This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certain
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Madison County, NY (S1701ACS036053) from 2012 to 2023 about Madison County, NY; Syracuse; NY; percent; poverty; 5-year; population; and USA.
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Percent of Population Below the Poverty Level (5-year estimate) in New York County, NY was 15.80% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in New York County, NY reached a record high of 17.90 in January of 2015 and a record low of 15.60 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in New York County, NY - last updated from the United States Federal Reserve on June of 2025.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Nassau County, NY (S1701ACS036059) from 2012 to 2023 about Nassau County, NY; New York; NY; percent; poverty; 5-year; population; and USA.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Erie County, NY (S1701ACS036029) from 2012 to 2023 about Erie County, NY; Buffalo; NY; percent; poverty; 5-year; population; and USA.
In 2023, about 14.2 percent of New York's population lived below the poverty line. This accounts for persons or families whose collective income in the preceding 12 months was below the national poverty level of the United States. The poverty rate of the United States can be found here.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Steuben County, NY (S1701ACS036101) from 2012 to 2023 about Steuben County, NY; NY; percent; poverty; 5-year; population; and USA.
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Under 5 years Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Putnam County, New York by age, education, race, gender, work experience and more.
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.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Suffolk County, NY (S1701ACS036103) from 2012 to 2023 about Suffolk County, NY; New York; NY; percent; poverty; 5-year; population; and USA.
"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100. Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports. In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the g
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Under 18 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Nedrow, New York by age, education, race, gender, work experience and more.
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Under 18 years Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Kings County, New York by age, education, race, gender, work experience and more.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Albany County, NY (S1701ACS036001) from 2012 to 2023 about Albany County, NY; Albany; NY; percent; poverty; 5-year; population; and USA.
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Under 5 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Bayport, New York by age, education, race, gender, work experience and more.
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Under 18 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Baywood, New York by age, education, race, gender, work experience and more.
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Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in Putnam County, NY (S1701ACS036079) from 2012 to 2023 about Putnam County, NY; New York; NY; percent; poverty; 5-year; population; and USA.
ESD provides capital grant funding from the Regional Council Capital Fund available for the State’s Regional Economic Development Council Initiative, which helps drive regional and local economic development across New York State in cooperation with ten Regional Economic Development Councils (“Regional Councils”). Capital grant funding is available for capital-based economic development projects intended to create or retain jobs; prevent, reduce or eliminate unemployment and underemployment; and/or increase business or economic activity in a community or Region. One of the program categories within the program will provide enhanced incentives for projects located in economically distressed areas (census tracts) where investments are needed to spur economic growth. The definition of economically distressed areas (census tracts) can be found below.
For more information and full program guidelines, please see the full program guidelines within the 2025 Available Resources at: https://regionalcouncils.ny.gov/
Economically distressed area shall mean the following based on the census tract for where the project is located:
Severely
distressed census tracts shall have at least 25 households receiving public
assistance income in the 2023 ACS 5-year estimate and meet at least five of the
criteria listed below:Moderately
distressed census tracts shall have at least 25 households receiving public
assistance income in the 2023 ACS 5-year estimate and meet at least three of
the criteria listed below:Slightly
distressed census tracts shall have at least 100 households receiving public
assistance income in the 2023 ACS 5-year estimate and meet at least two of the
criteria listed below:o
Population
loss between the 2023 ACS 5-year estimate and the 2019 ACS 5-year estimate – an
absolute loss in population.o
Unemployment
rate (2023 ACS 5-year estimate) higher than the State’s rate.o
Private
sector employment growth rate (2023 ACS 5-year estimate) over the preceding 5
years was lower than the State’s OR private sector employment (2023 ACS 5-year
estimate) as a percentage of total employment was less than the State’s.o
Percentage
of households receiving public assistance (2023 ACS 5-year estimate) was
greater than the statewide percentage.o
Poverty
rate (2023 ACS 5-year estimate) was greater than the State’s poverty rate.o
Per
Capita Income change (2023 ACS 5-year estimate) over the preceding five years
was less than the growth in the consumer price index (CPI) for all urban
consumers nationally OR per capita income was less than the State’s per capita
income.
Attributes:
Field Name
Data Type
Description
Census Tract
Number
The 11 digit geoid associated with each census tract in New York State. Census tracts are small, relatively permanent statistical subdivisions of a county that average about 4,000 inhabitants.
Stress Level
Number
The stress level number (1-4) associated with the census tract.
Stress Level Description
Text
The stress level description (Not Distressed, Slight Distress, Moderate Distress, Severe Distress) associated with the census tract.
Stress Level Color
Text
The stress level color (Gray, Light Orange, Dark Orange, Red) associated with the census tract.
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Under 18 years Poverty Rate Statistics for 2022. This is part of a larger dataset covering poverty in Lloyd Harbor, New York by age, education, race, gender, work experience and more.
This directory is for at-risk for HIV and eligible persons living with HIV in New York City seeking HIV medical and supportive services. The agencies and their listed programs receive CDC and Ryan White Part-A funding to provide: Targeted-Testing among Priority Populations, Food and Nutrition Services, Health Education and Risk Reduction Services, Harm Reduction Services, Legal Services, Mental Health Services, Case Management and Care Coordination Services, and Supportive Counseling Services. To be eligible to recieve these services, prospective clients must: 1)be HIV-positive; 2) have a total household income below 435% of the Federal Poverty Level (FPL) (this is the same as the income eligible guidelines for the New York State AIDS Drug Assistance Program (ADAP) and higher than the income eligiblity guidelines for Medicaid in New York State); and 3) reside in New York City or the counties of Westchester, Rockland, and Putnam. For providers, to make a referral, please contact the program directly using the information provided in the diretory (please be sure to call before directing clients to the program). When making a referral, you may also find it useful to talk to your client about executing a release of information form authorizing you to share confidential health and HIV-related information with another service provider in order to coordinate care (for more information, go to https://www.health.ny.gov/diseases/aids/providers/forms/informedconsent.htm).
This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certain