There are 100 Senators that serve in the United States Congress at any given time - two from each of the fifty states. As of the first day of the 118th Congress, there were three African American Senators, two Asian American Senators, and six Hispanic Senators.

This study considers the growing potential of the Internet in United States elections at the sub-presidential level and whether the Internet can be used as an effective tool in campaigns and elections. Internet sites for incumbents, challengers, and third-party candidates were closely examined and compared on several dimensions of quality. Using a sample of sites collected in the 2002 elections, a comprehensive tool was developed to assess Internet quality using both analytical criteria and statistical checks. Five dimensions were examined: content, interactivity, usability, transparency, and audience. This analysis of the 2002 United States election Web sites focuses on the contests for the House of Representatives, the Senate, and for governor in those states with scheduled elections. The dataset includes 111 separate races: 84 for the House, 12 for the Senate and 16 for governor. There are 245 individual House candidates, 62 gubernatorial candidates, and 45 individual Senate candidates. This dataset also explores the relationship between Internet quality and the political and demographic features of a district. Internet quality also is evaluated in relation to other significant resources in a candidate's campaign, e.g., years of service, incumbency, political party, competition, and campaign finance. House races were isolated in order to evaluate the relationship between Internet quality, these significant political resources, and demographic aspects of the districts. Shifting the level of analysis from the candidate to the district examined how short-term elements of campaigns, including a candidate's Web site, interact and correlate with political features of a contest and demographic features of a congressional district.

The number of women in the United States Senate has been increasing in recent years. In 2025, there were 25 women serving in the United States Senate. Of those, 2 identified as Latina, and two as Black.

As of 2025, the average age of senators in the 119th Congress was **. Of the total 100, ** members of the U.S. Senate were between the ages of ** and ** - more than any other age group. The minimum age requirement to be a member of the Senate is **, opposed to the House of Representatives which has a minimum age requirement of **. The average age of members of Congress from 2009 to 2023 can be found here.

The 119th Congress began in January 2025. In this Congress, there were 26 women serving as Senators, and 74 men. The number of women has increased since the 1975 when there were no women in the Senate. The first female Senator was Rebecca Felton of Georgia who was sworn in 1922. A breakdown of women Senators by party can be found here.

US Census American Community Survey (ACS) 2017, 5-year estimates of the key demographic characteristics of State Senate Legislative Districts (Upper) geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census American Community Survey (ACS) 2014, 5-year estimates of the key demographic characteristics of State Senate Legislative Districts (Upper) geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2014 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

This special topic poll, fielded April 1-5, 2000, queried residents of New York State on the prospective Senate race between First Lady Hillary Rodham Clinton and New York City Mayor Rudolph Giuliani in 2000, and on a range of other political and social issues. Respondents were asked to give their opinions of President Bill Clinton, New York State governor George Pataki, Hillary Clinton, Rudolph Giuliani, and civil rights activist Al Sharpton. Regarding the upcoming Senate race, respondents were asked how much attention they were paying to the upcoming election, for whom they would vote, whether that decision was firm, and who they thought would win. Respondents were also asked which of the potential candidates cared more about people like the respondent, whether the candidates cared about the needs and problems of Black people, and whether the candidates were trying to bring together or divide various groups of New Yorkers. Respondents were asked whether they approved or disapproved of the way Giuliani was handling his job as mayor, and the way he was handling crime, education, and race relations. Regarding Mrs. Clinton, respondents were asked whether they approved of the way she was handling her role as First Lady. Opinions were also elicited on whether Hillary Clinton and Giuliani were spending more time explaining what they would do as senator or attacking each other. Respondents were asked to rate the performance of the New York City police department, whether the police should interfere in individuals' freedoms to make the city safer, and if the respondent had ever been insulted by an officer, felt in personal danger from a police officer, or felt safer because of a police officer. Other questions focused on whether racial profiling was widespread in New York City, whether racial profiling was justified, whether respondents had personally been racially profiled, and if the police favored whites over Blacks or Blacks over whites. In relation to the police shooting death of Patrick Dorismond, an unarmed Black male, outside of a Manhattan bar, respondents were asked how closely they had been following the shooting, how common brutality by the New York City police department against minorities was, how the policies of the Giuliani administration affected the amount of police brutality in New York City, whether the officer involved in the Dorismond shooting should face criminal charges, and whether the public comments made by Giuliani, Hillary Clinton, and Sharpton regarding the shooting made the situation better or worse. Background information on respondents includes voter registration and participation history, political party, political orientation, marital status, religion, education, age, sex, race, Hispanic descent, and family income.

Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates. The ACS 5-year period are period estimates that describe the average characteristics of the population and housing over the period of data collection (2018 through 2022). Data provides broad social, economics, housing, and demographics information by Maryland Senate Districts.

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 total population and change by Georgia Senate 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

# Area, Acres, 2017

SqMi

# Area, square miles, 2017

County

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

CountyName

County Name

TotPop_e

# Total population, 2017

TotPop_m

# Total population, 2017 (MOE)

rPopDensity

Population density (people per square mile), 2017

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.

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

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

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

As of December 2022, the United States Senate race in Georgia spent the highest amount of funds in the midterm election cycle, followed by Pennsylvania and Florida. Pennsylvania and Georgia both played a key roll in securing Democrats a Senate majority after the 2022 midterms.

This data collection, focusing on Senate elections, combines data from a three-part series (1988, 1990, 1992) of Senate studies. Over the course of these three elections voters in each of the 50 states were interviewed, and data were gathered on citizen evaluations of all senators at three stages of their six-year election cycles. Both survey data and contextual data for all 50 states are included. The survey data facilitate the comparison of House of Representatives and Senate races through the use of questions that generally parallel those questions used in election studies since 1978 concerning respondents' interaction with and evaluation of candidates for the House of Representatives. However, because of redistricting in the early 1990s, the congressional districts for the 1992 respondents could not be pre-identified. The survey instrument was, therefore, redesigned to some degree, cutting some of the House-related content for the 1992 survey. The 50-state survey design also allows for the comparison of respondents' perceptions and evaluation of senators who were up for re-election with those in the second or fourth years of their terms. Topics covered include respondent's recall and like/dislike of House and Senate candidates, issues discussed in the campaigns, contact with House and Senate candidates/incumbents, respondent's opinion of the proper roles for senators and representatives, a limited set of issue questions, liberal/conservative self-placement, party identification, media exposure, and demographic information. Contextual data presented include election returns for the Senate primary and general elections, voting indices for the years 1983-1992, information about the Senate campaign such as election outcome predictions, campaign pollster used, and spending patterns, and demographic, geographic, and economic data for the state. Also included are derived measures that reorganize the House of Representatives and Senate variables by the party and incumbency/challenger status of the candidate and, for Senate variables only, by proximity to next election. Additionally, a number of analytic variables intended to make analyses more convenient (e.g., Senate class number and whether the respondent voted for the incumbent) are presented.

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 by sex and age by Georgia Senate 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:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here). Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Connecticut Senate Race | RealClearPolling

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from 2010 U.S. Census to show population change by state Senate district for the state of Georgia.Attributes: DISTRICT = GA Senate District POPULATION = District Population (2010 Census) Population_2010 = Population, 2010 Population_2000 = Population, 2000 Population_Change_2000_2010 = Population Change, 2000-2010Pct_Population_Change_2000_2010 = % Population Change, 2000-2010Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)profile_url = Web address of ARC district profile- - - - - -Pop_under_age_19_2010 = Population under age 19, 2010 Pop_ages_20_34_2010 = Population ages 20-34, 2010 Pop_ages_35_44_2010 = Population ages 35-44, 2010 Pop_ages_45_64_2010 = Population ages 45-64, 2010 Pop_ages_65_over_2010 = Population ages 65 and over, 2010 Pct_Pop_under_age_19_2010 = % Population under age 19, 2010 Pct_Pop_ages_20_34_2010 = % Population ages 20-34, 2010 Pct_Pop_ages_35_44_2010 = % Population ages 35-44, 2010 Pct_Pop_ages_45_64_2010 = % Population ages 45-64, 2010 Pct_Pop_ages_65_over_2010 = % Population ages 65 and over, 2010 Pop_under_age_19_2000 = Population under age 19, 2000 Pop_ages_20_34_2000 = Population ages 20-34, 2000 Pop_ages_35_44_2000 = Population ages 35-44, 2000 Pop_ages_45_64_2000 = Population ages 45-64, 2000 Pop_ages_65_over_2000 = Population ages 65 and over, 2000 Pct_Pop_under_age_19_2000 = % Population under age 19, 2000 Pct_Pop_ages_20_34_2000 = % Population ages 20-34, 2000 Pct_Pop_ages_35_44_2000 = % Population ages 35-44, 2000 Pct_Pop_ages_45_64_2000 = % Population ages 45-64, 2000 Pct_Pop_ages_65_over_2000 = % Population ages 65 and over, 2000 Chg_Pop_Under_19 = Change in Population Under 19 (2000-2010) Chg_Pct_Pop_Under_19 = Change in Percent Population Under 19 (2000-2010) Chg_pop_ages_20_34 = Change in 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, 2010 Non_Hisp_Black_2010 = Non-Hispanic Black, 2010 Non_Hisp_AsianPI_2010 = Non-Hispanic Asian/Pacific Islander, 2010 Non_Hisp_Other_Biracial_2010 = Non-Hispanic Other Races (includes biracial), 2010 Hisp_All_races_2010 = Hispanic, All races, 2010 Pct_Non_Hisp_White_2010 = % Non-Hispanic White, 2010 Pct_Non_Hisp_Black_2010 = % Non-Hispanic Black, 2010 Pct_Non_Hisp_AsianPI_2010 = % Non-Hispanic Asian/Pacific Islander, 2010 Pct_Non_Hisp_Other_Bi_2010 = % Non-Hispanic Other Races (includes biracial), 2010 Pct_Hisp_All_races_2010 = % Hispanic, All races, 2010 Non_Hisp_White_2000 = Non-Hispanic White, 2000 Non_Hisp_Black_2000 = Non-Hispanic Black, 2000 Non_Hisp_AsianPI_2000 = Non-Hispanic Asian/Pacific Islander, 2000 Non_Hisp_Other_Biracial_2000 = Non-Hispanic Other Races (includes biracial), 2000 Hisp_All_races_2000 = Hispanic, All races, 2000 Pct_Non_Hisp_White_2000 = % Non-Hispanic White, 2000 Pct_Non_Hisp_Black_2000 = % Non-Hispanic Black, 2000 Pct_Non_Hisp_AsianPI_2000 = % Non-Hispanic Asian/Pacific Islander, 2000 Pct_Non_Hisp_Other_Bi_2000 = % Non-Hispanic Other Races (includes biracial), 2000 Pct_Hisp_All_races_2000 = % Hispanic, All races, 2000 Chg_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)

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