The 2020-2021 School Neighborhood Poverty Estimates are based on school locations from the 2020-2021 Common Core of Data (CCD) school file and income data from families with children ages 5 to 17 in the U.S. Census Bureau’s 2017-2021 American Community Survey (ACS) 5-year collection. The ACS is a continuous household survey that collects social, demographic, economic, and housing information from the population in the United States each month. The Census Bureau calculates the income-to-poverty ratio (IPR) based on money income reported for families relative to the poverty thresholds, which are determined based on the family size and structure. Noncash benefits (such as food stamps and housing subsidies) are excluded, as are capital gains and losses. The IPR is the percentage of family income that is above or below the federal poverty level. The IPR indicator ranges from 0 to a top-coded value of 999. A family with income at the poverty threshold has an IPR value of 100. The estimates in this file reflect the IPR for the neighborhoods around schools which may be different from the neighborhood conditions of students enrolled in schools.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

VITAL SIGNS INDICATOR
Poverty (EQ5)

FULL MEASURE NAME
The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED
January 2023

DESCRIPTION
Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE
U.S Census Bureau: Decennial Census - http://www.nhgis.org
1980-2000

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2007-2021
Form C17002

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or non-cash benefits (such as public housing, Medicaid and food stamps).

For the national poverty level definitions by year, see: US Census Bureau Poverty Thresholds - https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

For an explanation on how the Census Bureau measures poverty, see: How the Census Bureau Measures Poverty - https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html.

American Community Survey (ACS) 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

In 2019, the highest poverty rate in Romania was registered in the North-East region of the country, at around ** percent, by 2022 it dropped to **** percent, and in 2023 it rose up to **** percent, making it the poorest region. By contrast, the north-western part of Romania had the lowest poverty rate in the country, at **** percent.

Graph and download economic data for Estimated Percent of People of All Ages in Poverty for United States (PPAAUS00000A156NCEN) from 1989 to 2023 about child, poverty, percent, and USA.

Poverty Area MeasuresThis data product provides poverty area measures for counties across 50 States and Washington DC. The measures include indicators of high poverty areas, extreme poverty areas, persistent poverty areas, and enduring poverty areas for Decennial Census years 1960–2000 and for American Community Survey (ACS) 5-year periods spanning both 2007–11 and 2015–19.HighlightsThis data product uniquely provides poverty area measures at the census-tract level for decennial years 1970 through 2000 and 5-year periods spanning 2007–11 and 2015–19.The poverty area measure—enduring poverty—is introduced, which captures the entrenchment of high poverty in counties for Decennial Census years 1960–2000 and for ACS 5-year periods spanning 2007–11 and 2015–19. The same is available for census tracts beginning in 1970.High and extreme poverty area measures are provided for various data years, offering end-users the flexibility to adjust persistent poverty area measures to meet their unique needs.All measures are geographically standardized to allow for direct comparison over time and for census tracts within county analysis.Diverse geocoding is provided, which can be used for mapping/GIS applications, to link to supplemental data (e.g., USDA, Economic Research Service’s Atlas of Rural and Small-Town America), and to explore various spatial categories (e.g., regions and metro/nonmetro status). DefinitionsHigh poverty: areas with a poverty rate of 20.0 percent or more in a single time period.Extreme poverty: areas with a poverty rate of 40.0 percent or more in a single time period.Persistent poverty: areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods).Enduring poverty: areas with a poverty rate of 20.0 percent or more for at least 5 consecutive time periods, about 10 years apart, spanning approximately 40 years or more (baseline time period plus four or more evaluation time periods).Additional information about the measures can be found in the downloadable Excel file, which includes the documentation, data, and codebook for the poverty area measures (county and tract).The next update to this data product—planned for early 2023—is expected to include the addition of poverty area measures for the 5-year period 2017–21.Data SetLast UpdatedNext UpdatePoverty area measures (in CSV format)11/10/2022Poverty area measures11/10/2022Poverty Area MeasuresOverviewBackground and UsesERS's Legacy of Poverty Area MeasurementDocumentationDescriptions and MapsLast updated: Thursday, November 10, 2022For more information, contact: Tracey Farrigan and Austin SandersRecommended CitationU.S. Department of Agriculture, Economic Research Service. Poverty Area Measures, November 2022.

The U.S. Census Bureau's Small Area Income and Poverty Estimates (SAIPE) program provides annual estimates of income and poverty statistics for all school districts, counties, and states. The main objective of this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. In order to implement provisions under Title I of the Elementary and Secondary Education Act as amended, we produce total population, number of children ages 5 to 17, and number of related children ages 5 to 17 in families in poverty estimates for school districts.

Source: U.S. Census Bureau; American Community Survey, 2019-2023 American Community Survey 5-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; https://data.census.gov/cedsci; (1 April 2025).

The Poverty Mapping Project: Small Area Estimates of Poverty and Inequality data set consists of consumption-based poverty, inequality and related measures for subnational administrative Units in approximately twenty countries throughout Africa, Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the original data providers into a unified spatially referenced and globally consistent data set. The data products include shapefiles (vector data), tabular data sets (csv format), and centroids (csv file with latitude and longitude of a geographic Unit and associated poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with a number of external data providers.

Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Moving to Opportunity (MTO) is a randomized housing experiment administered by the United States Department of Housing and Urban Development that gave low-income families living in high-poverty areas in five cities the chance to move to lower-poverty areas. Families were randomly assigned to one of three groups: (1) The experimental group (also called the low-poverty voucher (LPV) group) received Section 8 rental assistance certificates or vouchers that they could use only in census tracts with 1990 poverty rates below 10 percent. The families received mobility counseling and help in leasing a new unit. One year after relocating, families could use their voucher to move again if they wished, without any special constraints on location. (2) The Section 8 group (also called the traditional voucher (TRV) group) received regular Section 8 certificates or vouchers that they could use anywhere; these families received no special mobility counseling. (3) The control group received no certificates or vouchers through MTO, but continued to be eligible for project-based housing assistance and whatever other social programs and services to which they would otherwise be entitled. Families were tracked from baseline (1994-98) through the long-term evaluation survey fielding period (2008-10) with the purpose of determining the effects of "neighborhood" on participating families. This data collection contains data from the 3,273 adult interviews completed as part of the MTO long-term evaluation and are comprised of adult variables that have been analyzed. Using data from the long-term evaluation, the associated article reports that moving from a high-poverty to lower-poverty neighborhood leads to long-term (10- to 15-year) improvements in adult physical and mental health and subjective well-being, despite not affecting economic self-sufficiency. The data contain all adult outcomes and mediators analyzed for the associated article as well as a variety of demographic and other baseline measures that were controlled for in the analysis.

This dataset contains a selection of 27 indicators of public health significance by Chicago community area, with the most updated information available. The indicators are rates, percents, or other measures related to natality, mortality, infectious disease, lead poisoning, and economic status. See the full description at https://data.cityofchicago.org/api/assets/BB7058D2-E8A1-4E11-86CE-6CF1738F0A02.

CHILDREN_POVERTY_2013_USCB_IN.SHP is a polygon shapefile showing 2013 census data showing percentages of children in poverty for each 2013-2014 school district within Indiana. Poverty data were provided by personnel of the Indiana Business Research Center (Rachel Strange, Geodemographic Analyst, Managing Editor, IBRC), which were obtained from the Web page of the U. S. Department of Commerce, U. S. Census Bureau, titled "Small Area Income and Poverty Estimates," http://www.census.gov/did/www/saipe/data/interactive/#. Discussion of these data, which are estimates produced under the Census Bureau's Small Area Income and Poverty Estimates (SAIPE) program, are provided at http://www.census.gov/did/www/saipe/about/index.html. The following is excerpted from metata of the U.S. Census Bureau (2013-2014 School Districts) and also from the Web page of the SAIPE program ( http://www.census.gov/did/www/saipe/downloads/sd13/README.txt ) : "School Districts are single-purpose administrative units within which local officials provide public educational services for the area's residents. The Census Bureau obtains school district boundaries, names, local education agency codes, grade ranges, and school district levels biennially from state school officials. The Census Bureau collects this information for the primary purpose of providing the U.S. Department of Education with annual estimates of the number of children in poverty within each school district, county, and state. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts. "The 2014 TIGER/Line Shapefiles include separate shapefiles for elementary, secondary, and unified school districts. The 2014 shapefiles contain information from the 2013-2014 school year. The 2013-2014 school districts represent districts in operation as of January 1, 2014. "The elementary school districts provide education to the lower grade/age levels and the secondary school districts provide education to the upper grade/age levels. The unified school districts are districts that provide education to children of all school ages. In general, where there is a unified school district, no elementary or secondary school district exists (see exceptions described below), and where there is an elementary school district the secondary school district may or may not exist (see explanation below). "The U.S. Census Bureau's Small Area Income and Poverty Estimates (SAIPE) program provides annual estimates of income and poverty statistics for all school districts, counties, and states. The main objective of this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. "The SAIPE program produces the following county and state estimates: Total number of people in poverty. Number of childer under age 5 in poevery (for states only). â¢number of related children ages 5 to 17 in families in poverty. Number of children under age 18 in poverty. Median household income."

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

The EDGE School Neighborhood Poverty Estimates rely on household economic data from the Census Bureau’s American Community Survey (ACS) and public school point locations developed by NCES to estimate the income-to-poverty ratio for neighborhoods around school buildings. Unlike neighborhood poverty estimates created from survey responses collected for predefined geographic areas like census tracts, Spatially Interpolated Demographic Estimates (SIDE) predict conditions at specific point locations based on the survey responses nearest to those locations. This approach allows SIDE estimates to extract new value from existing data sources to provide indicators of neighborhood conditions. The economic conditions of school neighborhoods may be different from the economic conditions in neighborhoods where students live. However, the economic condition of the neighborhood around a school may impact schools, just as the condition of neighborhood schools may impact local neighborhoods. The school neighborhood poverty estimates provide an additional indicator to help identify these local conditions.

Abstract

The main objective of the 2019 Chattogram for Low Income Area Gender, Inclusion, and Poverty (CITY) study is to collect primary data from male and female residents in slum and non-slum poor neighborhoods in Chattogram, the second largest city of Bangladesh, and build the evidence base about their constraints to access more and better jobs. The CITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh as well as to identify key constraints and solutions for low-income women trying to obtain better jobs.

A broad array of information was collected on issues related to women's economic empowerment, ranging from demographic and socioeconomic characteristics to detailed work history, time use, attitudes about work, and perceptions of work. The key feature of this survey is to collect economic data directly from the main household members, generally the main couples, unlike traditional surveys which only interviewed the heads of households (who tend to be men in most cases); thus, failed to gather valuable information from the female population.

Geographic coverage

Poor areas of slum & non-slum areas of Chattogram, the second largest city of Bangladesh.

Analysis unit

Household, individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The CITY 2019 survey was designed using a two-stage sampling strategy. The major features include the following steps:

FIRST STAGE: The primary sampling units (PSUs) in the first stage were selected using a probability proportional to size (PPS) methods. Using the 2011 census sampling frame, low-income PSUs were defined as non-slum census enumeration areas (EAs) using the 2011 Bangladesh Poverty Map. Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio less than 10%; the second stratum between 11% and 14%; and the third stratum, those exceeding 15%. Overall, 22 low-income EAs were selected in the Chattogram City Corporation (CC).

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Based on the sizes of the slums, three strata were used for sampling purposes. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, more than 100 households. Small slums with fewer than 50 households were not included in the sampling frame. Overall, 18 slums were included as a part of the survey.

SECOND STAGE: The second stage of the selection process in each of the EAs began with a listing exercise. For very large EAs, a smaller section was delineated for the listing. The second level of stratification are defined as follows:

i) Households with both working-age male and female members; ii) Households with only a working-age female; iii) Households with only a working-age male.

Households were randomly selected from each stratum with the predetermined ratio of 16:3:1. Overall, data was collected from 805 households (1289 individuals - 580 in slum and 709 in non-slum areas).

Sampling deviation

For EAs where the ratio was unable to be attained due to absence of households in certain strata, households from the first category to arrive at a final number of 20 per EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

77%

In 2022, an estimated population of ** million people in Nigeria lived in extreme poverty, the majority in rural areas. The count of people living on less than **** U.S. dollars a day in rural regions reached **** million, while around *** million extremely poor people were located in urban areas. Overall, throughout the period examined, the poverty incidence remained above ** million in rural communities.

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

This indicator provides information about the percentage of the population living in census tracts identified as having persistent poverty. The ACS defined persistent poverty as having a poverty rate of 20% or higher over a 30-year period using the 1990 and 2000 Censuses and the 2005–2009 and 2015–2019 ACS 5-year estimates. A census tract is characterized as a persistent poverty census tract if 20% or more of its residents are living below the federal poverty level over an extended period.Areas of persistent poverty may have limited access to healthcare services, quality education, and healthy and affordable food. These communities may also face unique social and structural challenges that relate to poverty. Identifying areas of persistent poverty and the population living in these areas can help streamline resource allocation to communities that may benefit the most from additional investments and support.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset identifies poverty status by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The poverty characteristics include race, sex, age, educational attainment, employment status, work experience, and poverty level. JSL enriched it with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.

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 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.