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.

In 2023 the poverty rate in the United States was highest among people between 18 and 24, with a rate of 16 percent for male Americans and a rate of 21 percent for female Americans. The lowest poverty rate for both men and women was for those aged between 45 and 54. What is the poverty line? The poverty line is a metric used by the U.S. Census Bureau to define poverty in the United States. It is a specific income level that is considered to be the bare minimum a person or family needs to meet their basic needs. If a family’s annual pre-tax income is below this income level, then they are considered impoverished. The poverty guideline for a family of four in 2021 was 26,500 U.S. dollars. Living below the poverty line According to the most recent data, almost one-fifth of African Americans in the United States live below the poverty line; the most out of any ethnic group. Additionally, over 7.42 million families in the U.S. live in poverty – a figure that has held mostly steady since 1990, outside the 2008 financial crisis which threw 9.52 million families into poverty by 2012. The poverty gender gap Wage inequality has been an ongoing discussion in U.S. discourse for many years now. The poverty gap for women is most pronounced during their child-bearing years, shrinks, and then grows again in old age. While progress has been made on the gender pay gap over the last 30 years, there are still significant disparities, even in occupations that predominantly employ men. Additionally, women are often having to spend more time attending to child and household duties than men.

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.

In 2023, it was estimated that around ** percent of people in the United States living with a disability were in poverty. In comparison, the poverty rate among people in the U.S. without a disability was **** percent. A disability is any physical or mental condition that significantly impacts a person's ability to carry out daily tasks or life activities. How many people in the United States are disabled? In 2023, around ** percent of people in the United States were thought to be living with a disability. Types of disabilities include those that affect hearing, cognition, self-care, mobility, and vision. The most common type of disability in the United States is ambulatory disabilities, which impairs a person’s ability to walk. In 2023, almost ** percent of those aged 75 years and older in the U.S. had an ambulatory disability. However, disabilities are far less common among younger people, with less than **** percent of those aged 21 to 64 suffering from an ambulatory disability. Employment among the disabled The most obvious reason why the poverty rate among those with a disability is higher than those without a disability is because disabilities affect a person’s ability to work and be employed. In 2023, the employment rate for those with a disability was **** percent, compared to an employment rate of **** percent among those without a disability. Those with hearing disabilities are the most likely to be employed, with a rate of around ** percent, compared to an employment rate of ** percent among those with an ambulatory disability. Still, those with disabilities who do work have lower annual median earnings than those without disabilities. In 2023, the annual median earnings for U.S. adults without a disability were ****** U.S. dollars, compared to ****** U.S. dollars for those with a disability.

This report was written in collaboration between the Mayor's Office of Innovation and the Rochester Monroe Anti-Poverty Initiative (RMAPI) and released in December 2019. Executive SummaryThe Rochester Monroe Anti-Poverty Initiative (RMAPI) has selected single female headed households with children as one of its key target populations in which to focus strategy and its next phase of initiatives. This report is intended to provide additional insight on this population to support the next phase of RMAPI’s strategic planning as well as broader advocacy efforts on behalf of this population.

We begin with a brief summary of historic policy and societal factors known to have contributed to the current day inequities, written in collaboration with content experts from RMAPI.

The core of this report is a fact sheet based on analysis of US Census data. Major findings include:

Finding 1: Families headed by unmarried parents are a significant segment of the city population and account for the majority of individuals living below the poverty level in the city.

Finding 2: Unmarried households with children experience lower incomes, lower rates of home ownership, and higher rent burdens compared to their married counterparts

Finding 3: Women and people of color are overrepresented among the heads of unmarried households with children.

Finding 4: Four in ten unmarried householders with children have less than a high school education. Nearly 80 percent of those without a high school education are in poverty.

Finding 5: Unmarried householders with children in poverty are more likely to be disabled or face other common barriers to employment.

Finding 6: The more adults present in unmarried households with children, the less likely that household is to be in poverty. This trend amplifies when considering the number of employed adults.

Finding 7: Unmarried parents under age 40 head the majority of all households with children in Rochester. Younger householders correlate with higher poverty rates regardless of marriage status.

Finding 8: A birth before age 20, being unmarried, and having not completed high school education are three factors that, when compounded, are associated with poor economic outcomes.

Finding 9: The highest densities of unmarried householders with children are clustered in the highest poverty neighborhoods in the city of Rochester

We end with a discussion of the gaps in available data, acknowledging that there is room for further investigation and interpretation, data collection, and insights. We recommend readers to think critically about what is presented and how it might impact their own work in poverty reduction efforts. We present a series of questions that are a jumping off point for new inquiry and reflection. Methodology can be found in the Appendix.

Data Source:2017 Census American Community Survey 5-Year Estimates, Public Microdata SampleData and documentation can be accessed here:https://www.census.gov/programs-surveys/acs/data/pums.html

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 2019 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 tractFIP19: 2019 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2019) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT10FIP19CSA: 2010 census tract with 2019 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.POP19_AGE_0_4: 2019 population 0 to 4 years oldPOP19_AGE_5_9: 2019 population 5 to 9 years old POP19_AGE_10_14: 2019 population 10 to 14 years old POP19_AGE_15_17: 2019 population 15 to 17 years old POP19_AGE_18_19: 2019 population 18 to 19 years old POP19_AGE_20_44: 2019 population 20 to 24 years old POP19_AGE_25_29: 2019 population 25 to 29 years old POP19_AGE_30_34: 2019 population 30 to 34 years old POP19_AGE_35_44: 2019 population 35 to 44 years old POP19_AGE_45_54: 2019 population 45 to 54 years old POP19_AGE_55_64: 2019 population 55 to 64 years old POP19_AGE_65_74: 2019 population 65 to 74 years old POP19_AGE_75_84: 2019 population 75 to 84 years old POP19_AGE_85_100: 2019 population 85 years and older POP19_WHITE: 2019 Non-Hispanic White POP19_BLACK: 2019 Non-Hispanic African AmericanPOP19_AIAN: 2019 Non-Hispanic American Indian or Alaska NativePOP19_ASIAN: 2019 Non-Hispanic Asian POP19_HNPI: 2019 Non-Hispanic Hawaiian Native or Pacific IslanderPOP19_HISPANIC: 2019 HispanicPOP19_MALE: 2019 Male POP19_FEMALE: 2019 Female POV19_WHITE: 2019 Non-Hispanic White below 100% Federal Poverty Level POV19_BLACK: 2019 Non-Hispanic African American below 100% Federal Poverty Level POV19_AIAN: 2019 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV19_ASIAN: 2019 Non-Hispanic Asian below 100% Federal Poverty Level POV19_HNPI: 2019 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV19_HISPANIC: 2019 Hispanic below 100% Federal Poverty Level POV19_TOTAL: 2019 Total population below 100% Federal Poverty Level POP19_TOTAL: 2019 Total PopulationAREA_SQMIL: Area in square milePOP19_DENSITY: Population per square mile.POV19_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, 2019. 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.

This map symbolizes the relative percentages of adults living below the poverty level for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2018 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate a higher level of adults living below the poverty level.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2014-2018 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.

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 2021 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 tractFIP21: 2021 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2021) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP21CSA: 2020 census tract with 2021 city FIPs for incorporated cities, 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.POP21_AGE_0_4: 2021 population 0 to 4 years oldPOP21_AGE_5_9: 2021 population 5 to 9 years old POP21_AGE_10_14: 2021 population 10 to 14 years old POP21_AGE_15_17: 2021 population 15 to 17 years old POP21_AGE_18_19: 2021 population 18 to 19 years old POP21_AGE_20_44: 2021 population 20 to 24 years old POP21_AGE_25_29: 2021 population 25 to 29 years old POP21_AGE_30_34: 2021 population 30 to 34 years old POP21_AGE_35_44: 2021 population 35 to 44 years old POP21_AGE_45_54: 2021 population 45 to 54 years old POP21_AGE_55_64: 2021 population 55 to 64 years old POP21_AGE_65_74: 2021 population 65 to 74 years old POP21_AGE_75_84: 2021 population 75 to 84 years old POP21_AGE_85_100: 2021 population 85 years and older POP21_WHITE: 2021 Non-Hispanic White POP21_BLACK: 2021 Non-Hispanic African AmericanPOP21_AIAN: 2021 Non-Hispanic American Indian or Alaska NativePOP21_ASIAN: 2021 Non-Hispanic Asian POP21_HNPI: 2021 Non-Hispanic Hawaiian Native or Pacific IslanderPOP21_HISPANIC: 2021 HispanicPOP21_MALE: 2021 Male POP21_FEMALE: 2021 Female POV21_WHITE: 2021 Non-Hispanic White below 100% Federal Poverty Level POV21_BLACK: 2021 Non-Hispanic African American below 100% Federal Poverty Level POV21_AIAN: 2021 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV21_ASIAN: 2021 Non-Hispanic Asian below 100% Federal Poverty Level POV21_HNPI: 2021 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV21_HISPANIC: 2021 Hispanic below 100% Federal Poverty Level POV21_TOTAL: 2021 Total population below 100% Federal Poverty Level POP21_TOTAL: 2021 Total PopulationAREA_SQMIL: Area in square milePOP21_DENSITY: Population per square mile.POV21_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 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, 2021. 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.

As of 2023, the region's average share of the indigenous population living under the poverty line was 42.3 percent. The most recent data for Colombia positions the country with 63.5 percent of the population, the highest in Latin America.

A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that 1 in 7 children born in large American cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were substantial across all cities and demographic groups studied, but children from disadvantaged backgrounds were most likely to experience eviction. Among those born into deep poverty, we estimate that about 1 in 4 were evicted by age 15. Given prior evidence that forced moves have negative consequences for children, we conclude that the high prevalence and social stratification of housing eviction are sufficient to play an important role in the reproduction of poverty and warrant greater policy attention.

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 2018 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 tractFIP18: 2018 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2018) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT10FIP18CSA: 2010 census tract with 2018 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.POP18_AGE_0_4: 2018 population 0 to 4 years oldPOP18_AGE_5_9: 2018 population 5 to 9 years old POP18_AGE_10_14: 2018 population 10 to 14 years old POP18_AGE_15_17: 2018 population 15 to 17 years old POP18_AGE_18_19: 2018 population 18 to 19 years old POP18_AGE_20_44: 2018 population 20 to 24 years old POP18_AGE_25_29: 2018 population 25 to 29 years old POP18_AGE_30_34: 2018 population 30 to 34 years old POP18_AGE_35_44: 2018 population 35 to 44 years old POP18_AGE_45_54: 2018 population 45 to 54 years old POP18_AGE_55_64: 2018 population 55 to 64 years old POP18_AGE_65_74: 2018 population 65 to 74 years old POP18_AGE_75_84: 2018 population 75 to 84 years old POP18_AGE_85_100: 2018 population 85 years and older POP18_WHITE: 2018 Non-Hispanic White POP18_BLACK: 2018 Non-Hispanic African AmericanPOP18_AIAN: 2018 Non-Hispanic American Indian or Alaska NativePOP18_ASIAN: 2018 Non-Hispanic Asian POP18_HNPI: 2018 Non-Hispanic Hawaiian Native or Pacific IslanderPOP18_HISPANIC: 2018 HispanicPOP18_MALE: 2018 Male POP18_FEMALE: 2018 Female POV18_WHITE: 2018 Non-Hispanic White below 100% Federal Poverty Level POV18_BLACK: 2018 Non-Hispanic African American below 100% Federal Poverty Level POV18_AIAN: 2018 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV18_ASIAN: 2018 Non-Hispanic Asian below 100% Federal Poverty Level POV18_HNPI: 2018 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV18_HISPANIC: 2018 Hispanic below 100% Federal Poverty Level POV18_TOTAL: 2018 Total population below 100% Federal Poverty Level POP18_TOTAL: 2018 Total PopulationAREA_SQMIL: Area in square milePOP18_DENSITY: Population per square mile.POV18_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, 2019. 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.

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 2014 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 tractFIP14: 2014 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2014) CT10FIP14: 2010 census tract with 2014 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.POP14_AGE_0_4: 2014 population 0 to 4 years oldPOP14_AGE_5_9: 2014 population 5 to 9 years old POP14_AGE_10_14: 2014 population 10 to 14 years old POP14_AGE_15_17: 2014 population 15 to 17 years old POP14_AGE_18_19: 2014 population 18 to 19 years old POP14_AGE_20_44: 2014 population 20 to 24 years old POP14_AGE_25_29: 2014 population 25 to 29 years old POP14_AGE_30_34: 2014 population 30 to 34 years old POP14_AGE_35_44: 2014 population 35 to 44 years old POP14_AGE_45_54: 2014 population 45 to 54 years old POP14_AGE_55_64: 2014 population 55 to 64 years old POP14_AGE_65_74: 2014 population 65 to 74 years old POP14_AGE_75_84: 2014 population 75 to 84 years old POP14_AGE_85_100: 2014 population 85 years and older POP14_WHITE: 2014 Non-Hispanic White POP14_BLACK: 2014 Non-Hispanic African AmericanPOP14_AIAN: 2014 Non-Hispanic American Indian or Alaska NativePOP14_ASIAN: 2014 Non-Hispanic Asian POP14_HNPI: 2014 Non-Hispanic Hawaiian Native or Pacific IslanderPOP14_HISPANIC: 2014 HispanicPOP14_MALE: 2014 Male POP14_FEMALE: 2014 Female POV14_WHITE: 2014 Non-Hispanic White below 100% Federal Poverty Level POV14_BLACK: 2014 Non-Hispanic African American below 100% Federal Poverty Level POV14_AIAN: 2014 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV14_ASIAN: 2014 Non-Hispanic Asian below 100% Federal Poverty Level POV14_HNPI: 2014 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV14_HISPANIC: 2014 Hispanic below 100% Federal Poverty Level POV14_TOTAL: 2014 Total population below 100% Federal Poverty Level POP14_TOTAL: 2014 Total PopulationAREA_SQMIL: Area in square milePOP14_DENSITY: Population per square mile.POV14_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, 2014. 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.

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.

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 2023 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries as of July 1, 2023. 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 Fields:CT20: 2020 Census tractFIP22: 2023 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2023) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP23CSA: 2020 census tract with 2023 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.POP23_AGE_0_4: 2023 population 0 to 4 years oldPOP23_AGE_5_9: 2023 population 5 to 9 years old POP23_AGE_10_14: 2023 population 10 to 14 years old POP23_AGE_15_17: 2022 population 15 to 17 years old POP23_AGE_18_19: 2023 population 18 to 19 years old POP23_AGE_20_44: 2023 population 20 to 24 years old POP23_AGE_25_29: 2023 population 25 to 29 years old POP23_AGE_30_34: 2023 population 30 to 34 years old POP23_AGE_35_44: 2023 population 35 to 44 years old POP23_AGE_45_54: 2023 population 45 to 54 years old POP23_AGE_55_64: 2023 population 55 to 64 years old POP23_AGE_65_74: 2023 population 65 to 74 years old POP23_AGE_75_84: 2023 population 75 to 84 years old POP23_AGE_85_100: 2023 population 85 years and older POP23_WHITE: 2023 Non-Hispanic White POP23_BLACK: 2023 Non-Hispanic African AmericanPOP23_AIAN: 2023 Non-Hispanic American Indian or Alaska NativePOP23_ASIAN: 2023 Non-Hispanic Asian POP23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific IslanderPOP23_HISPANIC: 2023 HispanicPOP23_MALE: 2023 Male POP23_FEMALE: 2023 Female POV23_WHITE: 2023 Non-Hispanic White below 100% Federal Poverty Level POV23_BLACK: 2023 Non-Hispanic African American below 100% Federal Poverty Level POV23_AIAN: 2023 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV23_ASIAN: 2023 Non-Hispanic Asian below 100% Federal Poverty Level POV23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV23_HISPANIC: 2023 Hispanic below 100% Federal Poverty Level POV23_TOTAL: 2023 Total population below 100% Federal Poverty Level POP23_TOTAL: 2023 Total PopulationAREA_SQMil: Area in square mile.POP23_DENSITY: 2023 Population per square mile.POV23_PERCENT: 2023 Poverty rate/percentage.How this data created?Population by age groups, ethnic groups and 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. Notes:1. Population and poverty data estimated as of July 1, 2023. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundaries are as of July 1, 2023.

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.

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 2015 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 tractFIP15: 2015 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2015) CT10FIP15: 2010 census tract with 2015 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.POP15_AGE_0_4: 2015 population 0 to 4 years oldPOP15_AGE_5_9: 2015 population 5 to 9 years old POP15_AGE_10_14: 2015 population 10 to 14 years old POP15_AGE_15_17: 2015 population 15 to 17 years old POP15_AGE_18_19: 2015 population 18 to 19 years old POP15_AGE_20_44: 2015 population 20 to 24 years old POP15_AGE_25_29: 2015 population 25 to 29 years old POP15_AGE_30_34: 2015 population 30 to 34 years old POP15_AGE_35_44: 2015 population 35 to 44 years old POP15_AGE_45_54: 2015 population 45 to 54 years old POP15_AGE_55_64: 2015 population 55 to 64 years old POP15_AGE_65_74: 2015 population 65 to 74 years old POP15_AGE_75_84: 2015 population 75 to 84 years old POP15_AGE_85_100: 2015 population 85 years and older POP15_WHITE: 2015 Non-Hispanic White POP15_BLACK: 2015 Non-Hispanic African AmericanPOP15_AIAN: 2015 Non-Hispanic American Indian or Alaska NativePOP15_ASIAN: 2015 Non-Hispanic Asian POP15_HNPI: 2015 Non-Hispanic Hawaiian Native or Pacific IslanderPOP15_HISPANIC: 2015 HispanicPOP15_MALE: 2015 Male POP15_FEMALE: 2015 Female POV15_WHITE: 2015 Non-Hispanic White below 100% Federal Poverty Level POV15_BLACK: 2015 Non-Hispanic African American below 100% Federal Poverty Level POV15_AIAN: 2015 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV15_ASIAN: 2015 Non-Hispanic Asian below 100% Federal Poverty Level POV15_HNPI: 2015 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV15_HISPANIC: 2015 Hispanic below 100% Federal Poverty Level POV15_TOTAL: 2015 Total population below 100% Federal Poverty Level POP15_TOTAL: 2015 Total PopulationAREA_SQMIL: Area in square milePOP15_DENSITY: Population per square mile.POV15_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, 2015. 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.

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 2017 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 tractFIP17: 2017 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2017) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT10FIP17CSA: 2010 census tract with 2017 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.POP17_AGE_0_4: 2017 population 0 to 4 years oldPOP17_AGE_5_9: 2017 population 5 to 9 years old POP17_AGE_10_14: 2017 population 10 to 14 years old POP17_AGE_15_17: 2017 population 15 to 17 years old POP17_AGE_18_19: 2017 population 18 to 19 years old POP17_AGE_20_44: 2017 population 20 to 24 years old POP17_AGE_25_29: 2017 population 25 to 29 years old POP17_AGE_30_34: 2017 population 30 to 34 years old POP17_AGE_35_44: 2017 population 35 to 44 years old POP17_AGE_45_54: 2017 population 45 to 54 years old POP17_AGE_55_64: 2017 population 55 to 64 years old POP17_AGE_65_74: 2017 population 65 to 74 years old POP17_AGE_75_84: 2017 population 75 to 84 years old POP17_AGE_85_100: 2017 population 85 years and older POP17_WHITE: 2017 Non-Hispanic White POP17_BLACK: 2017 Non-Hispanic African AmericanPOP17_AIAN: 2017 Non-Hispanic American Indian or Alaska NativePOP17_ASIAN: 2017 Non-Hispanic Asian POP17_HNPI: 2017 Non-Hispanic Hawaiian Native or Pacific IslanderPOP17_HISPANIC: 2017 HispanicPOP17_MALE: 2017 Male POP17_FEMALE: 2017 Female POV17_WHITE: 2017 Non-Hispanic White below 100% Federal Poverty Level POV17_BLACK: 2017 Non-Hispanic African American below 100% Federal Poverty Level POV17_AIAN: 2017 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV17_ASIAN: 2017 Non-Hispanic Asian below 100% Federal Poverty Level POV17_HNPI: 2017 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV17_HISPANIC: 2017 Hispanic below 100% Federal Poverty Level POV17_TOTAL: 2017 Total population below 100% Federal Poverty Level POP17_TOTAL: 2017 Total PopulationAREA_SQMIL: Area in square milePOP17_DENSITY: Population per square mile.POV17_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, 2017. 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.

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.

This study was undertaken to enable cross-community analysis of gang trends in all areas of the United States. It was also designed to provide a comparative analysis of social, economic, and demographic differences among non-metropolitan jurisdictions in which gangs were reported to have been persistent problems, those in which gangs had been more transitory, and those that reported no gang problems. Data were collected from four separate sources and then merged into a single dataset using the county Federal Information Processing Standards (FIPS) code as the attribute of common identification. The data sources included: (1) local police agency responses to three waves (1996, 1997, and 1998) of the National Youth Gang Survey (NYGS), (2) rural-urban classification and county-level measures of primary economic activity from the Economic Research Service (ERS) of the United States Department of Agriculture, (3) county-level economic and demographic data from the County and City Data Book, 1994, and from USA Counties, 1998, produced by the United States Department of Commerce, and (4) county-level data on access to interstate highways provided by Tom Ricketts and Randy Randolph of the University of North Carolina at Chapel Hill. Variables include the FIPS codes for state, county, county subdivision, and sub-county, population in the agency jurisdiction, type of jurisdiction, and whether the county was dependent on farming, mining, manufacturing, or government. Other variables categorizing counties include retirement destination, federal lands, commuting, persistent poverty, and transfer payments. The year gang problems began in that jurisdiction, number of youth groups, number of active gangs, number of active gang members, percent of gang members who migrated, and the number of gangs in 1996, 1997, and 1998 are also available. Rounding out the variables are unemployment rates, median household income, percent of persons in county below poverty level, percent of family households that were one-parent households, percent of housing units in the county that were vacant, had no telephone, or were renter-occupied, resident population of the county in 1990 and 1997, change in unemployment rates, land area of county, percent of persons in the county speaking Spanish at home, and whether an interstate highway intersected the county.

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.