Historical dataset showing Puerto Rico poverty rate by year from N/A to N/A.

Porto Rico: Poverty ratio, percent living on less than 1.90 USD a day: Pour cet indicateur, La Banque mondiale fournit des données pour la Porto Rico de à . La valeur moyenne pour Porto Rico pendant cette période était de pour cent avec un minimum de pour cent en et un maximum de pour cent en .

Statistical data for Income-to-poverty Ratio Distribution in Ceiba Municipio, Puerto Rico (2023).

Goal and Objective : Primary objective is to study variability in the poverty rate in the US counties by means of one or more of independent or control variable and provide best suitable model to quantify relationships in determining target value Our goal is to design various models to take into consideration the effect of various factors like employment, population and education to predict the poverty rate in all US Counties We further wish to analyze the status of a county based on whether it is metropolitan or not

List of datasets:

Socioeconomic indicators like poverty rates, population change, unemployment rates, and education levels vary geographically across U.S. States and counties 1. Unemployment 2. PovertyEstimates 3.Population Estimates 4. Education

All the four individual datasets have common unique id FIPS Code defined as State-County FIPS Code. It is unique for each county falling under the states. In our dataset, we are covering all 52 USA states including federal district DC and Puerto Rico.

Data Modelling :

Target Variable: Metro_2015 – This binary variable shows status of County as Metro or Non-Metro A decision tree model designed using Metro_2015 as target variable will efficiently determine the classification of the population into Metro and Non-metro counties. Dataset will be partitioned into training and validation datasets before implementing decision tree rules. The attributes that will be considered in selecting best model will be fit statistics, misclassification rate, and average square error.

Clustering can be performed to create the collection of objects similar to each other which will give insight into data distribution. Variables will be standardized before performing clustering to avoid noisy data and outliers. Euclidean distance will be the measure to determine stability and separation.

Recommendation :

The regression equation determines % Poverty rate in a particular county based on significant factors. This model can be This model can be used by education boards to increase or decrease the funds spent on the education system in different counties in order to lower the poverty rate. Census board can use this model in identifying poverty line index based on a population estimate an average household income. By estimating the poverty rate and considering factors like unemployment and education, an analysis can be done to set up employment opportunities in targeted counties.

Statistical data for Income-to-poverty Ratio Distribution in Morovis Municipio, Puerto Rico (2023).

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer will be updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico.Census tracts with no population are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

This layer shows Population and Poverty Status. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of people whose income in the past 12 months is below poverty level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2018-2022ACS Table(s): B17017, C17002, DP02, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Statistical data for Income-to-poverty Ratio Distribution in Sabana Grande Municipio, Puerto Rico (2023).

The Department of Veterans Affairs provides official estimates and projections of the Veteran population using the Veteran Population Projection Model (VetPop). Based on the latest model VetPop2023 and the most recent national survey estimates from the 2023 American Community Survey 1-Year (ACS) data, the projected number of Veterans living in the 50 states, DC and Puerto Rico for fiscal years, 2023 to 2025, are allocated to Urban and Rural areas. As defined by the Census Bureau, Rural encompasses all population, housing, and territory not included within an Urban area (https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html).

This table contains the Veteran estimates by urban/rural, age group, poverty, and disability. The poverty level and disability are determined by ACS based on responses on total income and functional difficulties. Refer to the sections on Poverty and Disability Status in the document, https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2023_ACSSubjectDefinitions.pdf

Note: rounding to the nearest 1,000 is always appropriate for VetPop estimates.

U.S. Census Bureau QuickFacts statistics for Puerto Rico. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Long-term longitudinal dataset with information on generational links and socioeconomic and health conditions of individuals over time. The central foci of the data are economic and demographic, with substantial detail on income sources and amounts, wealth, savings, employment, pensions, family composition changes, childbirth and marriage histories, and residential location. Over the life of the PSID, the NIA has funded supplements on wealth, health, parental health and long term care, housing, and the financial impact of illness, thus also making it possible to model retirement and residential mobility. Starting in 1999, much greater detail on specific health conditions and health care expenses is included for respondent and spouse. Other enhancements have included a question series about emotional distress (2001); the two stem questions from the Composite International Diagnostic Interview to assess symptoms of major depression (2003); a supplement on philanthropic giving and volunteering (2001-03); a question series on Internet and computer use (2003); linkage to the National Death Index with cause of death information for more than 4,000 individuals through the 1997 wave, updated for each subsequent wave; social and family history variables and GIS-linked environmental data; basic data on pension plans; event history calendar methodology to facilitate recall of employment spells (2001). The reporting unit is the family: single person living alone or sharing a household with other non-relatives; group of people related by blood, marriage, or adoption; unmarried couple living together in what appears to be a fairly permanent arrangement. Interviews were conducted annually from 1968 through 1997; biennial interviewing began in 1999. There is an oversample of Blacks (30%). Waves 1990 through 1995 included a 20% Hispanic oversample; within the Hispanic oversample, Cubans and Puerto Ricans were oversampled relative to Mexicans. All data from 1994 through 2001 are available as public release files; prior waves can be obtained in archive versions. The special files with weights for families are also available. Restricted files include the Geocode Match File with information for 1968 through 2001, the 1968-2001 Death File, and the 1991 Medicare Claims File. * Dates of Study: 1968-2003 * Study Features: Longitudinal, Minority Oversampling * Sample Size: 65,000+ Links * ICPSR Series: http://www.icpsr.umich.edu/icpsrweb/ICPSR/series/00131 * ICPSR 1968-1999: Annual Core Data: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/07439 * ICPSR 1968-1999: Supplemental Files: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03202 * ICPSR 1989-1990: Latino Sample: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03203

Table from the American Community Survey (ACS) 5-year series on poverty and employment status related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B23025 Employment Status for the Population 16 years and over, B23024 Poverty Status by Disability Status by Employment Status for the Population 20 to 64 years, B17010 Poverty Status of Families by Family Type by Presence of Related Children under 18 years, C17002 Ratio of Income to Poverty Level in the Past 12 Months. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

These geospatial data resources and the linked mapping tool below reflect currently available data on three categories of potentially qualifying Low-Income communities: 1) Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1. 2) Census tracts that meet the White House's Climate and Economic Justice Screening Tool's threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography. 3) Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography. Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico. The excel tool provides the land area percentage of each 2023 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool. Additional information on this tax credit program can be found on the DOE Landing Page for the 48e program at https://www.energy.gov/diversity/low-income-communities-bonus-credit-program or the IRS Landing Page at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit. Maps last updated: September 1st, 2024 Next map update expected: December 7th, 2024 Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Program. Source Acknowledgements: 1. The New Market Tax Credit (NMTC) Tract layer using data from the 2016-2020 ACS is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program. 2. The 'Energy' Category Tract layer from the Climate and Economic Justice Screening Tool (CEJST) is created by the Council on Environmental Quality (CEQ) within the Executive Office of the President. To learn more, visit https://screeningtool.geoplatform.gov/en/. Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income. 3. The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. 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). Until Dec 7th, 2024 both the USDA estimates using 2007-2011 and 2017-2021 ACS 5-year data. On Dec 8th, 2024, only the USDA estimates using 2017-2021 data will be accepted for program eligibility.

BackgroundThe United States is experiencing a continuing crisis of gun violence, and economically marginalized and racially segregated inner-city areas are among the most affected. To decrease this violence, public health interventions must engage with the complex social factors and structural drivers—especially with regard to the clandestine sale of narcotics—that have turned the neighborhood streets of specific vulnerable subgroups into concrete killing fields. Here we present a mixed-methods ethnographic and epidemiological assessment of narcotics-driven firearm violence in Philadelphia’s impoverished, majority Puerto Rican neighborhoods.MethodsUsing an exploratory sequential study design, we formulated hypotheses about ethnic/racial vulnerability to violence, based on half a dozen years of intensive participant-observation ethnographic fieldwork. We subsequently tested them statistically, by combining geo-referenced incidents of narcotics- and firearm-related crime from the Philadelphia police department with census information representing race and poverty levels. We explored the racialized relationships between poverty, narcotics, and violence, melding ethnography, graphing, and Poisson regression.FindingsEven controlling for poverty levels, impoverished majority-Puerto Rican areas in Philadelphia are exposed to significantly higher levels of gun violence than majority-white or black neighborhoods. Our mixed methods data suggest that this reflects the unique social position of these neighborhoods as a racial meeting ground in deeply segregated Philadelphia, which has converted them into a retail endpoint for the sale of astronomical levels of narcotics.ImplicationsWe document racial/ethnic and economic disparities in exposure to firearm violence and contextualize them ethnographically in the lived experience of community members. The exceptionally concentrated and high-volume retail narcotics trade, and the violence it generates in Philadelphia’s poor Puerto Rican neighborhoods, reflect unique structural vulnerability and cultural factors. For most young people in these areas, the narcotics economy is the most readily accessible form of employment and social mobility. The performance of violence is an implicit part of survival in these lucrative, illegal narcotics markets, as well as in the overcrowded jails and prisons through which entry-level sellers cycle chronically. To address the structural drivers of violence, an inner-city Marshall Plan is needed that should include well-funded formal employment programs, gun control, re-training police officers to curb the routinization of brutality, reform of criminal justice to prioritize rehabilitation over punishment, and decriminalization of narcotics possession and low-level sales.

This study explored the lives of the working poor in the inner city. Three hundred male and female participants were drawn from central and west Harlem, New York City; 200 worked at one of four fast food restaurants in Harlem, and 100 had applied to one of those restaurants but were not hired. Participants were African American, Dominican and Puerto Rican of varied ages, most between 15 and 40 years of age. Educational status also varied, with the majority of participants' highest level of education being a high school degree. This study consists of three waves. The first wave was conducted in 1993-1994 with 300 participants. All 300 completed a survey, providing data on basic demographics (such as race, marital status, income, members of family, places where respondent has lived), as well as information on education, health care, and in-depth employment history. One-hundred fifty of these participants completed an extensive, semi-structured three to four hour interview telling their life history, covering topics such as family history; neighborhood identity; work history and aspirations; and race relations. Interviewers noted their impressions of the neighborhood and the physical appearance of the participant and her surroundings. The restaurant owners and managers were interviewed as well. Twelve of the participants agreed to be intensely studied; members of the research team worked alongside these participants at the fast food restaurants for four months, got to know their parents and children, and interviewed other key figures in their lives such as teachers and priests. The second wave was conducted in 1997-1998 with 100 of the original participants - some were employed, and some were unemployed. A survey was completed, addressing the same topics as the wave one survey. Interviews were conducted to ascertain life updates since wave one. The third wave was conducted in 2001-2002 with 40 of the 100 wave 2 participants. No more follow-up waves are planned. The Henry A. Murray Research Archives currently holds original record paper data, and audiotape data from waves 1 and 2 of this study.

Key Table Information.Table Title.Place of Birth by Poverty Status in the Past 12 Months in the United States.Table ID.ACSDT1Y2024.B06012.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states...

Key Table Information.Table Title.Poverty Status of Individuals in the Past 12 Months by Living Arrangement.Table ID.ACSDT1Y2024.B17021.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, ...