In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** 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.

This dataset contains R/ECAP data for the nine-county San Francisco Bay Region at the census tract level.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs.

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.

Data Source: Decennial census (2010); American Community Survey (ACS), 2006-2010; Brown Longitudinal Tract Database (LTDB) based on decennial census data, 2000 & 1990 References: Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

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.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

This table contains data on the percentage of the total population living below 200% of the Federal Poverty Level (FPL), and the percentage of children living below 200% FPL for California, its regions, counties, cities, towns, public use microdata areas, and census tracts. Data for time periods 2011-2015 (overall poverty) and 2012-2016 (child poverty) and with race/ethnicity stratification is included in the table. The poverty rate table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Poverty is an important social determinant of health (see http://www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=39) that can impact people’s access to basic necessities (housing, food, education, jobs, and transportation), and is associated with higher incidence and prevalence of illness, and with reduced access to quality health care. More information on the data table and a data dictionary can be found in the About/Attachments section.

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

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

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2022 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT20: 2020 Census tractFIP22: 2022 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2022) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP22CSA: 2020 census tract with 2022 city FIPs for incorporated cities and unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP22_AGE_0_4: 2022 population 0 to 4 years oldPOP22_AGE_5_9: 2022 population 5 to 9 years old POP22_AGE_10_14: 2022 population 10 to 14 years old POP22_AGE_15_17: 2022 population 15 to 17 years old POP22_AGE_18_19: 2022 population 18 to 19 years old POP22_AGE_20_44: 2022 population 20 to 24 years old POP22_AGE_25_29: 2022 population 25 to 29 years old POP22_AGE_30_34: 2022 population 30 to 34 years old POP22_AGE_35_44: 2022 population 35 to 44 years old POP22_AGE_45_54: 2022 population 45 to 54 years old POP22_AGE_55_64: 2022 population 55 to 64 years old POP22_AGE_65_74: 2022 population 65 to 74 years old POP22_AGE_75_84: 2022 population 75 to 84 years old POP22_AGE_85_100: 2022 population 85 years and older POP22_WHITE: 2022 Non-Hispanic White POP22_BLACK: 2022 Non-Hispanic African AmericanPOP22_AIAN: 2022 Non-Hispanic American Indian or Alaska NativePOP22_ASIAN: 2022 Non-Hispanic Asian POP22_HNPI: 2022 Non-Hispanic Hawaiian Native or Pacific IslanderPOP22_HISPANIC: 2022 HispanicPOP22_MALE: 2022 Male POP22_FEMALE: 2022 Female POV22_WHITE: 2022 Non-Hispanic White below 100% Federal Poverty Level POV22_BLACK: 2022 Non-Hispanic African American below 100% Federal Poverty Level POV22_AIAN: 2022 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV22_ASIAN: 2022 Non-Hispanic Asian below 100% Federal Poverty Level POV22_HNPI: 2022 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV22_HISPANIC: 2022 Hispanic below 100% Federal Poverty Level POV22_TOTAL: 2022 Total population below 100% Federal Poverty Level POP22_TOTAL: 2022 Total PopulationAREA_SQMil: Area in square mile.POP22_DENSITY: Population per square mile.POV22_PERCENT: Poverty rate/percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2022. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

Highlight data for the Hispanic or Latino Origin population of any race as reported by the US Census Bureau, American Community Survey (ACS) 5-year estimates. The year shown in the dataset referes to the final year of the five-year reporting period (ie "2019" refers to the 2015-2019 ACS).

Poverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.

Descriptive statistics of residential (high-poverty) trajectories by race and cohort.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English. The disadvantage variable was incorrectly calculated for the following datasets: DS7 Socioeconomic Status and Demographic Characteristics of Census Tracts (2020 Census), United States, 2018-2022 Data DS8 Socioeconomic Status and Demographic Characteristics of ZIP Code Tabulation Areas (2020 Census), United States, 2018-2022 Data Please refrain from downloading these datasets. The updated datasets are forthcoming and will be made available soon. Users needing these datasets can reach out to nanda-admin@umich.edu.

Tabular data of population by age groups, race and gender, and the poverty by ethnic groups is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2010 census tracts split by 2011 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2010 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT10: 2010 Census tractFIP11: 2011 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2011) CT10FIP11: 2010 census tract with 2011 city FIPs for incorporated cities and unincorporated areas. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP11_AGE_0_4: 2011 population 0 to 4 years oldPOP11_AGE_5_9: 2011 population 5 to 9 years old POP11_AGE_10_14: 2011 population 10 to 14 years old POP11_AGE_15_17: 2011 population 15 to 17 years old POP11_AGE_18_19: 2011 population 18 to 19 years old POP11_AGE_20_44: 2011 population 20 to 24 years old POP11_AGE_25_29: 2011 population 25 to 29 years old POP11_AGE_30_34: 2011 population 30 to 34 years old POP11_AGE_35_44: 2011 population 35 to 44 years old POP11_AGE_45_54: 2011 population 45 to 54 years old POP11_AGE_55_64: 2011 population 55 to 64 years old POP11_AGE_65_74: 2011 population 65 to 74 years old POP11_AGE_75_84: 2011 population 75 to 84 years old POP11_AGE_85_100: 2011 population 85 years and older POP11_WHITE: 2011 Non-Hispanic White POP11_BLACK: 2011 Non-Hispanic African AmericanPOP11_AIAN: 2011 Non-Hispanic American Indian or Alaska NativePOP11_ASIAN: 2011 Non-Hispanic Asian POP11_HNPI: 2011 Non-Hispanic Hawaiian Native or Pacific IslanderPOP11_HISPANIC: 2011 HispanicPOP11_MALE: 2011 Male POP11_FEMALE: 2011 Female POV11_WHITE: 2011 Non-Hispanic White below 100% Federal Poverty Level POV11_BLACK: 2011 Non-Hispanic African American below 100% Federal Poverty Level POV11_AIAN: 2011 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV11_ASIAN: 2011 Non-Hispanic Asian below 100% Federal Poverty Level POV11_HNPI: 2011 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV11_HISPANIC: 2011 Hispanic below 100% Federal Poverty Level POV11_TOTAL: 2011 Total population below 100% Federal Poverty Level POP11_TOTAL: 2011 Total PopulationAREA_SQMIL: Area in square milePOP11_DENSITY: Population per square mile.POV11_PERCENT: Poverty percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2010 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2011. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

Key Table Information.Table Title.Poverty Status in the Past 12 Months by Sex by Age (Black or African American Alone).Table ID.ACSDT1Y2024.B17001B.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nati...

This layer is part of source data for the State of Poverty 2018-2024 Los Angeles County Dashboard.Layers include estimates of total population and population in poverty by demographics at each geography level in LA County.Source: Annual Population and Poverty Estimation, Los Angeles County ISD-Demography.Datasets for all years available in the State of Poverty dashboard:PAI Poverty Map Data 2024PAI Poverty Map Data 2023PAI Poverty Map Data 2022PAI Poverty Map Data 2021PAI Poverty Map Data 2020PAI Poverty Map Data 2019PAI Poverty Map Data 2018 Included Geography LevelsSplit Census TractsCensus TractsCountywide Statistical Areas (CSA)Public Use Microdata Areas (PUMA)Service Planning Area (SPA)Supervisor District (SD)Los Angeles County Split Census Tract and CSA boundaries correspond to the year of the population and poverty estimates. Census Tract, PUMA, SPA, SD, and county boundaries are current as of 2020 US Census. Field NamesPlease see Field Aliases for detailed field names.Field name logic:1st character Race/Ethnicityt = Totala = Asianb = Black or African Americanh = Hispanic or Latinoi = American Indian and Alaska Native (AIAN)p = Pacific Islanderw = White2nd character Gendert = Totalf = Femalem = Male3-4th characters Year2-digit year (2018-22)Possible 5th character Poverty Level (%FPL)a = Below 100% FPLd = Below 200% FPLg = Below 266% FPLRemaining characters after underscoret = Total (all ages)

This map service displays data derived from the 2008-2012 American Community Survey (ACS). Values derived from the ACS and used for this map service include: Total Population, Population Density (per square mile), Percent Minority, Percent Below Poverty Level, Percent Age (less than 5, less than 18, and greater than 64), Percent Housing Units Built Before 1950, Percent (population) 25 years and over (with less than a High School Degree and with a High School Degree), Percent Linguistically Isolated Households, Population of American Indians and Alaskan Natives, Population of American Indians and Alaskan Natives Below Poverty Level, and Percent Low Income Population (Less Than 2X Poverty Level). The map service was created for inclusion in US EPA mapping applications.

Key Table Information.Table Title.Poverty Status in the Past 12 Months of Families by Family Type by Presence of Related Children Under 18 Years (Black or African American Alone Householder).Table ID.ACSDT1Y2024.C17010B.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produce...

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2010 census tracts split by 2016 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2010 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT10: 2010 Census tractFIP16: 2016 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2016) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT10FIP16CSA: 2010 census tract with 2016 city FIPs for incorporated cities, unincorporated areas and LA neighborhoods. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP16_AGE_0_4: 2016 population 0 to 4 years oldPOP16_AGE_5_9: 2016 population 5 to 9 years old POP16_AGE_10_14: 2016 population 10 to 14 years old POP16_AGE_15_17: 2016 population 15 to 17 years old POP16_AGE_18_19: 2016 population 18 to 19 years old POP16_AGE_20_44: 2016 population 20 to 24 years old POP16_AGE_25_29: 2016 population 25 to 29 years old POP16_AGE_30_34: 2016 population 30 to 34 years old POP16_AGE_35_44: 2016 population 35 to 44 years old POP16_AGE_45_54: 2016 population 45 to 54 years old POP16_AGE_55_64: 2016 population 55 to 64 years old POP16_AGE_65_74: 2016 population 65 to 74 years old POP16_AGE_75_84: 2016 population 75 to 84 years old POP16_AGE_85_100: 2016 population 85 years and older POP16_WHITE: 2016 Non-Hispanic White POP16_BLACK: 2016 Non-Hispanic African AmericanPOP16_AIAN: 2016 Non-Hispanic American Indian or Alaska NativePOP16_ASIAN: 2016 Non-Hispanic Asian POP16_HNPI: 2016 Non-Hispanic Hawaiian Native or Pacific IslanderPOP16_HISPANIC: 2016 HispanicPOP16_MALE: 2016 Male POP16_FEMALE: 2016 Female POV16_WHITE: 2016 Non-Hispanic White below 100% Federal Poverty Level POV16_BLACK: 2016 Non-Hispanic African American below 100% Federal Poverty Level POV16_AIAN: 2016 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV16_ASIAN: 2016 Non-Hispanic Asian below 100% Federal Poverty Level POV16_HNPI: 2016 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV16_HISPANIC: 2016 Hispanic below 100% Federal Poverty Level POV16_TOTAL: 2016 Total population below 100% Federal Poverty Level POP16_TOTAL: 2016 Total PopulationAREA_SQMIL: Area in square milePOP16_DENSITY: Population per square mile.POV16_PERCENT: Poverty rate/percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2010 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2016. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include the state-level poverty ratio, which compares the proportion of females living in poverty to the proportion of males living in poverty in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.

These data assess the effects of the risk of local jail incarceration and of police aggressiveness in patrol style on rates of violent offending. The collection includes arrest rates for public order offenses, size of county jail populations, and numbers of new prison admissions as they relate to arrest rates for index (serious) crimes. Data were collected from seven sources for each city. CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: SUMMARY TAPE FILE 1A (ICPSR 7941), provided county-level data on number of persons by race, age, and age by race, number of persons in households, and types of households within each county. CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: SUMMARY TAPE FILE 3A (ICPSR 8071), measured at the city level, provided data on total population, race, age, marital status by sex, persons in household, number of households, housing, children, and families above and below the poverty level by race, employment by race, and income by race within each city. The Federal Bureau of Investigation (FBI) 1980 data provided variables on total offenses and offense rates per 100,000 persons for homicides, rapes, robbery, aggravated assault, burglary, larceny, motor vehicle offenses, and arson. Data from the FBI for 1980-1982, averaged per 100,000, provided variables for the above offenses by sex, age, and race, and the Uniform Crime Report arrest rates for index crimes within each city. The NATIONAL JAIL CENSUS for 1978 and 1983 (ICPSR 7737 and ICPSR 8203), aggregated to the county level, provided variables on jail capacity, number of inmates being held by sex, race, and status of inmate's case (awaiting trial, awaiting sentence, serving sentence, and technical violations), average daily jail populations, number of staff by full-time and part-time, number of volunteers, and number of correctional officers. The JUVENILE DETENTION AND CORRECTIONAL FACILITY CENSUS for 1979 and 1982-1983 (ICPSR 7846 and 8205), aggregated to the county level, provided data on the number of individuals being held by type of crime and sex, as well as age of juvenile offenders by sex, average daily prison population, and payroll and other expenditures for the institutions.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.