This dataset was 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 2017-2021 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:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (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)BeltLine (buffer)BeltLine Study (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 Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState 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)WFF = Westside Future Fund (subarea of City of Atlanta)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 2017-2021). 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: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Graph and download economic data for Resident Population in Fulton County, GA (GAFULT1POP) from 1970 to 2024 about Fulton County, GA; Atlanta; GA; residents; population; and USA.

Graph and download economic data for Resident Population in Dawson County, GA (GADAWS5POP) from 1970 to 2024 about Dawson County, GA; Atlanta; GA; residents; population; and USA.

About this dataset

Context

The dataset tabulates the population of Fulton County by race. It includes the population of Fulton County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Fulton County across relevant racial categories.

Key observations

The percent distribution of Fulton County population by race (across all racial categories recognized by the U.S. Census Bureau): 39.35% are white, 43.12% are Black or African American, 0.25% are American Indian and Alaska Native, 7.59% are Asian, 0.03% are Native Hawaiian and other Pacific Islander, 3.17% are some other race and 6.48% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Fulton County
• Population: The population of the racial category (excluding ethnicity) in the Fulton County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Fulton County 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 Fulton County Population by Race & Ethnicity. You can refer the same here

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

Graph and download economic data for Population Estimate, Total (5-year estimate) in Douglas County, GA (B03002001E013097) from 2009 to 2023 about Douglas County, GA; Atlanta; GA; estimate; persons; 5-year; population; and USA.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2020 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Some new features of the 2020 Rankings data compared to previous versions:More race/ethnicity categories, including Asian/Pacific Islander and American Indian/Alaska NativeReliability flags that to flag an estimate as unreliable5 new variables: math scores, reading scores, juvenile arrests, suicides, and traffic volumeData Processing Notes:Data downloaded March 2020Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.For demographic variables only, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Graph and download economic data for Population Estimate, Total (5-year estimate) in Houston County, GA (B03002001E013153) from 2009 to 2023 about Houston County, GA; Warner Robins; GA; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total (5-year estimate) in Gwinnett County, GA (B03002001E013135) from 2009 to 2023 about Gwinnett County, GA; Atlanta; GA; estimate; persons; 5-year; population; and USA.

This layer shows poverty status by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. This layer is symbolized to show the count and percentage of the population whose income falls below the Federal poverty line. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains population (urban) measurements in number units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Asian Alone (5-year estimate) in Gwinnett County, GA (B03002016E013135) from 2009 to 2023 about Gwinnett County, GA; Atlanta; asian; GA; latino; hispanic; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Resident Population in Henry County, GA (GAHENR1POP) from 1970 to 2024 about Henry County, GA; Atlanta; GA; residents; population; and USA.

Graph and download economic data for Resident Population in Clayton County, GA (GACLAY5POP) from 1970 to 2024 about Clayton County, GA; Atlanta; GA; residents; population; and USA.

OverviewThis map shows population change compared to pre-crisis baseline in the affected areas by Hurricane Idalia on a daily basis for all Census Designated Population (CDP), an administrative unit smaller than county, in Florida, Georgia, Alabama, and South Carolina. The layer has time enabled to show the change from 2023-08-28 to the latest date when population change data harvested by Data for Good at Meta is available.Population maps provided by Data for Good at Meta are generated based on users of Facebook. For more information about the disaster population maps provided by Data for Good at Meta, please refer to this link.Default data visualizationA divergent color ramp was employed to create a choropleth map for % population change compared to the pre-crisis baseline. The size of pre-crisis baseline is visualized using circles in different sizes. Each polygon represents one census designated place in the affected areas.This feature layer contains the following metrics for mapping and analysis:Baseline population - an estimated number of Facebook users during the pre-crisis period. It is calculated as an average of 90 days before the crisis (in this case, 2023-08-28 was used as the onset of crisis).Crisis population - an estimated number of Facebook users during the crisis. Original data are provided every 8 hours.Difference in population - the difference between crisis population and the baseline populationPercent change in population - the percentage of population change from baseline to a given date during the crisisZ-score - a unitless normalized measurement to quantify the population change from baselineDate - Date of data acquisition. Original data are provided three times a day (8-hour interval). We calculated a daily average using all three timestamps available for each day. Users can filter by Date to create a subset showing the population change on a selected dateMethod of data preparationRemove data points without a valid baseline population or percent change in populationCalculate daily average using the three timestamps available for each dayAggregate the original point data to census designated places in the affected areasAppend all daily average census designated places data to a single file to enable time option of the layer

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains population (urban) measurements in number units and were aggregated to a yearly timescale.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Georgia Coastal Ecosystems (GCE) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

Graph and download economic data for Resident Population in McIntosh County, GA (GAMCIN1POP) from 1970 to 2024 about McIntosh County, GA; Brunswick; GA; residents; population; and USA.