In 2023, about **** percent of the population in Georgia was between 25 and 34 years old. A further **** percent of people in Georgia were between the ages of 35 and 44 years old in that year.

About this dataset

Context

The dataset tabulates the Georgia 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 Georgia. The dataset can be utilized to understand the population distribution of Georgia by age. For example, using this dataset, we can identify the largest age group in Georgia.

Key observations

The largest age group in Georgia was for the group of age 15 to 19 years years with a population of 761,260 (7.03%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Georgia was the 85 years and over years with a population of 152,366 (1.41%). 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 groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset tabulates the population of Georgia by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Georgia. The dataset can be utilized to understand the population distribution of Georgia by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Georgia. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Georgia.

Key observations

Largest age group (population): Male # 15-19 years (388,242) | Female # 30-34 years (382,604). 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 groups:

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Georgia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Georgia is shown in the following column.
• Population (Female): The female population in the Georgia is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Georgia for each age group.

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 Georgia Population by Gender. You can refer the same here

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2010-2014 to show population age data, by city for the state of Georgia.

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. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2010-2014). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

NAME = Name of city or municipality

Acres = Area in acres

Sq_Miles = Area in square miles

County20 = Within ARC 20-county region

County10 = Within ARC 10-county region

Total_Population = Total Population

Under_18_years = Under 18 years

Pct_Under_18_years = % Under 18 years

18_29_years = 18-29 years

Pct_18_29_years = % 18-29 years

30_44_years = 30-44 years

Pct_30_44_years = % 30-44 years

45_64_years = 45-64 years

Pct_45_64_years = % 45-64 years

65_years_and_over = 65 years and over

Pct_65_years_and_over = % 65 years and over

Median_Age = Median Age

last_edited_date = Last date feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2010-2014

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2010-2014, to show various demographic data by city in the state of Georgia (including the following categories: total population, household composition, grandparents, school enrollment, educational attainment, disability, foreign born status, linguistic isolation, unemployment, commuting mode, occupation, income, health insurance, poverty, housing characteristics, vehicle availability, housing values, and housing affordability).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. The Census Bureau also calculates a corresponding margin of error (MOE) for ACS measures (although margins of error are not included in this dataset).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 2010-2014). 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, refer to Census Bureau documentation.Base Attributes:NAME = Name of city or municipalityAcres = Area in acresSq_Miles = Area in square milesCounty20 = Within ARC 20-county regionCounty10 = Within ARC 10-county regionAttributes from ACS:Workers_16_years_and_over= Number, Workers, 16 years and overCar_Truck_or_Van_drove_alone= Number, Car, truck, or van – drove alonePct_Car_Truck_Van_drove_alone= Percent, Car, truck, or van – drove aloneCar_truck_or_van_carpooled= Number, Car, truck, or van – carpooledPct_Car_Truck_Van_carpooled= Percent, Car, truck, or van – carpooledPublic_Transport_excluding_Taxi= Number, Public transportation (excluding taxicab)Pct_Public_Transp_exclude_Taxi= Percent, Public transportation (excluding taxicab)Worked_at_home= Number, Worked at homePct_Worked_at_home= Percent, Worked at homeMean_Travel_Time_to_Work_min= Mean travel time to work (minutes)- - - - - -Civilian_nonInstitutional_Pop= Total Civilian Noninstitutionalized PopulationCiv_nonInstitution_Pop_wDisabil= #, Civilian Noninstitutionalized Population With a disabilityPct_Civ_nonInstitut_Pop_wDisab= %, Civilian Noninstitutionalized Population With a disabilityCiv_nonInstitut_Pop_under_18yrs= #, Civilian Noninstitutionalized Population Under 18 yearsCiv_nonInst_under18_wDisab= #, Civilian Noninstitutionalized Under 18 years With a disabilityPct_Civ_nonInst_under18_wDisab= %, Civilian Noninstitutionalized Under 18 years With a disabilityCiv_nonInst_Pop_18_to_64= #, Civilian Noninstitutionalized Population 18 to 64 yearsCiv_nonInst_18_to_64_wDisab= #, Civilian Noninstitutionalized 18 to 64 years With a disabilityPct_Civ_nonInst_18to64_wDisab= %, Civilian Noninstitutionalized 18 to 64 years With a disabilityCiv_nonInst_Pop_65years_up= #, Civilian Noninstitutionalized Population 65 years and overCiv_nonInst_65up_wDisab= #, Civilian Noninstitutionalized 65 years and over With a disabilityPct_Civ_nonInst_65up_wDisab= %, Civilian Noninstitutionalized 65 years and over With a disability- - - - - -Population_25_years_and_over= #, Population 25 years and overLess_than_HS_or_GED= #, Less than HS or GEDPercent_Less_than_HS_or_GED= %, Less than HS or GEDBA_or_Higher= #, BA or HigherPercent_BA_or_Higher= %, BA or Higher- - - - - -US_Native= #, U.S. NativePercent_US_Native= %, U.S. NativeUSnative_Born_in_US= #, U.S. Native, Born in the United StatesPct_USnative_Born_US= %, U.S. Native, Born in the United StatesUSnative_Born_State_Resid= #, U.S. Native, Born in State of ResidencePct_USnative_Born_State_Resid= %, U.S. Native, Born in State of ResidenceUS_Native_Born_Diff_State= #, U.S. Native, Born in Different StatePct_US_Natv_Born_inDiff_State= %, U.S. Native, Born in Different StateForeign_Born= #, Foreign BornPercent_Foreign_Born= %, Foreign BornForBorn_Nat_UScitizen= #, Foreign Born, Naturalized U.S. CitizenPct_ForBorn_Nat_UScitizen= %, Foreign Born, Naturalized U.S. CitizenForeignBorn_notUS_Citizen= #, Foreign Born, Not a U.S. CitizenPct_ForBorn_notUS_Citizen= %, Foreign Born, Not a U.S. Citizen- - - - - -GParents_Liv_wOwn_GChild_und18= #, Grandparents living with own grandchildren under 18 yearsGParents_RespFor_Gchildren= #, Grandparents Responsible for grandchildrenPct_GPar_RespFor_Gchildren= %, Grandparents Responsible for grandchildren- - - - - -Pop_wHealth_Insurance= #, Civilian noninstitutionalized population with health insurance coveragePct_Pop_wHealth_Ins= %, Civilian noninstitutionalized population with health insurance coveragePop_wPriv_Health_Ins= #, Civilian noninstitutionalized population with private health insurancePct_Pop_wPriv_Health_Ins= %, Civilian noninstitutionalized population with private health insurancePopulation_with_public_coverage= #, Civilian noninstitutionalized population with public coveragePct_Pop_with_public_coverage= %, Civilian noninstitutionalized population with public coveragePop_wNo_Health_Ins= #, Civilian noninstitutionalized population with no health insurance coveragePct_Pop_wNo_Health_Ins= %, Civilian noninstitutionalized population with no health insurance coveragePop_u18_wNo_Health_Ins= #, Civilian Noninstitutionalized Population Under 18 years with no health insurancePct_Pop_u18_wNo_Health_Ins= %, Civilian Noninstitutionalized Population Under 18 years with no health insurancePop_18to64_Employed= #, Civilian noninstitutionalized ages 18 to 64, employedPop_18to64_Empl_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePct_Pop_18to64_Emp_wNo_Hlth_Ins= %, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePop_18to64_Unemployed= #, Civilian noninstitutionalized ages 18 to 64, unemployedPop_18to64_Unemp_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePct_Pop_18to64_Unemp_No_HlthIns= %, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePop_18to64_Not_in_Labor_Force= #, Civilian noninstitutionalized ages 18 to 64, not in labor forcePop_18to64_Not_LabFor_NoHlthIns= #, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurancePctPop_18to64_NotLFor_NoHlthIns= %, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurance- - - - - -HousUnits_MonthOwnerCosts_toInc= #, Housing units for which Selected Monthly Owner Costs as % of income are computedSel_Mo_Own_Costs_30pct_of_Incom= #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomePct_Sel_Mo_Own_Costs_30pct_Inc= %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomeHousUnits_Compute_RentPctIncome= #, Housing units for which Gross rent as a percentage of income is computedRent_Pct_of_Inc_More30Pct= #, Gross rent as a percentage of household income (GRAPI) is 30% or morePctRent_PctIncome_More30Pct= %, Gross rent as a percentage of household income (GRAPI) is 30% or moreHousUnits_OwnRent_Compute= #, Housing units for which SMOCAPI or GRAPI are computedHousCosts_Units_30pctMore_Inc= #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household incomePctHousCost_30pctMore_Income= %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income- - - - - -Total_housing_units = Housing Characteristics: Total housing unitsOccupied_housing_units= #, Occupied housing unitsPercent_Occupied_housing_units= %, Occupied housing unitsVacant_housing_units= #, Vacant housing unitsPercent_Vacant_housing_units= %, Vacant housing unitsHomeowner_vacancy_rate= Homeowner vacancy rateRental_vacancy_rate= Rental vacancy rateOne_unit_detatched_housing_unit= #, 1-unit detached housing unitsPercent_1Unit_Detached= %, 1-unit detached housing unitsHousing_units_built_since_2000= #, Housing units built since 2000Pct_Units_Built_Since_2000= %, Housing units built since 2000Units_Built_1980_to_1999= #, Housing units built 1980 to 1999Pct_Units_Built_1980_to_1999= %, Housing units built 1980 to 1999Units_Built_1979_or_Earlier= #, Housing units built 1979 or earlierPct_Units_Built_1979_or_Earlier= %, Housing units built 1979 or earlier- - - - - -Total_Housing_Units_Val = Housing Value: Total Housing UnitsOccupied_Housing_Units_Val = Housing Value: Occupied Housing UnitsOwnOcc_units_valued_less_100k= #, Owner occupied housing units valued less than $100,000Pct_OwnOcc_units_val_less_100k= %, Owner occupied housing units valued less than $100,000OwnOcc_units_valued_100k_300k= #, Owner occupied housing units valued $100,000-$299,999Pct_OwnOcc_units_val_100k_300k= %, Owner occupied housing units valued $100,000-$299,999OwnOcc_units_valued_300k_more= #, Owner occupied housing units valued $300,000 or morePct_OwnOcc_units_val_300k_more= %, Owner occupied housing units valued $300,000 or moreMedian_value_own_occ_units= Median value, owner occupied housing units- - - - - -Income_Total_households = Income: Total householdsHousehold_inc_less_35k= #, Household income less than $35,000Pct_Household_inc_less_35k= %, Household income less than $35,000Household_inc_35k_75k= #, Household income $35,000 to $74,999Pct_Household_inc_35k_75k= %, Household income $35,000 to $74,999Household_inc_75k_200k= #, Household income $75,000 to $200,000Pct_Household_inc_75k_200k= %, Household income $75,000 to $200,000Household_inc_200k_more= #,

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census, to show various demographic and housing data by state House district in the state of Georgia (including the following categories: population, households, housing characteristics, age, and race/ethnicity), for 2000 and 2010.- - - - - -Base Attributes:DISTRICT = GA House DistrictPOPULATION = District Population (2010 Census)Name = GA House District NameTotal_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)profile_url = Web address of district profile - - - - - -Attributes from Census Bureau:Family_households = Family households, 2010Pct_Family_households = % Family households, 2010Family_HH_wOwnChild_un18yr = Family households with own children under 18 years, 2010Pct_Family_HH_wOwnChild_un18yr = % Family households with own children under 18 years, 2010Husband_wife_families = Husband-wife families, 2010Pct_Husband_wife_families = % Husband-wife families, 2010Hus_wife_families_wChild = Husband-wife families with children, 2010Pct_hus_wife_families_wChild = % Husband-wife families with children, 2010Single_parent_households = Single parent households, 2010Pct_Single_parent_households = % Single parent households, 2010Nonfamily_households = Nonfamily households, 2010Pct_Nonfamily_households = % Nonfamily households, 2010HH_with_individuals_un18yr = Households with individuals under 18 years, 2010Pct_HH_with_individuals_un18yr = % Households with individuals under 18 years, 2010- - - - - -Total_housing_units = Total housing units, 2010Occupied_housing_units = #, Occupied housing units, 2010Percent_Occupied_housing_units = %, Occupied housing units, 2010Vacant_housing_units = #, Vacant housing units, 2010Percent_Vacant_housing_units = %, Vacant housing units, 2010Owner_occupied_housing_units = #, Owner occupied housing units, 2010Pct_Owner_Occ_HousUnits = %, Owner occupied housing units, 2010Renter_occupied_housing_units = #, Renter occupied housing units, 2010Pct_Renter_Occ_Units = %, Renter occupied housing units, 2010- - - - - -Pop_under_age_19_2010 = Population under age 19, 2010Pop_ages_20_34_2010 = Population ages 20-34, 2010Pop_ages_35_44_2010 = Population ages 35-44, 2010Pop_ages_45_64_2010 = Population ages 45-64, 2010Pop_ages_65_over_2010 = Population ages 65 and over, 2010Pct_Pop_under_age_19_2010 = % Population under age 19, 2010Pct_Pop_ages_20_34_2010 = % Population ages 20-34, 2010Pct_Pop_ages_35_44_2010 = % Population ages 35-44, 2010Pct_Pop_ages_45_64_2010 = % Population ages 45-64, 2010Pct_Pop_ages_65_over_2010 = % Population ages 65 and over, 2010Pop_under_age_19_2000 = Population under age 19, 2000Pop_ages_20_34_2000 = Population ages 20-34, 2000Pop_ages_35_44_2000 = Population ages 35-44, 2000Pop_ages_45_64_2000 = Population ages 45-64, 2000Pop_ages_65_over_2000 = Population ages 65 and over, 2000Pct_Pop_under_age_19_2000 = % Population under age 19, 2000Pct_Pop_ages_20_34_2000 = % Population ages 20-34, 2000Pct_Pop_ages_35_44_2000 = % Population ages 35-44, 2000Pct_Pop_ages_45_64_2000 = % Population ages 45-64, 2000Pct_Pop_ages_65_over_2000 = % Population ages 65 and over, 2000Chg_Pop_Under_19 = Change in Population Under 19 (2000-2010)Chg_Pct_Pop_Under_19 = Change in Percent Population Under 19 (2000-2010)Chg_Pct_pop_ages_20_34 = Change in Percent population ages 20-34 (2000-2010)Chg_Pct_pop_ages_20_34 = Change in Percent population ages 20-34 (2000-2010)Chg_pop_ages_35_44 = Change in population ages 35-44 (2000-2010)Chg_Pct_pop_ages_35_44 = Change in Percent population ages 35-44 (2000-2010)Chg_pop_ages_45_64 = Change in population ages 45-64 (2000-2010)Chg_Pct_pop_ages_45_64 = Change in Percent population ages 45-64 (2000-2010)Chg_pop_ages_65_over = Change in population ages 65 and over (2000-2010)Chg_Pct_pop_ages_65_over = Change in Percent population ages 65 and over (2000-2010)- - - - - -Non_Hisp_White_2010 = Non-Hispanic White, 2010Non_Hisp_Black_2010 = Non-Hispanic Black, 2010Non_Hisp_AsianPI_2010 = Non-Hispanic Asian/Pacific Islander, 2010Non_Hisp_Other_Biracial_2010 = Non-Hispanic Other Races (includes biracial), 2010Hisp_All_races_2010 = Hispanic, All races, 2010Pct_Non_Hisp_White_2010 = % Non-Hispanic White, 2010Pct_Non_Hisp_Black_2010 = % Non-Hispanic Black, 2010Pct_Non_Hisp_AsianPI_2010 = % Non-Hispanic Asian/Pacific Islander, 2010Pct_Non_Hisp_Other_Bi_2010 = % Non-Hispanic Other Races (includes biracial), 2010Pct_Hisp_All_races_2010 = % Hispanic, All races, 2010Non_Hisp_White_2000 = Non-Hispanic White, 2000Non_Hisp_Black_2000 = Non-Hispanic Black, 2000Non_Hisp_AsianPI_2000 = Non-Hispanic Asian/Pacific Islander, 2000Non_Hisp_Other_Biracial_2000 = Non-Hispanic Other Races (includes biracial), 2000Hisp_All_races_2000 = Hispanic, All races, 2000Pct_Non_Hisp_White_2000 = % Non-Hispanic White, 2000Pct_Non_Hisp_Black_2000 = % Non-Hispanic Black, 2000Pct_Non_Hisp_AsianPI_2000 = % Non-Hispanic Asian/Pacific Islander, 2000Pct_Non_Hisp_Other_Bi_2000 = % Non-Hispanic Other Races (includes biracial), 2000Pct_Hisp_All_races_2000 = % Hispanic, All races, 2000Chg_Non_Hisp_White = Change in Non-Hispanic White Population (2000-2010)Chg_Non_Hisp_Black = Change in Non-Hispanic Black Population (2000-2010)Chg_Non_Hisp_AsianPI = Change in Non-Hispanic Asian/Pacific Islander Population (2000-2010)Chg_Non_Hisp_Other_Biracial = Change in Non-Hispanic Other (includes biracial) Population (2000-2010)Chg_Hisp_Population = Change in Hispanic Population (2000-2010)Chg_Pct_Non_Hisp_White = Change in Percent Non-Hispanic White (2000-2010)Chg_Pct_Non_Hisp_Black = Change in Percent Non-Hispanic Black (2000-2010)Chg_Pct_Non_Hisp_AsianPI = Change in Percent Non-Hispanic Asian/Pacific Islander (2000-2010)Chg_Pct_Non_Hisp_Other_Biracial = Change in Percent Non-Hispanic Other (includes biracial) (2000-2010)Chg_Pct_Hisp_Population = Change in Percent Hispanic Population (2000-2010)- - - - - -Population_2010 = Population, 2010Population_2000 = Population, 2000Population_Change_2000_2010 = Population Change, 2000-2010Pct_Population_Change_2000_2010 = % Population Change, 2000-2010- - - - - -Source: U.S. Census Bureau, Atlanta Regional CommissionDates: 2000, 2010For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

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 2014-2018). 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 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.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

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% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show numbers and percentages for voting age population by Georgia House in the Atlanta region.

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 2013-2017). 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.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

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)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

VotingAgeCitizen_e

# Citizen, 18 and over population, 2017

VotingAgeCitizen_m

# Citizen, 18 and over population, 2017 (MOE)

VotingAgeCitizenMale_e

# Male citizen, 18 and over population, 2017

VotingAgeCitizenMale_m

# Male citizen, 18 and over population, 2017 (MOE)

pVotingAgeCitizenMale_e

% Male citizen, 18 and over population, 2017

pVotingAgeCitizenMale_m

% Male citizen, 18 and over population, 2017 (MOE)

VotingAgeCitizenFemale_e

# Female citizen, 18 and over population, 2017

VotingAgeCitizenFemale_m

# Female citizen, 18 and over population, 2017 (MOE)

pVotingAgeCitizenFemale_e

% Female citizen, 18 and over population, 2017

pVotingAgeCitizenFemale_m

% Female citizen, 18 and over population, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

1 computer laser optical disc ; 4 3/4 in.CDRM 454600Hierarchical file structure.ISO 9660 format.Provides census data designed and formatted for use in legislative redistricting. Census counts, for areas as small as blocks, census tracts, and voting districts, include totals for population, race groups, persons of Hispanic origin, population 18 years and over, and housing units.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2011-2015 to show poverty status and rates, by age category, by state Senate district for the state of Georgia.

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. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

DISTRICT = GA Senate District

POPULATION = District Population (2010 Census)

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

last_edited_date = Last date feature was edited by ARC

profile_url = Web address of ARC district profile

Pop_PovertyStatus_Determined = #, Population for whom poverty status is determined

Population_in_poverty = #, Population in poverty

Percent_Population_in_poverty = %, Population in poverty

Pop_under18_PovStatusDetermined = #, Population under 18 years for whom poverty status is determined

Pop_under18_in_Poverty = #, Population under 18 years in poverty

Pct_Pop_under18_in_Poverty = %, Population under 18 years in poverty

Pop_18_64_PovStatus_Determined = #, Population 18 to 64 years for whom poverty status is determined

Pop_18_64_Years_in_Poverty = #, Population 18 to 64 years in poverty

Pct_Pop_18_64_Years_in_Poverty = %, Population 18 to 64 years in poverty

Pop_65older_PovStatusDetermined = #, Population 65 years and over for whom poverty status is determined

Pop_65older_in_Poverty = #, Population 65 years and over in poverty

Pct_Pop_65older_in_Poverty = %, Population 65 years and over in poverty

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2010-2014, to show various demographic data by city in the state of Georgia (including the following categories: total population, household composition, grandparents, school enrollment, educational attainment, disability, foreign born status, linguistic isolation, unemployment, commuting mode, occupation, income, health insurance, poverty, housing characteristics, vehicle availability, housing values, and housing affordability).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. The Census Bureau also calculates a corresponding margin of error (MOE) for ACS measures (although margins of error are not included in this dataset).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 2010-2014). 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, refer to Census Bureau documentation.Base Attributes:NAME = Name of city or municipalityAcres = Area in acresSq_Miles = Area in square milesCounty20 = Within ARC 20-county regionCounty10 = Within ARC 10-county regionAttributes from ACS:Workers_16_years_and_over= Number, Workers, 16 years and overCar_Truck_or_Van_drove_alone= Number, Car, truck, or van – drove alonePct_Car_Truck_Van_drove_alone= Percent, Car, truck, or van – drove aloneCar_truck_or_van_carpooled= Number, Car, truck, or van – carpooledPct_Car_Truck_Van_carpooled= Percent, Car, truck, or van – carpooledPublic_Transport_excluding_Taxi= Number, Public transportation (excluding taxicab)Pct_Public_Transp_exclude_Taxi= Percent, Public transportation (excluding taxicab)Worked_at_home= Number, Worked at homePct_Worked_at_home= Percent, Worked at homeMean_Travel_Time_to_Work_min= Mean travel time to work (minutes)- - - - - -Civilian_nonInstitutional_Pop= Total Civilian Noninstitutionalized PopulationCiv_nonInstitution_Pop_wDisabil= #, Civilian Noninstitutionalized Population With a disabilityPct_Civ_nonInstitut_Pop_wDisab= %, Civilian Noninstitutionalized Population With a disabilityCiv_nonInstitut_Pop_under_18yrs= #, Civilian Noninstitutionalized Population Under 18 yearsCiv_nonInst_under18_wDisab= #, Civilian Noninstitutionalized Under 18 years With a disabilityPct_Civ_nonInst_under18_wDisab= %, Civilian Noninstitutionalized Under 18 years With a disabilityCiv_nonInst_Pop_18_to_64= #, Civilian Noninstitutionalized Population 18 to 64 yearsCiv_nonInst_18_to_64_wDisab= #, Civilian Noninstitutionalized 18 to 64 years With a disabilityPct_Civ_nonInst_18to64_wDisab= %, Civilian Noninstitutionalized 18 to 64 years With a disabilityCiv_nonInst_Pop_65years_up= #, Civilian Noninstitutionalized Population 65 years and overCiv_nonInst_65up_wDisab= #, Civilian Noninstitutionalized 65 years and over With a disabilityPct_Civ_nonInst_65up_wDisab= %, Civilian Noninstitutionalized 65 years and over With a disability- - - - - -Population_25_years_and_over= #, Population 25 years and overLess_than_HS_or_GED= #, Less than HS or GEDPercent_Less_than_HS_or_GED= %, Less than HS or GEDBA_or_Higher= #, BA or HigherPercent_BA_or_Higher= %, BA or Higher- - - - - -US_Native= #, U.S. NativePercent_US_Native= %, U.S. NativeUSnative_Born_in_US= #, U.S. Native, Born in the United StatesPct_USnative_Born_US= %, U.S. Native, Born in the United StatesUSnative_Born_State_Resid= #, U.S. Native, Born in State of ResidencePct_USnative_Born_State_Resid= %, U.S. Native, Born in State of ResidenceUS_Native_Born_Diff_State= #, U.S. Native, Born in Different StatePct_US_Natv_Born_inDiff_State= %, U.S. Native, Born in Different StateForeign_Born= #, Foreign BornPercent_Foreign_Born= %, Foreign BornForBorn_Nat_UScitizen= #, Foreign Born, Naturalized U.S. CitizenPct_ForBorn_Nat_UScitizen= %, Foreign Born, Naturalized U.S. CitizenForeignBorn_notUS_Citizen= #, Foreign Born, Not a U.S. CitizenPct_ForBorn_notUS_Citizen= %, Foreign Born, Not a U.S. Citizen- - - - - -GParents_Liv_wOwn_GChild_und18= #, Grandparents living with own grandchildren under 18 yearsGParents_RespFor_Gchildren= #, Grandparents Responsible for grandchildrenPct_GPar_RespFor_Gchildren= %, Grandparents Responsible for grandchildren- - - - - -Pop_wHealth_Insurance= #, Civilian noninstitutionalized population with health insurance coveragePct_Pop_wHealth_Ins= %, Civilian noninstitutionalized population with health insurance coveragePop_wPriv_Health_Ins= #, Civilian noninstitutionalized population with private health insurancePct_Pop_wPriv_Health_Ins= %, Civilian noninstitutionalized population with private health insurancePopulation_with_public_coverage= #, Civilian noninstitutionalized population with public coveragePct_Pop_with_public_coverage= %, Civilian noninstitutionalized population with public coveragePop_wNo_Health_Ins= #, Civilian noninstitutionalized population with no health insurance coveragePct_Pop_wNo_Health_Ins= %, Civilian noninstitutionalized population with no health insurance coveragePop_u18_wNo_Health_Ins= #, Civilian Noninstitutionalized Population Under 18 years with no health insurancePct_Pop_u18_wNo_Health_Ins= %, Civilian Noninstitutionalized Population Under 18 years with no health insurancePop_18to64_Employed= #, Civilian noninstitutionalized ages 18 to 64, employedPop_18to64_Empl_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePct_Pop_18to64_Emp_wNo_Hlth_Ins= %, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePop_18to64_Unemployed= #, Civilian noninstitutionalized ages 18 to 64, unemployedPop_18to64_Unemp_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePct_Pop_18to64_Unemp_No_HlthIns= %, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePop_18to64_Not_in_Labor_Force= #, Civilian noninstitutionalized ages 18 to 64, not in labor forcePop_18to64_Not_LabFor_NoHlthIns= #, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurancePctPop_18to64_NotLFor_NoHlthIns= %, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurance- - - - - -HousUnits_MonthOwnerCosts_toInc= #, Housing units for which Selected Monthly Owner Costs as % of income are computedSel_Mo_Own_Costs_30pct_of_Incom= #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomePct_Sel_Mo_Own_Costs_30pct_Inc= %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomeHousUnits_Compute_RentPctIncome= #, Housing units for which Gross rent as a percentage of income is computedRent_Pct_of_Inc_More30Pct= #, Gross rent as a percentage of household income (GRAPI) is 30% or morePctRent_PctIncome_More30Pct= %, Gross rent as a percentage of household income (GRAPI) is 30% or moreHousUnits_OwnRent_Compute= #, Housing units for which SMOCAPI or GRAPI are computedHousCosts_Units_30pctMore_Inc= #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household incomePctHousCost_30pctMore_Income= %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income- - - - - -Total_housing_units = Housing Characteristics: Total housing unitsOccupied_housing_units= #, Occupied housing unitsPercent_Occupied_housing_units= %, Occupied housing unitsVacant_housing_units= #, Vacant housing unitsPercent_Vacant_housing_units= %, Vacant housing unitsHomeowner_vacancy_rate= Homeowner vacancy rateRental_vacancy_rate= Rental vacancy rateOne_unit_detatched_housing_unit= #, 1-unit detached housing unitsPercent_1Unit_Detached= %, 1-unit detached housing unitsHousing_units_built_since_2000= #, Housing units built since 2000Pct_Units_Built_Since_2000= %, Housing units built since 2000Units_Built_1980_to_1999= #, Housing units built 1980 to 1999Pct_Units_Built_1980_to_1999= %, Housing units built 1980 to 1999Units_Built_1979_or_Earlier= #, Housing units built 1979 or earlierPct_Units_Built_1979_or_Earlier= %, Housing units built 1979 or earlier- - - - - -Total_Housing_Units_Val = Housing Value: Total Housing UnitsOccupied_Housing_Units_Val = Housing Value: Occupied Housing UnitsOwnOcc_units_valued_less_100k= #, Owner occupied housing units valued less than $100,000Pct_OwnOcc_units_val_less_100k= %, Owner occupied housing units valued less than $100,000OwnOcc_units_valued_100k_300k= #, Owner occupied housing units valued $100,000-$299,999Pct_OwnOcc_units_val_100k_300k= %, Owner occupied housing units valued $100,000-$299,999OwnOcc_units_valued_300k_more= #, Owner occupied housing units valued $300,000 or morePct_OwnOcc_units_val_300k_more= %, Owner occupied housing units valued $300,000 or moreMedian_value_own_occ_units= Median value, owner occupied housing units- - - - - -Income_Total_households = Income: Total householdsHousehold_inc_less_35k= #, Household income less than $35,000Pct_Household_inc_less_35k= %, Household income less than $35,000Household_inc_35k_75k= #, Household income $35,000 to $74,999Pct_Household_inc_35k_75k= %, Household income $35,000 to $74,999Household_inc_75k_200k= #, Household income $75,000 to $200,000Pct_Household_inc_75k_200k= %, Household income $75,000 to $200,000Household_inc_200k_more= #,

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show population in poverty by Georgia House in the Atlanta region.

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 2013-2017). 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.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

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)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

PopPovDet_e

# Population for whom poverty status is determined, 2017

PopPovDet_m

# Population for whom poverty status is determined, 2017 (MOE)

PopPov_e

# Population below poverty, 2017

PopPov_m

# Population below poverty, 2017 (MOE)

pPopPov_e

% Population below poverty, 2017

pPopPov_m

% Population below poverty, 2017 (MOE)

PopPovU18Det_e

# Population under 18 years for whom poverty status is determined, 2017

PopPovU18Det_m

# Population under 18 years for whom poverty status is determined, 2017 (MOE)

PopPovU18_e

# Population under 18 years below poverty, 2017

PopPovU18_m

# Population under 18 years below poverty, 2017 (MOE)

pPopPovU18_e

% Population under 18 years below poverty, 2017

pPopPovU18_m

% Population under 18 years below poverty, 2017 (MOE)

PopPov18_64Det_e

# Population 18 to 64 years for whom poverty status is determined, 2017

PopPov18_64Det_m

# Population 18 to 64 years for whom poverty status is determined, 2017 (MOE)

PopPov18_64_e

# Population 18 to 64 years below poverty, 2017

PopPov18_64_m

# Population 18 to 64 years below poverty, 2017 (MOE)

pPopPov18_64_e

% Population 18 to 64 years below poverty, 2017

pPopPov18_64_m

% Population 18 to 64 years below poverty, 2017 (MOE)

PopPov65PDet_e

# Population 65 years and over for whom poverty status is determined, 2017

PopPov65PDet_m

# Population 65 years and over for whom poverty status is determined, 2017 (MOE)

PopPov65P_e

# Population 65 years and over below poverty, 2017

PopPov65P_m

# Population 65 years and over below poverty, 2017 (MOE)

pPopPov65P_e

% Population 65 years and over below poverty, 2017

pPopPov65P_m

% Population 65 years and over below poverty, 2017 (MOE)

FamWChildPovStat_e

# Families with related children, 2017

FamWChildPovStat_m

# Families with related children, 2017 (MOE)

FamWChild150Pov_e

# Families with related children below 150 percent of the poverty line, 2017

FamWChild150Pov_m

# Families with related children below 150 percent of the poverty line, 2017 (MOE)

pFamWChild150Pov_e

% Families with related children below 150 percent of the poverty line, 2017

pFamWChild150Pov_m

% Families with related children below 150 percent of the poverty line, 2017 (MOE)

ChildPovStatRatio_e

# Children for whom poverty status is determined, 2017

ChildPovStatRatio_m

# Children for whom poverty status is determined, 2017 (MOE)

ChildInFam200Pov_e

# Children in families below 200 percent of the poverty line, 2017

ChildInFam200Pov_m

# Children in families below 200 percent of the poverty line, 2017 (MOE)

pChildInFam200Pov_e

% Children in families below 200 percent of the poverty line, 2017

pChildInFam200Pov_m

% Children in families below 200 percent of the poverty line, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2011-2015, to show various demographic data by House district in the state of Georgia (including the following categories: total population, age, race/ethnicity, household composition, grandparents, school enrollment, educational attainment, veteran status, disability, foreign born status, linguistic isolation, unemployment, commuting mode, occupation, income, health insurance, poverty, housing characteristics, vehicle availability, housing values, and housing affordability).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. The Census Bureau also calculates a corresponding margin of error (MOE) for ACS measures (although margins of error are not included in this dataset).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 2011-2015). 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, refer to Census Bureau documentation.- - - - - -Base Attributes:DISTRICT = GA House DistrictPOPULATION = District Population (2010 Census)Name = GA House District NameTotal_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)profile_url = Web address of district profile- - - - - -Attributes from ACS:Workers_16_years_and_over= Number, Workers, 16 years and overCar_Truck_or_Van_drove_alone= Number, Car, truck, or van – drove alonePct_Car_Truck_Van_drove_alone= Percent, Car, truck, or van – drove aloneCar_truck_or_van_carpooled= Number, Car, truck, or van – carpooledPct_Car_Truck_Van_carpooled= Percent, Car, truck, or van – carpooledPublic_Transport_excluding_Taxi= Number, Public transportation (excluding taxicab)Pct_Public_Transp_exclude_Taxi= Percent, Public transportation (excluding taxicab)Worked_at_home= Number, Worked at homePct_Worked_at_home= Percent, Worked at homeMean_Travel_Time_to_Work_min= Mean travel time to work (minutes)- - - - - -Civilian_nonInstitutional_Pop= Total Civilian Noninstitutionalized PopulationCiv_nonInstitution_Pop_wDisabil= #, Civilian Noninstitutionalized Population With a disabilityPct_Civ_nonInstitut_Pop_wDisab= %, Civilian Noninstitutionalized Population With a disabilityCiv_nonInstitut_Pop_under_18yrs= #, Civilian Noninstitutionalized Population Under 18 yearsCiv_nonInst_under18_wDisab= #, Civilian Noninstitutionalized Under 18 years With a disabilityPct_Civ_nonInst_under18_wDisab= %, Civilian Noninstitutionalized Under 18 years With a disabilityCiv_nonInst_Pop_18_to_64= #, Civilian Noninstitutionalized Population 18 to 64 yearsCiv_nonInst_18_to_64_wDisab= #, Civilian Noninstitutionalized 18 to 64 years With a disabilityPct_Civ_nonInst_18to64_wDisab= %, Civilian Noninstitutionalized 18 to 64 years With a disabilityCiv_nonInst_Pop_65years_up= #, Civilian Noninstitutionalized Population 65 years and overCiv_nonInst_65up_wDisab= #, Civilian Noninstitutionalized 65 years and over With a disabilityPct_Civ_nonInst_65up_wDisab= %, Civilian Noninstitutionalized 65 years and over With a disability- - - - - -Population_25_years_and_over= #, Population 25 years and overLess_than_HS_or_GED= #, Less than HS or GEDPercent_Less_than_HS_or_GED= %, Less than HS or GEDBA_or_Higher= #, BA or HigherPercent_BA_or_Higher= %, BA or Higher- - - - - -US_Native= #, U.S. NativePercent_US_Native= %, U.S. NativeUSnative_Born_in_US= #, U.S. Native, Born in the United StatesPct_USnative_Born_US= %, U.S. Native, Born in the United StatesUSnative_Born_State_Resid= #, U.S. Native, Born in State of ResidencePct_USnative_Born_State_Resid= %, U.S. Native, Born in State of ResidenceUS_Native_Born_Diff_State= #, U.S. Native, Born in Different StatePct_US_Natv_Born_inDiff_State= %, U.S. Native, Born in Different StateForeign_Born= #, Foreign BornPercent_Foreign_Born= %, Foreign BornForBorn_Nat_UScitizen= #, Foreign Born, Naturalized U.S. CitizenPct_ForBorn_Nat_UScitizen= %, Foreign Born, Naturalized U.S. CitizenForeignBorn_notUS_Citizen= #, Foreign Born, Not a U.S. CitizenPct_ForBorn_notUS_Citizen= %, Foreign Born, Not a U.S. Citizen- - - - - -GParents_Liv_wOwn_GChild_und18= #, Grandparents living with own grandchildren under 18 yearsGParents_RespFor_Gchildren= #, Grandparents Responsible for grandchildrenPct_GPar_RespFor_Gchildren= %, Grandparents Responsible for grandchildren- - - - - -Pop_wHealth_Insurance= #, Civilian noninstitutionalized population with health insurance coveragePct_Pop_wHealth_Ins= %, Civilian noninstitutionalized population with health insurance coveragePop_wPriv_Health_Ins= #, Civilian noninstitutionalized population with private health insurancePct_Pop_wPriv_Health_Ins= %, Civilian noninstitutionalized population with private health insurancePopulation_with_public_coverage= #, Civilian noninstitutionalized population with public coveragePct_Pop_with_public_coverage= %, Civilian noninstitutionalized population with public coveragePop_wNo_Health_Ins= #, Civilian noninstitutionalized population with no health insurance coveragePct_Pop_wNo_Health_Ins= %, Civilian noninstitutionalized population with no health insurance coveragePop_u18_wNo_Health_Ins= #, Civilian Noninstitutionalized Population Under 18 years with no health insurancePct_Pop_u18_wNo_Health_Ins= %, Civilian Noninstitutionalized Population Under 18 years with no health insurancePop_18to64_Employed= #, Civilian noninstitutionalized ages 18 to 64, employedPop_18to64_Empl_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePct_Pop_18to64_Emp_wNo_Hlth_Ins= %, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePop_18to64_Unemployed= #, Civilian noninstitutionalized ages 18 to 64, unemployedPop_18to64_Unemp_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePct_Pop_18to64_Unemp_No_HlthIns= %, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePop_18to64_Not_in_Labor_Force= #, Civilian noninstitutionalized ages 18 to 64, not in labor forcePop_18to64_Not_LabFor_NoHlthIns= #, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurancePctPop_18to64_NotLFor_NoHlthIns= %, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurance- - - - - -HousUnits_MonthOwnerCosts_toInc= #, Housing units for which Selected Monthly Owner Costs as % of income are computedSel_Mo_Own_Costs_30pct_of_Incom= #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomePct_Sel_Mo_Own_Costs_30pct_Inc= %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomeHousUnits_Compute_RentPctIncome= #, Housing units for which Gross rent as a percentage of income is computedRent_Pct_of_Inc_More30Pct= #, Gross rent as a percentage of household income (GRAPI) is 30% or morePctRent_PctIncome_More30Pct= %, Gross rent as a percentage of household income (GRAPI) is 30% or moreHousUnits_OwnRent_Compute= #, Housing units for which SMOCAPI or GRAPI are computedHousCosts_Units_30pctMore_Inc= #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household incomePctHousCost_30pctMore_Income= %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income- - - - - -Total_housing_units= Total housing unitsOccupied_housing_units= #, Occupied housing unitsPercent_Occupied_housing_units= %, Occupied housing unitsVacant_housing_units= #, Vacant housing unitsPercent_Vacant_housing_units= %, Vacant housing unitsHomeowner_vacancy_rate= Homeowner vacancy rateRental_vacancy_rate= Rental vacancy rateOne_unit_detatched_housing_unit= #, 1-unit detached housing unitsPercent_1Unit_Detached= %, 1-unit detached housing unitsHousing_units_built_since_2000= #, Housing units built since 2000Pct_Units_Built_Since_2000= %, Housing units built since 2000Units_Built_1980_to_1999= #, Housing units built 1980 to 1999Pct_Units_Built_1980_to_1999= %, Housing units built 1980 to 1999Units_Built_1979_or_Earlier= #, Housing units built 1979 or earlierPct_Units_Built_1979_or_Earlier= %, Housing units built 1979 or earlierOwner_occupied_housing_units= Housing Tenure: #, Owner occupied housing unitsPct_Owner_Occ_HousUnits= Housing Tenure: %, Owner occupied housing unitsRenter_occupied_housing_units= Housing Tenure: #, Renter occupied housing unitsPct_Renter_Occ_Units= Housing Tenure: %, Renter occupied housing units- - - - - -OwnOcc_units_valued_less_100k= #, Owner occupied housing units valued less than $100,000Pct_OwnOcc_units_val_less_100k= %, Owner occupied housing units valued less than $100,000OwnOcc_units_valued_100k_300k= #, Owner occupied housing units valued $100,000-$299,999Pct_OwnOcc_units_val_100k_300k= %, Owner occupied housing units valued $100,000-$299,999OwnOcc_units_valued_300k_more= #, Owner occupied housing units valued $300,000 or morePct_OwnOcc_units_val_300k_more= %, Owner occupied housing units valued $300,000 or moreMedian_value_own_occ_units= Median value, owner occupied housing units- - - - - -Income_Total_households = Income: Total householdsHousehold_inc_less_35k= #, Household income less than $35,000Pct_Household_inc_less_35k= %, Household income less

About this dataset

Context

The dataset tabulates the data for the Georgia population pyramid, which represents the Georgia 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

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Georgia, is 29.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Georgia, is 22.1.
• Total dependency ratio for Georgia is 51.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Georgia is 4.5.

Content

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

Age groups:

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

Variables / Data Columns

• Age Group: This column displays the age group for the Georgia population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Georgia for the selected age group is shown in the following column.
• Population (Female): The female population in the Georgia for the selected age group is shown in the following column.
• Total Population: The total population of the Georgia for the selected age group 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 Georgia Population by Age. You can refer the same here

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Georgia.

Analysis unit

• Household
• Individual

Universe

The WVS for Georgia covers national population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling universe included the adult population of Georgia residing in both rural and urban areas, excluding the conflict zones of Abkhazia and Ossetia. Military bases and prisons were also not included. In addition, some villages near the regional city of Gori and Zugdidi that are still under occupation by Russian troops were not included in the sampling. The sample design involved a fivestage random cluster sampling. The sampling frame design is based on the 2002 census information.

In this sampling design the sampling units were:

1) Regions and individual cities (Tbilisi and other principal cities) 2) Towns and villages (primary sampling units, PSUs) 3) Districts in cities, towns, and villages in rural areas (sampling points, SPs) 4) Household (by household we mean a group of individuals who live under the same roof and use the same kitchen for cooking) 5) Randomly selected adult members of households At the first stage, the number of respondents was allocated by probability-proportional-to-size (PPS) method. Likewise, at the second and third stages PSUs and SPs were selected by the probability proportional to the unit size (PPS) method. Households were selected via a random route technique and respondents at the household level were selected via the next-birthday technique:

Stage 1 - Primary sampling units At the first stage of the sampling design Georgia was divided into 11 regions; the division being based on the official administrative and geographic regions of the country.

 1 Tbilisi 
 2 Kakheti 
 3 Shida Kartli 
 4 Kvemo Kartli 
 5 Samtskhe Javakheti 
 6 Ajara 
 7 Guria 
 8 Samegrelo 
 9 Imereti & Svaneti 
10 Mtskheta Mtianeti 
11 Racha 

Each region was stratified according to three criteria:

 a) Large cities over 45,000 individuals - There are seven large cities in Georgia including the capital. All of them will be included in the sampling frame and are regarded as having been selfrepresentative cities or PSUs. 
 b) Other cities and towns with populations of less than 45,000 
 c) Rural settlements The number of interviews in all 10 regions was allocated proportional to the size of the adult population in each region. 

Stage 2 - Selection of PSUs In this stage the PSUs are equivalent to rayons- there are a total of 59 rayons (PSUs) in Georgia (excluding Abkhazia and Ossetia). The final sample covered 24 PSUs; this included seven self-representative PSUs were also included in this number. Due to the security reasons, areas close to Ossetian (town of Akhalgori, which was and continues to be under by Russian troops and the buffer zone areas), as well as the town Zugdidi (villages and small towns surrounding town of Zugdidi) were excluded from the sampling framework. Stage 3 - Selection of sampling points (SPs) In urban areas the SPs were census districts whereas in rural areas an entire village was considered as an SP. There are total of 16,582 registered census districts in Georgia and for each one, information existed as to its location/address and the size of the adult population. In the pre-selected PSUs (according to PPS), the number of SPs were determined and per each selected SP around 10 interviews were completed. Rural areas villages are considered as a separate SP and from the list of villages, (this list contains information on the number of adult population per village), and the SPs was selected by PPS. The achieved sampling framework consisted of 188 randomly selected (via PPS) SPs Stage 4 - Selection of households Selection of households was conducted by the application of a random route technique. For each one, SP starting points were identified and given to supervisors who then instructed interviewers as to how sampling mechanism was to be completed. Interviewers were then instructed to make up to two call backs if the original respondent was not available at the time of the initial contact.

Remarks about sampling:

The interviewer was given a route map in which a starting point for each sample point was accurately indicated. Every interviewer was then expected to have conducted no less than 10 interviews for urban SP and 5 among rural sampling points. The choice of starting points for all SP was made by the project manager or supervisor and was not left to the interviewers discretion. The STARTING POINT may be any point along the route, including day care establishments, schools, hospitals, administration buildings, or the beginning or end of a street (the starting point was indicated on the route map beforehand). If the starting point was the beginning of a street, it is necessary to keep to one side of the street (right or left). If a crossroad is met during the route, it is necessary to turn at this juncture and stay to the same side of the route until an appropriate respondent was chosen (i.e. if the left side is chosen, it was necessary to keep to the left side of the crossroad). If the required number of appropriate respondents was not found and the street ended, the interviewer should than have turned to the other side of the street and continued to the left handed side of the street. If the starting point had been a multi-storied building, the interviewer should have proceeded to the top floor and knocked at the door of the apartment on the side of which he followed during the route. It was not possible skipped any apartment until the appropriate respondent was found. After the interview with the appropriate respondent was completed the interviewer was to have followed the route and selected every fifth apartment. The interviewer followed this method after a successful interview was completed. In other cases s/he should have visited the next apartment until an interview was completed. If the interviewer were meeting private houses/plots on the sampling route, he should follow the instructions as indicated above and to have visited every fifth household. Interviews were held only in buildings that contained residences. Exceptions were those buildings (private hospitals, shops, restaurants, etc.), where one or more families permanently resided. The interviewer must allowed the supervisor to have been informed of and coordinated with him any changes that were concerned with the route that occurred during the fieldwork.

The sample size for Georgia is N=1500 and includes the national population aged 18 years and over for both sexes.

Mode of data collection

Face-to-face [f2f]

Research instrument

Length of interviews - Report each instrument separately - Report quartiles and interquartile range as well as median and mean Issues with survey instrument - Problems with particular questions - -for each question why was this problematic - Problems with length No serious problem that could cause the quality of the interviewing process was encountered either during the interviewing or due to the length of the surveys.

Response rate

Reason Cases No one at home 2146 Refusal from the family member 343 Refusal from the respondent 243 Respondents could not communicate (health related problems, language related problems, etc) 31 Respondent was not at home 311 Respondent is out of home during duration of the fieldwork 48 Termination of interview 0 Completed interview 1500

Sampling error estimates

+/- 2,6%

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the 2015 U.S. Census Bureau's American Community Survey to show counts and percentages of population in poverty by census tract in the Atlanta region.Attributes:TRACTCE10 = 6-digit census tract codeGEOID10 = The full FIPS code for this geographyNAME10 = Census tract codePLNG_REGIO = Planning RegionPopulation for Whom Poverty Status is DeterminedPopulation in PovertyPercent Population in PovertyPopulation Under 18 Years for Whom Poverty Status is DeterminedPopulation Under 18 Years in PovertyPercent Population Under 18 Years in PovertyPopulation 18 to 64 Years for Whom Poverty Status is DeterminedPopulation 18 to 64 Years in PovertyPercent Population 18 to 64 Years in PovertyPopulation 65 Years and Over for Whom Poverty Status is DeterminedPopulation 65 Years and Over in PovertyPercent Population 65 Years and Over in PovertyShape.STArea() = Area in square feetSource: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2015For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2011-2015 to show populations with disabilities by state Senate district for the state of Georgia. 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. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here. Attributes: DISTRICT = GA Senate District POPULATION = District Population (2010 Census) Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS) last_edited_date = Last date feature was edited by ARC profile_url = Web address of ARC district profile- - - - - -Civilian_nonInstitutional_Pop = Total Civilian Noninstitutionalized Population Civ_nonInstitution_Pop_wDisabil = #, Civilian Noninstitutionalized Population With a disability Pct_Civ_nonInstitut_Pop_wDisab = %, Civilian Noninstitutionalized Population With a disability Civ_nonInstitut_Pop_under_18yrs = #, Civilian Noninstitutionalized Population Under 18 years Civ_nonInst_under18_wDisab = #, Civilian Noninstitutionalized Under 18 years With a disability Pct_Civ_nonInst_under18_wDisab = %, Civilian Noninstitutionalized Under 18 years With a disability Civ_nonInst_Pop_18_to_64 = #, Civilian Noninstitutionalized Population 18 to 64 years Civ_nonInst_18_to_64_wDisab = #, Civilian Noninstitutionalized 18 to 64 years With a disability Pct_Civ_nonInst_18to64_wDisab = %, Civilian Noninstitutionalized 18 to 64 years With a disability Civ_nonInst_Pop_65years_up = #, Civilian Noninstitutionalized Population 65 years and over Civ_nonInst_65up_wDisab = #, Civilian Noninstitutionalized 65 years and over With a disability Pct_Civ_nonInst_65up_wDisab = %, Civilian Noninstitutionalized 65 years and over With a disability Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

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 2014-2018). 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 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.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

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% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

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 2014-2018). 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 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.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

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% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18