In 2023, 17.9 percent of Black people living in the United States were living below the poverty line, compared to 7.7 percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was 11.1 percent. Poverty in the United States Single people in the United States making less than 12,880 U.S. dollars a year and families of four making less than 26,500 U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

In 2023, about 26.9 percent of Asian private households in the U.S. had an annual income of 200,000 U.S. dollars and more. Comparatively, around 13.9 percent of Black households had an annual income under 15,000 U.S. dollars.

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent. Data Source: Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17.Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2017 - 2021 ACSDate Updated: 10/2023

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 race/ethnicity and change data by Neighborhood Planning Unit 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: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)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes: SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameTotPop_e# Total population, 2017TotPop_m# Total population, 2017 (MOE)Hisp_e# Hispanic or Latino (of any race), 2017Hisp_m# Hispanic or Latino (of any race), 2017 (MOE)pHisp_e% Hispanic or Latino (of any race), 2017pHisp_m% Hispanic or Latino (of any race), 2017 (MOE)Not_Hisp_e# Not Hispanic or Latino, 2017Not_Hisp_m# Not Hispanic or Latino, 2017 (MOE)pNot_Hisp_e% Not Hispanic or Latino, 2017pNot_Hisp_m% Not Hispanic or Latino, 2017 (MOE)NHWhite_e# Not Hispanic, White alone, 2017NHWhite_m# Not Hispanic, White alone, 2017 (MOE)pNHWhite_e% Not Hispanic, White alone, 2017pNHWhite_m% Not Hispanic, White alone, 2017 (MOE)NHBlack_e# Not Hispanic, Black or African American alone, 2017NHBlack_m# Not Hispanic, Black or African American alone, 2017 (MOE)pNHBlack_e% Not Hispanic, Black or African American alone, 2017pNHBlack_m% Not Hispanic, Black or African American alone, 2017 (MOE)NH_AmInd_e# Not Hispanic, American Indian and Alaska Native alone, 2017NH_AmInd_m# Not Hispanic, American Indian and Alaska Native alone, 2017 (MOE)pNH_AmInd_e% Not Hispanic, American Indian and Alaska Native alone, 2017pNH_AmInd_m% Not Hispanic, American Indian and Alaska Native alone, 2017 (MOE)NH_Asian_e# Not Hispanic, Asian alone, 2017NH_Asian_m# Not Hispanic, Asian alone, 2017 (MOE)pNH_Asian_e% Not Hispanic, Asian alone, 2017pNH_Asian_m% Not Hispanic, Asian alone, 2017 (MOE)NH_PacIsl_e# Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017NH_PacIsl_m# Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017 (MOE)pNH_PacIsl_e% Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017pNH_PacIsl_m% Not Hispanic, Native Hawaiian and Other Pacific Islander alone, 2017 (MOE)NH_OthRace_e# Not Hispanic, some other race alone, 2017NH_OthRace_m# Not Hispanic, some other race alone, 2017 (MOE)pNH_OthRace_e% Not Hispanic, some other race alone, 2017pNH_OthRace_m% Not Hispanic, some other race alone, 2017 (MOE)NH_TwoRace_e# Not Hispanic, two or more races, 2017NH_TwoRace_m# Not Hispanic, two or more races, 2017 (MOE)pNH_TwoRace_e% Not Hispanic, two or more races, 2017pNH_TwoRace_m% Not Hispanic, two or more races, 2017 (MOE)NH_AsianPI_e# Non-Hispanic Asian or Pacific Islander, 2017NH_AsianPI_m# Non-Hispanic Asian or Pacific Islander, 2017 (MOE)pNH_AsianPI_e% Non-Hispanic Asian or Pacific Islander, 2017pNH_AsianPI_m% Non-Hispanic Asian or Pacific Islander, 2017 (MOE)NH_Other_e# Non-Hispanic other (Native American, other one race, two or more races), 2017NH_Other_m# Non-Hispanic other (Native American, other one race, two or more races), 2017 (MOE)pNH_Other_e% Non-Hispanic other (Native American, other one race, two or more races), 2017pNH_Other_m% Non-Hispanic other (Native American, other one race, two or more races), 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

This dataset represents the geospatial extent as polygons and the corresponding attribution for census block groups that meet the first half of the definition of Communities of Color according to the Virginia 2020 Environmental Justice Act, or the former part in the excerpt provided below: “Community of Color” definition: “’Community of color’ means any geographically distinct area where the population of color, expressed as a percentage of the total population of such area, is higher than the population of color in the Commonwealth expressed as a percentage of the total population of the Commonwealth. However, if a community of color is composed primarily of one of the groups listed in the definition of "population of color," the percentage population of such group in the Commonwealth shall be used instead of the percentage population of color in the Commonwealth.

The referenced “population of color” definition is also provided below: “Population of Color” definition: “’Population of color’ means a population of individuals who identify as belonging to one or more of the following groups: Black, African American, Asian, Pacific Islander, Native American, other non-white race, mixed race, Hispanic, Latino, or linguistically isolated.”

Graph and download economic data for Unemployment Rate - Black or African American (LNS14000006) from Jan 1972 to Feb 2025 about African-American, 16 years +, household survey, unemployment, rate, and USA.

U.S. Census Bureau QuickFacts statistics for Nevada. 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.

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:YouthOlder AdultsFemaleRacial MinorityEthnic MinorityForeign-BornDisabledLimited English ProficiencyLow-IncomeCensus tables used to gather data from the 2018-2022 American Community Survey 5-Year EstimatesUsing U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group.Census tables used to gather data from the 2018-2022 American Community Survey 5-Year Estimates.For more information and for methodology, visit DVRPC's website:http://www.dvrpc.org/GetInvolved/TitleVI/For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipdSource of tract boundaries: 2020 US Census Bureau, TIGER/Line ShapefilesNote: Tracts with null values should be symbolized as "Insufficient or No Data".Data Dictionary for Attributes:(Source = DVRPC indicates a calculated field)FieldAliasDescriptionSourceyearIPD analysis yearDVRPCgeoid2011-digit tract GEOIDCensus tract identifierACS 5-yearstatefp2-digit state GEOIDFIPS Code for StateACS 5-yearcountyfp3-digit county GEOIDFIPS Code for CountyACS 5-yeartractceTract numberTract NumberACS 5-yearnameTract numberCensus tract identifier with decimal placesACS 5-yearnamelsadTract nameCensus tract name with decimal placesACS 5-yeard_classDisabled percentile classClassification of tract's disabled percentage as: well below average, below average, average, above average, or well above averagecalculatedd_estDisabled count estimateEstimated count of disabled populationACS 5-yeard_est_moeDisabled count margin of errorMargin of error for estimated count of disabled populationACS 5-yeard_pctDisabled percent estimateEstimated percentage of disabled populationACS 5-yeard_pct_moeDisabled percent margin of errorMargin of error for percentage of disabled populationACS 5-yeard_pctileDisabled percentileTract's regional percentile for percentage disabledcalculatedd_scoreDisabled percentile scoreCorresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4calculatedem_classEthnic minority percentile classClassification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above averagecalculatedem_estEthnic minority count estimateEstimated count of Hispanic/Latino populationACS 5-yearem_est_moeEthnic minority count margin of errorMargin of error for estimated count of Hispanic/Latino populationACS 5-yearem_pctEthnic minority percent estimateEstimated percentage of Hispanic/Latino populationcalculatedem_pct_moeEthnic minority percent margin of errorMargin of error for percentage of Hispanic/Latino populationcalculatedem_pctileEthnic minority percentileTract's regional percentile for percentage Hispanic/Latinocalculatedem_scoreEthnic minority percentile scoreCorresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4calculatedf_classFemale percentile classClassification of tract's female percentage as: well below average, below average, average, above average, or well above averagecalculatedf_estFemale count estimateEstimated count of female populationACS 5-yearf_est_moeFemale count margin of errorMargin of error for estimated count of female populationACS 5-yearf_pctFemale percent estimateEstimated percentage of female populationACS 5-yearf_pct_moeFemale percent margin of errorMargin of error for percentage of female populationACS 5-yearf_pctileFemale percentileTract's regional percentile for percentage femalecalculatedf_scoreFemale percentile scoreCorresponding numeric score for tract's female classification: 0, 1, 2, 3, 4calculatedfb_classForeign-born percentile classClassification of tract's foreign born percentage as: well below average, below average, average, above average, or well above averagecalculatedfb_estForeign-born count estimateEstimated count of foreign born populationACS 5-yearfb_est_moeForeign-born count margin of errorMargin of error for estimated count of foreign born populationACS 5-yearfb_pctForeign-born percent estimateEstimated percentage of foreign born populationcalculatedfb_pct_moeForeign-born percent margin of errorMargin of error for percentage of foreign born populationcalculatedfb_pctileForeign-born percentileTract's regional percentile for percentage foreign borncalculatedfb_scoreForeign-born percentile scoreCorresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4calculatedle_classLimited English proficiency percentile classClassification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above averagecalculatedle_estLimited English proficiency count estimateEstimated count of limited english proficiency populationACS 5-yearle_est_moeLimited English proficiency count margin of errorMargin of error for estimated count of limited english proficiency populationACS 5-yearle_pctLimited English proficiency percent estimateEstimated percentage of limited english proficiency populationACS 5-yearle_pct_moeLimited English proficiency percent margin of errorMargin of error for percentage of limited english proficiency populationACS 5-yearle_pctileLimited English proficiency percentileTract's regional percentile for percentage limited english proficiencycalculatedle_scoreLimited English proficiency percentile scoreCorresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4calculatedli_classLow-income percentile classClassification of tract's low income percentage as: well below average, below average, average, above average, or well above averagecalculatedli_estLow-income count estimateEstimated count of low income (below 200% of poverty level) populationACS 5-yearli_est_moeLow-income count margin of errorMargin of error for estimated count of low income populationACS 5-yearli_pctLow-income percent estimateEstimated percentage of low income (below 200% of poverty level) populationcalculatedli_pct_moeLow-income percent margin of errorMargin of error for percentage of low income populationcalculatedli_pctileLow-income percentileTract's regional percentile for percentage low incomecalculatedli_scoreLow-income percentile scoreCorresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4calculatedoa_classOlder adult percentile classClassification of tract's older adult percentage as: well below average, below average, average, above average, or well above averagecalculatedoa_estOlder adult count estimateEstimated count of older adult population (65 years or older)ACS 5-yearoa_est_moeOlder adult count margin of errorMargin of error for estimated count of older adult populationACS 5-yearoa_pctOlder adult percent estimateEstimated percentage of older adult population (65 years or older)ACS 5-yearoa_pct_moeOlder adult percent margin of errorMargin of error for percentage of older adult populationACS 5-yearoa_pctileOlder adult percentileTract's regional percentile for percentage older adultcalculatedoa_scoreOlder adult percentile scoreCorresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4calculatedrm_classRacial minority percentile classClassification of tract's non-white percentage as: well below average, below average, average, above average, or well above averagecalculatedrm_estRacial minority count estimateEstimated count of non-white populationACS 5-yearrm_est_moeRacial minority count margin of errorMargin of error for estimated count of non-white populationACS 5-yearrm_pctRacial minority percent estimateEstimated percentage of non-white populationcalculatedrm_pct_moeRacial minority percent margin of errorMargin of error for percentage of non-white populationcalculatedrm_pctileRacial minority percentileTract's regional percentile for percentage non-whitecalculatedrm_scoreRacial minority percentile scoreCorresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4calculatedtot_ppTotal population estimateEstimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born)ACS 5-yeartot_pp_moeTotal population margin of errorMargin of error for estimated total population of tractACS 5-yeary_classYouth percentile classClassification of tract's youth percentage as: well below average, below average, average, above average, or well above averagecalculatedy_estYouth count estimateEstimated count of youth population (under 18 years)ACS 5-yeary_est_moeYouth count margin of errorMargin of error for estimated count of youth populationACS 5-yeary_pctYouth population percentage estimateEstimated percentage of youth population (under 18 years)calculatedy_pct_moeYouth population percentage margin of

WFRC Community Focus Areas (2023)Geographic Representation Units WFRC’s Community Focus Areas (CFAs) are geographic areas for which additional consideration may be given within the planning and programming processes for future transportation, economic development, and other projects administered through WFRC. CFAs are used by WFRC in support of meeting the Council-established goal of promoting “inclusive engagement in transportation planning processes and equitable access to affordable and reliable transportation options.” CFAs are designated from Census block group geographic zones that meet the criteria described below. Census block groups are used as these are the smallest geographic areas for which more detailed household characteristics like employment, income, vehicle ownership, commute trip, and English language proficiency are available. WFRC recognizes the limitations of geography-based analysis, as proper planning work considers together the needs of individuals, groups and sectors, and geographic areas. However, geography-based analyses offer a useful starting point for the consideration and prioritization of projects that will serve specific community needs.2023 Community Focus Area Criteria UpdateFor the 2023 RTP planning cycle, WFRC will use two factors in designating geography-based CFAs: 1) concentration of low-income households and 2) concentration of persons identifying as members of racial and ethnic minority groups. The geography for these factors can be identified from consistent and regularly updated data sources maintained by the U.S. Census Bureau. WFRC will also make data available that conveys, while maintaining individual anonymity, the geographic distribution of additional measures including concentrations of persons with disabilities, households with limited English language proficiency, households that do not own a vehicle, older residents (65+ years of age), and younger residents (0-17 years of age). While the application of these factors within the planning process is less straightforward because of their higher statistical margins of error and comparatively even distribution within the region, these additional factors remain valuable as planning context. Low Income Focus Areas, Methodology for IdentificationThe block group-level data from the 2020 Census American Community Survey (ACS) 5-year dataset (Table C17002: Ratio of Income to Poverty Level), is used to determine the percentage of the population within each block group that have a ratio of income to federal poverty threshold of equal to or less than 1, i.e., their income is below the poverty level. The federal poverty threshold is set differently for households, considering their household size and age of household members.Census block groups in which more than 20% of the households whose income is less than or equal to the federal poverty threshold are included in the WFRC CFAs and designated as Low-Income focus areas. Racial and Ethnic Minority Focus AreasThe block group-level data from the 2020 ACS 5-year dataset (Table B03002: Hispanic or Latino Origin By Race) is used to determine the percentage of the population that did not self-identify their race and ethnicity as “White alone.” The average census block group area in the Wasatch Front urbanized areas has 24.2% of its population that identifies as Black or African American alone, American Indian, and Alaska Native alone, Asian alone, Native Hawaiian and other Pacific Islander alone, some other race alone, two or more races, or of Hispanic or Latino origin.Census blocks in which more than 40%2 of the population identifies as one or more of the racial or ethnic groups listed above are included in the WFRC CFAs and designated as Racial and Ethnic Minority focus areas.Excluding Predominantly Non-Residential Areas from CFAsSome census block groups that meet one or both of the CFA criteria described above contain large, non-residential areas or low density residential areas. Such census block areas may have small residential neighborhoods surrounded by predominantly commercial or industrial land uses, or large areas of public land or as-yet undeveloped lands. For this reason, WFRC staff may adjust the boundaries of an CFA whose census block group population density is less than 500 persons per square mile, to exclude areas of those block groups that have large, predominantly non-residential land uses.Community Focus Area Update FrequencyThe geography for WFRC CFAs will be updated not less than every four years, preceding the project phasing period of the Regional Transportation Planning update cycle. The update will use the most recent version of the 5 year ACS dataset. The next update is expected in the summer of 2026 (the beginning of the 4th year for the 2027 RTP development process) and is expected to use the 2024 5-year ACS results that average results across 2020-2024.Footnotes:1. The 2019 version of WFRC CFAs used ‘Zero Car Households’ as a third factor. This factor is no longer included because of its geographic and statistical fluctuation over time in data reported by the American Community Survey. Additionally, ‘Zero Car households’ was observed to have a strong relationship with the other two CFA designation factors.2. The percentage threshold specified here is approximately one standard deviation above the regional mean for this indicator. Assuming a statistically normal distribution, approximately 16% of the overall set (i.e. census blocks, in this case) would fall above a one standard deviation threshold.3. Table B03002 includes information from both 'Race' and 'Hispanic or Latino Origin' identification questions asked as part of the Census Bureau's American Community Survey.

Feature class representing retail alcohol outlet density at the census tract level developed directly from address information from liquor licensee lists that were obtained from the Colorado Department of Revenue-Liquor Enforcement Division (DOR-LED). This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment (CDPHE), including the Alcohol Outlet Density StoryMap. CDPHE nor DOR-LED are responsible for data products made using this publicly available data. It should be stated that neither agency is acting as an active data steward of this map service/geospatial data layer at this point in time. This dataset is representative of Colorado licensing data gathered in January 2024. The data file contains the following attributes:FIPSTract Name Tract FIPS StateCountyLand Area Square Miles (Area of Land in Square Miles)Water Area SquareMiles (Area of Water in Square Miles)Population Total (Total Population as estimated in ACS 2018-2022)Percent Race White (Percent of population identifying as White as estimated in ACS 2018-2022) Percent Race African American Percent (Percent of population identifying as African American as estimated in ACS 2018-2022)Race American Indian Alaskan Native (Percent of population identifying as American Indian or Alaskan Native as estimated in ACS 2018-2022)Percent Race Asian (Percent of population identifying as Asian as estimated in ACS 2018-2022)Percent Race NHawaiian OPI (Percent of population identifying as Native Hawaiian or Pacific Islander as estimated in 2018-2022)Percent Race Other (Percent of population identifying as another race as estimated in 2018-2022)Percent Ethnicity Hispanic Latino (Percent of population identifying as Hispanic or Latino as estimated in 2018-2022)Percent Ethnicity Not Hispanic or Latino (Percent of population identifying as not Hispanic or Latino as estimated in 2018-2022)Percent Race Minority Race or Hispanic Latino (Percent of population made up of a Race and/or Ethnicity other than White, Non-Hispanic)Percent Population over 24 Years No HS Diploma (Percent of population over 24 years old without a High School Diploma as estimated in 2018-2022)Frequency All Retail Outlets 2024 (All retail alcohol outlets from January 2024)Average Distance Between Outlets in Meters (Average distance in Meters between an alcohol outlet and its nearest neighboring outlet)Frequency Off Premises Outlets 2024 (All Off-premises retail alcohol outlets from January 2024)Frequency On Premises Outlets 2024 (All On-premises retail alcohol outlets from January 2024)Rate Total Outlets per Square Mile (Rate of all retail alcohol outlets per square mile)Rate Total Outlets per 1,000 Residents (Rate of all retail alcohol outlets per 1,000 residents)Rate On Premises Outlets per Square Mile (Rate of On-premises retail alcohol outlets per square mile)Rate Off Premises Outlets per Square Mile (Rate of On-premises retail alcohol outlets per square mile)Rate On Premises Outlets per 1,000 Residents (Rate of on-premises retail alcohol outlets per 1,000 residents)Rate Off Premises Outlets per 1,000 Residents (Rate of off-premises retail alcohol outlets per 1,000 residents)Average Distance Between Outlets in Miles (Average distance in Miles between an alcohol outlet and its nearest neighboring outlet)

Equity Atlas Data Description

Geographies Background:

Census Tract populations range from 1,200 to 8,000, have an average population of 4,000, and are intended to be relatively homogeneous units with respect to the resident population’s characteristics, economic status, and housing conditions. There are 375 Census Tracts completely within Bexar County. Census Tracts do not follow the CoSA boundary. Both Decennial Census and ACS Tract level data are available for Bexar County.

Blocks are the smallest subdivisions of Tracts. They are typically bounded by visible features like roads and boundaries like city limits. They can have populations that vary from zero to several hundred, such as when an apartment complex occupies the entire area. Blocks are the smallest geographic unit used by the Census Bureau for tabulation of 100-percent data (Data collected from all houses such as in the Decennial Census). There are 23,698 Blocks in Bexar County, 18,629 of which had a population of at least one and as much as 5,052 in the 2020 Decennial Census.

Demographic Data Background:

The U.S. Census Bureau’s Decennial Census is conducted once every ten years. During the Decennial Census, the Census Bureau strives to count every single person and every single residence using what was, prior to 2010, known as the “Short Form.” Decennial Census data are released down to the Census Block level. The data provided in the Decennial Census is much more accurate than the data available from the American Community Survey (ACS), which replaces what was known as the Decennial Census “Long Form.” However, since the Decennial Census is only conducted once every 10-years, the data are not as up to date as that provided by the ACS (Except for the year of Decennial Census data release).

The U.S. Census Bureau’s ACS sends out approximately 3.5-million surveys to nationwide households annually, approximately 135 households per Tract, nationwide, over a 5-year period. The ACS has a final approximate response rate of 67%, or 2.3-million surveys. This means that approximately 13,300 or 1.85% of 717,124 Total Households (Per 2021 ACS 5-Year estimates) in Bexar County respond to an ACS survey in a single year.

ACS 5-year estimates include survey results from 5-years, such as from 2017 to 2021 for the 2021 ACS 5-year estimates. The approximate 66,502 or 9.27% of Total Households within Bexar County responding to the ACS survey over a 5-years period, are the basis for numbers released that represent all households in the county. While the ACS data are more up-to date then Decennial Census data, they are less accurate due to the small sample size and Margin of Error.

Several 2021 ACS 5-Year Estimates tables were used to create the EquityScore GIS data layer attribute table, and the Equity Atlas companion data tables, EquityScoreAdditionalVariables and EquityScoreSpecialVariables. Those ACS tables are:

1. DP02 SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES

2. DP04 SELECTED HOUSING CHARACTERISTICS

3. DP05 ACS DEMOGRAPHIC AND HOUSING ESTIMATES

4. S1701 POVERTY STATUS IN THE PAST 12 MONTHS

5. S1903 MEDIAN INCOME IN THE PAST 12 MONTHS (IN 2017 INFLATION-ADJUSTED DOLLARS)

Split Tracts and Data Allocation:

A couple of issue arise with using the more up to data annually released ACS Census Tract estimates. These issues involve splitting Tracts and allocating demographic values between the split portions of Tracts.

First, Census Tract boundaries do not align with the CoSA boundary, and some Tracts are thus split by the CoSA boundary. To address this, when the portion of a Tract intersecting the CoSA was reduced to a very small area (e.g., Less than 10 Acres) or the intersecting portion is very long and exceedingly narrow sliver, those areas were merged with adjacent Tracts within the CoSA to avoid map clutter. The demographic data of the merged small area/sliver (Typically small counts, if any) do not convey to the Tract with which it was merged since it is important that the demographic values allocated to the portions of split Tracts add up to the original Tract’s values for quality assurance procedures. Instead, that value was added to the majority area portion of the original Tract that is outside the CoSA.

Second, the count values (e.g., Total Population, Race/Ethnicity, High School Education…) of a split by the CoSA boundary Tract need to be divided between the sub-portions of the Tract in a way that acknowledges the fact that population is often not evenly distributed within Tract areas. To address this, two allocation methods were used. The Dasymetric Allocation method divided the 2021 ACS 5-year Tract estimates values within its source Track, based on the 2020 Decennial Census total population values of sub-Tracts area Blocks. For instance, if Tract 1 had 10% of its 2020 Decennial Census Total Population within its Block A, then Block A would be assigned 10% of that Tract’s 2021 ACS Total Population. This methodology approximates population densities within a Tract. For variables with averages rather than counts (e.g., Median Household Incomes), portions of split Tracts retain the original values.

Blocks can also be split by the CoSA boundary. To address this, the Areal Allocation method divided split sub-Tract Block areas based on the percentage of the total area within or without the CoSA boundary. For instance, if a Block had a Dasymetric Allocation assigned Total Population value of 200, and that Block was split so that 75% of its area was in the CoSA, then that portion of the Block intersecting the CoSA was assigned a Total Population value of 150.

Equity Score Assignment:

Following the Split Tract Data Allocation, the CoSA Total Population was calculated as being 1,440,704. This value must be used rather than the Census Bureau’s ACS 5-Year estimate Total Population for the CoSA, 1,434,540, since the allocated values for all the Tracts must add up to the Total Population value. Discrepancies between the allocated from Tracts with the CoSA Boundary value and the Census Bureau CoSA value are minor (+6,164) and at least partly attributable to CoSA boundary changes in recent years (Census Bureau does not update their boundaries as frequently). For the People of Color, Median Household Income, Education and Language Equity Scores, the goal is to have approximately 20-percent of the Tract allocated CoSA Total Population, 288,141, in each of the 5 Equity scores (1-5) for a particular variable.

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In the U.S., median household income rose from 51,570 U.S. dollars in 1967 to 80,610 dollars in 2023. In terms of broad ethnic groups, Black Americans have consistently had the lowest median income in the given years, while Asian Americans have the highest; median income in Asian American households has typically been around double that of Black Americans.

WFRC Equity Focus Areas (2023)Geographic Representation Units WFRC’s Equity Focus Areas (EFAs) are geographic areas for which additional consideration may be given within the planning and programming processes for future transportation, economic development, and other projects administered through WFRC. EFAs are used by WFRC in support of meeting the Council-established goal of promoting “inclusive engagement in transportation planning processes and equitable access to affordable and reliable transportation options.” EFAs are designated from Census block group geographic zones that meet the criteria described below. Census block groups are used as these are the smallest geographic areas for which more detailed household characteristics like employment, income, vehicle ownership, commute trip, and English language proficiency are available. WFRC recognizes the limitations of geography-based equity analysis, as proper planning work considers together the needs of individuals, groups and sectors, and geographic areas. However, geography-based equity analyses offer a useful starting point for the consideration and prioritization of projects that will serve specific community needs.2023 Equity Focus Area Criteria UpdateWFRC will use two factors in designating geography-based EFAs: 1) concentration of low-income households and 2) concentration of persons identifying as members of racial and ethnic minority groups. The geography for these factors can be identified from consistent and regularly updated data sources maintained by the U.S. Census Bureau. WFRC will also make data available that conveys, while maintaining individual anonymity, the geographic distribution of additional equity factor measures including concentrations of persons with disabilities, households with limited English language proficiency, households that do not own a vehicle, older residents (65+ years of age), and younger residents (0-17 years of age). While the application of these factors within the planning process is less straightforward because of their higher statistical margins of error and comparatively even distribution within the region, these additional factors remain valuable as planning context. Low Income Focus Areas, Methodology for IdentificationThe block group-level data from the 2020 Census American Community Survey (ACS) 5-year dataset (Table C17002: Ratio of Income to Poverty Level), is used to determine the percentage of the population within each block group that have a ratio of income to federal poverty threshold of equal to or less than 1, i.e., their income is below the poverty level. The federal poverty threshold is set differently for households, considering their household size and age of household members.Census block groups in which more than 20% of the households whose income is less than or equal to the federal poverty threshold are included in the WFRC EFAs and designated as Low-Income focus areas. Racial and Ethnic Minority Focus AreasThe block group-level data from the 2020 ACS 5-year dataset (Table B03002: Hispanic or Latino Origin By Race) is used to determine the percentage of the population that did not self-identify their race and ethnicity as “White alone.” The average census block group area in the Wasatch Front urbanized areas has 24.2% of its population that identifies as Black or African American alone, American Indian, and Alaska Native alone, Asian alone, Native Hawaiian and other Pacific Islander alone, some other race alone, two or more races, or of Hispanic or Latino origin.Census blocks in which more than 40%2 of the population identifies as one or more of the racial or ethnic groups listed above are included in the WFRC EFAs and designated as Racial and Ethnic Minority focus areas.Excluding Predominantly Non-Residential Areas from EFAsSome census block groups that meet one or both of the EFA criteria described above contain large, non-residential areas or low density residential areas. Such census block areas may have small residential neighborhoods surrounded by predominantly commercial or industrial land uses, or large areas of public land or as-yet undeveloped lands. For this reason, WFRC staff may adjust the boundaries of an EFA whose census block group population density is less than 500 persons per square mile, to exclude areas of those block groups that have large, predominantly non-residential land uses.Equity Focus Area Update FrequencyThe geography for WFRC EFAs will be updated not less than every four years, preceding the project phasing period of the Regional Transportation Planning update cycle. The update will use the most recent version of the 5 year ACS dataset. The next update is expected in the summer of 2026 (the beginning of the 4th year for the 2027 RTP development process) and is expected to use the 2024 5-year ACS results that average results across 2020-2024.Footnotes:1. The 2019 version of WFRC EFAs uses ‘Zero Car Households’ as a third factor. This factor is no longer included because of its geographic and statistical fluctuation over time in data reported by the American Community Survey. Additionally, ‘Zero Car households’ was observed to have a strong relationship with the other two EFA designation factors.2. The percentage threshold specified here is approximately one standard deviation above the regional mean for this indicator. Assuming a statistically normal distribution, approximately 16% of the overall set (i.e. census blocks, in this case) would fall above a one standard deviation threshold.3. Table B03002 includes information from both 'Race' and 'Hispanic or Latino Origin' identification questions asked as part of the Census Bureau's American Community Survey.

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

In 2022, there were 313,017 cases filed by the NCIC where the race of the reported missing was White. In the same year, 18,928 people were missing whose race was unknown.

What is the NCIC?

The National Crime Information Center (NCIC) is a digital database that stores crime data for the United States, so criminal justice agencies can access it. As a part of the FBI, it helps criminal justice professionals find criminals, missing people, stolen property, and terrorists. The NCIC database is broken down into 21 files. Seven files belong to stolen property and items, and 14 belong to persons, including the National Sex Offender Register, Missing Person, and Identify Theft. It works alongside federal, tribal, state, and local agencies. The NCIC’s goal is to maintain a centralized information system between local branches and offices, so information is easily accessible nationwide.

Missing people in the United States

A person is considered missing when they have disappeared and their location is unknown. A person who is considered missing might have left voluntarily, but that is not always the case. The number of the NCIC unidentified person files in the United States has fluctuated since 1990, and in 2022, there were slightly more NCIC missing person files for males as compared to females. Fortunately, the number of NCIC missing person files has been mostly decreasing since 1998.

United States presidential elections are quadrennial elections that decide who will be the President and Vice President of the United States for the next four years. Voter turnout has ranged between 54 and 70 percent since 1964, with white voters having the highest voter turnout rate (particularly when those of Hispanic descent are excluded). In recent decades, turnout among black voters has got much closer to the national average, and in 2008 and 2012, the turnout among black voters was higher than the national average, exceeded only by non-Hispanic white voters; this has been attributed to Barack Obama's nomination as the Democratic nominee in these years, where he was the first African American candidate to run as a major party's nominee. Turnout among Asian and Hispanic voters is much lower than the national average, and turnout has even been below half of the national average in some elections. This has been attributed to a variety of factors, such as the absence of voting tradition in some communities or families, the concentration of Asian and Hispanic communities in urban (non-swing) areas, and a disproportionate number of young people (who are less likely to vote).

In 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.

During the Second World War, the three Axis powers of Germany, Italy, and Finland mobilized the largest share of their male population. For the Allies, the Soviet Union mobilized the largest share of men, as well as the largest total army of any country, but it was restricted in its ability to mobilize more due to the impact this would have on its economy. Other notable statistics come from the British Empire, where a larger share of men were drafted from Dominions than from the metropole, and there is also a discrepancy between the share of the black and white populations from South Africa.

However, it should be noted that there were many external factors from the war that influenced these figures. For example, gender ratios among the adult populations of many European countries was already skewed due to previous conflicts of the 20th century (namely WWI and the Russian Revolution), whereas the share of the male population eligible to fight in many Asian and African countries was lower than more demographically developed societies, as high child mortality rates meant that the average age of the population was much lower.