This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This multi-scale map uses dots to represent the population of each race/ethnicity living within an area. Map opens at the state level, centered on the lower 48 states. Data is from U.S. Census Bureau's 2020 PL 94-171 data for tract, block group, and block.The map's colors represent each of the eight race/ethnicity categories have the highest total count. You can adjust the density of dots in your area by choosing "Change Style" for a layer. Race and ethnicity highlights from the U.S. Census Bureau:White population remained the largest race or ethnicity group in the United States, with 204.3 million people identifying as White alone. Overall, 235.4 million people reported White alone or in combination with another group. However, the White alone population decreased by 8.6% since 2010.Two or More Races population (also referred to as the Multiracial population) has changed considerably since 2010. The Multiracial population was measured at 9 million people in 2010 and is now 33.8 million people in 2020, a 276% increase.“In combination” multiracial populations for all race groups accounted for most of the overall changes in each racial category.All of the race alone or in combination groups experienced increases. The Some Other Race alone or in combination group (49.9 million) increased 129%, surpassing the Black or African American population (46.9 million) as the second-largest race alone or in combination group.The next largest racial populations were the Asian alone or in combination group (24 million), the American Indian and Alaska Native alone or in combination group (9.7 million), and the Native Hawaiian and Other Pacific Islander alone or in combination group (1.6 million).Hispanic or Latino population, which includes people of any race, was 62.1 million in 2020. Hispanic or Latino population grew 23%, while the population that was not of Hispanic or Latino origin grew 4.3% since 2010.View more 2020 Census statistics highlights on race and ethnicity.

This layer shows dot density of the population by race and Hispanic origin - each race and Hispanic or Latino ethnicity is represented by a dot of the number of people who reported their race in each category. This is shown by 2020 census tract boundaries. This map 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 additional calculated attributes related to this topic, which can be mapped or used within analysis. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. This map uses services from these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. For more information regarding the ACS vintage, table sources and data processing notes, please see the item page for the source map service.

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter. -----------------------------------------This layer shows population broken down by race and Hispanic origin. This layer shows Census data from Esri's Living Atlas and is clipped to only show Tempe census tracts. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). Data is from US Census American Community Survey (ACS) 5-year estimates. Vintage: 2015-2019 ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.) Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: December 10, 2020 National Figures: data.census.gov Additional Census data notes and data processing notes are available at the Esri Living Atlas Layer: https://tempegov.maps.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8f&view=list&sortOrder=desc&sortField=defaultFSOrder#overview (Esri's Living Atlas always shows latest data)

This map shows the percentage of people who identify as something other than non-Hispanic white throughout the US according to the most current American Community Survey. The pattern is shown by states, counties, and Census tracts. Zoom or search for anywhere in the US to see a local pattern. Click on an area to learn more. Filter to your area and save a new version of the map to use for your own mapping purposes.The Arcade expression used was: 100 - B03002_calc_pctNHWhiteE, which is simply 100 minus the percent of population who identifies as non-Hispanic white. The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map update automatically annually when the newest estimates are released by ACS. For more detailed metadata, visit the ArcGIS Living Atlas Layer: ACS Race and Hispanic Origin Variables - Boundaries.The data on race were derived from answers to the question on race that was asked of individuals in the United States. The Census Bureau collects racial data in accordance with guidelines provided by the U.S. Office of Management and Budget (OMB), and these data are based on self-identification. The racial categories included in the census questionnaire generally reflect a social definition of race recognized in this country and not an attempt to define race biologically, anthropologically, or genetically. The categories represent a social-political construct designed for collecting data on the race and ethnicity of broad population groups in this country, and are not anthropologically or scientifically based. Learn more here.Other maps of interest:American Indian or Alaska Native Population in the US (Current ACS)Asian Population in the US (Current ACS)Black or African American Population in the US (Current ACS)Hawaiian or Other Pacific Islander Population in the US (Current ACS)Hispanic or Latino Population in the US (Current ACS) (some people prefer Latinx)Population who are Some Other Race in the US (Current ACS)Population who are Two or More Races in the US (Current ACS) (some people prefer mixed race or multiracial)White Population in the US (Current ACS)Race in the US by Dot DensityWhat is the most common race/ethnicity?

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population that is Hispanic or Latino. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2018-2022ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyData Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 15, 2023National Figures: data.census.gov

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population that is Hispanic or Latino. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2019-2023ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyData Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 12, 2024National Figures: data.census.gov

This map shows which race/ethnicity group has the lowest median income in the United States by tract, county and state, using the latest available data from the U.S. Census Bureau's American Community Survey (ACS).For each group showing a median income figure, the lowest median income determines the color used on the map. This is shown by tract, county, and state boundaries. 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. Median income and income source is based on income in past 12 months of survey. The map's topic is shown by tract, county, and state boundaries. 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. To see the full list of attributes available in this map's layers, go to a layer listed under the "Layers" section below and choose the "Data" tab for that layer, and choose "Fields" at the top right on that page.

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

This web map summarizes racial and ethnic diversity in the United States. The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). The diversity score for the entire United States in 2010 is 60. This data variable is included in Esri’s Updated Demographics (2010/2015). Diversity in the U.S. population is increasing. The states with the most diverse populations are California, Hawaii, and New Mexico. This map shows Esri's 2010 estimates using Census 2000 geographies. The geography depicts States at greater than 25m scale, Counties at 1m to 25m scale, Census Tracts at 250k to 1m scale, and Census Block Groups at less than 250k scale.Esri's Updated Demographics (2010/2015) – Population, age, income, sex, and race are among the variables included in the database. Each year, Esri's data development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of geographies. See Updated Demographics for more information. Information about the USA Diversity Index map service used in this map is here.

This layer shows population broken down by race and Hispanic origin. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population that is Hispanic or Latino. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data curated from Esri Living Atlas clipped to Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: data.census.govAdditional Census data notes and data processing notes are available at the Esri Living Atlas Layer:https://tempegov.maps.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8f&view=list&sortOrder=desc&sortField=defaultFSOrder#overview(Esri's Living Atlas always shows latest data)

This multi-scale map shows the predominant (most numerous) race/ethnicity living within an area. Map opens at the state level, centered on the lower 48 states. Data is from U.S. Census Bureau's 2020 PL 94-171 data for state, county, tract, block group, and block.The map's colors indicate which of the eight race/ethnicity categories have the highest total count.Race and ethnicity highlights from the U.S. Census Bureau:White population remained the largest race or ethnicity group in the United States, with 204.3 million people identifying as White alone. Overall, 235.4 million people reported White alone or in combination with another group. However, the White alone population decreased by 8.6% since 2010.Two or More Races population (also referred to as the Multiracial population) has changed considerably since 2010. The Multiracial population was measured at 9 million people in 2010 and is now 33.8 million people in 2020, a 276% increase.“In combination” multiracial populations for all race groups accounted for most of the overall changes in each racial category.All of the race alone or in combination groups experienced increases. The Some Other Race alone or in combination group (49.9 million) increased 129%, surpassing the Black or African American population (46.9 million) as the second-largest race alone or in combination group.The next largest racial populations were the Asian alone or in combination group (24 million), the American Indian and Alaska Native alone or in combination group (9.7 million), and the Native Hawaiian and Other Pacific Islander alone or in combination group (1.6 million).Hispanic or Latino population, which includes people of any race, was 62.1 million in 2020. Hispanic or Latino population grew 23%, while the population that was not of Hispanic or Latino origin grew 4.3% since 2010.View more 2020 Census statistics highlights on race and ethnicity.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

Census block-level data focusing on race and ethnicity. This layer captures the distribution of 2010 Census respondents self-identifying as "American Indian" in the City of Johns Creek, GA.

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

This map shows density surfaces derived from the 2010 US Census block points.This data shows % of people who identified themselves as single race and whiteThe block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

This dataset identifies race in detail by zip code tabulation areas within the United States. The dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The races included in this dataset are White, Black or African American, American Indian and Alaskan Native, Asian, Native Hawaiian and other Pacific Islander, and others. The ACS also looks at races as stand-alone, and two or more races combined.

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 2016-2020 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.

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% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (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 (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (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)

State of Georgia (statewide)

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 2016-2020). 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 Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

American Community Survey (2011-2015 5-Year Estimates) block group data was downloaded from American FactFinder, containing race and ethnicity population numbers. Data Driven Detroit assigned each block group a predominant race/ethnicity. Data was obtained for the Demographic section of Little Caesar's Arena District Needs Assessment.