relationship (universe: population in households, table X9, 19 fields); S03: Marital status (universe: population 15 years and over, table X12, 13 fields); S04: Fertility (universe: women 15-50 years who had birth in the past 12 months, table X13, 11 fields); S05: Grandparents (universe: grandparents living or responsible for own grandchildren under 18 years, table X10, 18 fields); S06: School enrollment (universe: population 3 years old and over enrolled in school, table X14, 17 fields); S07: Educational attainment (universe: population 25 years and over, table X15, 25 fields); S08: Veteran status (universe: civilian population 18 years and over, table X21, 2 fields); S09: Disability status and type by sex and age (universe: total civilian non-institutionalized population, table X18, 77 fields); S10: Disability status by age and health insurance coverage (universe: civilian non-institutionalized population, table X18, 16 fields); S11: Residence 1 year ago (universe: population 1 year and over, table X7, 6 fields); S12: Place of birth (universe: total population, table X5, 27 fields); S13: Citizenship status by nativity in the US (universe: total population, table X5, 6 fields); S14: Year of entry (universe: population born outside the US, table X5, 21 fields); S15: World region of birth of foreign born population (universe: foreign born population, excluding population born at sea, table X5, 25 fields); S16: Language spoken in households (universe: total households, table X16, 6 fields); S17: Language spoken at home (universe: population 5 years and over, table X16, 67 fields); S18: Ancestry (universe: total population reporting ancestry, table X4, 114 fields), and; S19: Computers and internet use (universe: total population in households and total households, table X28, 13 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

Population by High School District from the Series 13 Regional Growth Forecast

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