Graph and download economic data for Estimated Percent of People of All Ages in Poverty for New York County, NY (PPAANY36061A156NCEN) from 1989 to 2023 about New York County, NY; New York; child; poverty; NY; percent; and USA.

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

American Community Survey Public Use Micro Sample, augmented by NYC Opportunity.

Graph and download economic data for 90% Confidence Interval Upper Bound of Estimate of Median Household Income for New York County, NY (MHICIUBNY36061A052NCEN) from 1989 to 2023 about New York County, NY; New York; NY; households; median; income; and USA.

American Community Survey Public Use Micro Sample, augmented by NYC Opportunity.

Graph and download economic data for Estimate of Median Household Income for New York (MHINY36000A052NCEN) from 1989 to 2023 about NY, households, median, income, and USA.

In 2021, Philadelphia, Pennsylvania was the city with the highest poverty rate of the United States' most populated cities. In this statistic, the cities are sorted by poverty rate, not population. The most populated city in 2021 according to the source was New York city - which had a poverty rate of 18 percent.

Graph and download economic data for Poverty Universe, All Ages for Nassau County, NY (PUAANY36059A647NCEN) from 1998 to 2023 about Nassau County, NY; New York; NY; poverty; and USA.

U.S. Census Bureau QuickFacts statistics for New York city, New York. 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.

Graph and download economic data for Estimate of Median Household Income for Bronx County, NY (MHINY36005A052NCEN) from 1989 to 2023 about Bronx County, NY; New York; NY; households; median; income; and USA.

Graph and download economic data for 90% Confidence Interval Lower Bound of Estimate of Median Household Income for Bronx County, NY (MHICILBNY36005A052NCEN) from 1989 to 2023 about Bronx County, NY; New York; NY; households; median; income; and USA.

This dataset gives NYC Equitable Zoning (NYCEZ), which is a zoning system of NYC derived from census tracts and ACS data with 574 zones.

The zoning system considers data reliability of 3 minority population groups: population below poverty level, seniors above 67, and long commuters (>1 hour). Underserved groups of interest include the population above 67 years old (seniors), the population under the poverty level, the population with a commute time above one hour, and the population with one or more disabilities. Only the former three groups are considered in zoning, since populations disabilities are already highly correlated with the others.

The 2168 census tracts in NYC are aggregated to improve the data reliability of the 3 minority groups. Average margin of error (MOE) percentages at census tract level of population above 67, population below poverty level, and population with a commute time above 1 hour are 15.22%, 50.07%, and 18.23%, respectively. After aggregation to the NYC Equitable Zones, MOE percentages become 8.02%, 12.33%, and 9.88%, respectively. Equitable Zones shown in Figure 5 simultaneously reduces the average MOE percentage of demographic data by 48% for seniors, 75% for low-income population, and 46% for long commuters.

Files include:

• NYC census tracts shapefile with mapping to NYCEZ (“zoning” column)
  • equitable_zoning_new_dissol.cpg
  • equitable_zoning_new_dissol.dbf
  • equitable_zoning_new_dissol.prj
  • equitable_zoning_new_dissol.sbn
  • equitable_zoning_new_dissol.sbx
  • equitable_zoning_new_dissol.shp
  • equitable_zoning_new_dissol.shx
• NYCEZ shapefile
  • equitable_zoning_new.cpg
  • equitable_zoning_new.dbf
  • equitable_zoning_new.prj
  • equitable_zoning_new.shp
  • equitable_zoning_new.shx
• Data used for NYCEZ generation (from American Community Survey (ACS))
  • Number of seniors in each census tract (with 80 variance replicate estimates)
    • data_elderly.csv
  • Number of disabled in each census tract (with 80 variance replicate estimates)
    • data_disabled.csv
  • Number of long commuters in each census tract (with 80 variance replicate estimates)
    • data_commute>1h.csv
  • Number of low incomers in each census tract (with 80 variance replicate estimates)
    • data_below_poverty.csv

Variance replicate estimates from ACS are used to MOE aggregation. Information can be found here: https://www.census.gov/programs-surveys/acs/data/variance-tables.html

Graph and download economic data for 90% Confidence Interval Lower Bound of Estimate of Median Household Income for Suffolk County, NY (MHICILBNY36103A052NCEN) from 1989 to 2023 about Suffolk County, NY; New York; NY; households; median; income; and USA.

U.S. Census Bureau QuickFacts statistics for Manhattan village, Illinois. 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.

Demographic and income disparities may impact food accessibility. Research has not yet well documented the precise location of healthy and unhealthy food resources around children’s homes and schools. The objective of this study was to examine the food environment around homes and schools for all public school children, stratified by race/ethnicity and poverty status. This cross-sectional study linked data on the exact home and school addresses of a population-based sample of public school children in New York City from 2013 to all corner stores, supermarkets, fast-food restaurants, and wait-service restaurants. Two measures were created around these addresses for all children: 1) distance to the nearest outlet, and 2) count of outlets within 0.25 miles. The total analytic sample included 789,520 K-12 graders. The average age was 11.78 years (SD ± 4.0 years). Black, Hispanic, and Asian students live and attend schools closer to nearly all food outlet types than White students, regardless of poverty status. Among not low-income students, Black, Hispanic, and Asian students were closer from home and school to corner stores and supermarkets, and had more supermarkets around school than White students. The context in which children live matters, and more nuanced data is important for development of appropriate solutions for childhood obesity. Future research should examine disparities in the food environment in other geographies and by other demographic characteristics, and then link these differences to health outcomes like body mass index. These findings can be used to better understand disparities in food access and to help design policies intended to promote healthy eating among children.

U.S. Census Bureau QuickFacts statistics for Manhattan city, Kansas. 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.

The poly shapefile shows income levels needed to support a family of four living in the high-cost of living areas within the states. It also shows percent income above the Federal poverty levels ($20,650 = 100%)

"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

Graph and download economic data for 90% Confidence Interval Lower Bound of Estimate of Median Household Income for Putnam County, NY (MHICILBNY36079A052NCEN) from 1989 to 2023 about Putnam County, NY; New York; NY; households; median; income; and USA.

This data comes from the US Census and is illustrated through margin of error, percent of those below the poverty line, and rank of states with the worst senior poverty.