The population rate of change of Ellisburg, NJ was -1.70% in 2018. The population rate of change of Glen Head, NY was 0.40% in 2018.

Population

Population Change

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

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Demographics and Population Datasets Involving Glen Head, NY or Ellisburg, NJ

  • API

    NYCHA Resident Data Book Summary

    data.cityofnewyork.us | Last Updated 2020-02-08T00:56:30.000Z

    Contains resident demographic data at a summary level as of January 1, 2019. The Resident Data Book is compiled to serve as an information source for queries involving resident demographic as well as a source of data for internal analysis. Statistics are compiled via HUD mandated annual income reviews involving NYCHA Staff and residents. Data is then aggregated and compiled by development. Each record pertains to a single public housing development.

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    NYSERDA Low- to Moderate-Income New York State Census Population Analysis Dataset: Average for 2013-2015

    data.ny.gov | Last Updated 2019-11-15T22:30:02.000Z

    How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

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    Personal Income Tax Filers, Summary Dataset 4 - County-level Major Items and Income & Deduction Components by Wide Income Range: Beginning Tax Year 2015

    data.ny.gov | Last Updated 2024-08-08T13:02:59.000Z

    Beginning with tax year 2015, the Department of Taxation and Finance (hereafter “the Department”) began producing a new annual population data study file to provide more comprehensive statistical information on New York State personal income tax returns. The data are from full‐year resident, nonresident, and part‐year resident returns filed between January 1 and December 31 of the year after the start of the liability period (hereafter referred to as the “processing year”). The four datasets display major income tax components by tax year. This includes the distribution of New York adjusted gross income and tax liability by county or place of residence, as well as the value of deductions, exemptions, taxable income and tax before credits by size of income. In addition, three of the four datasets include all the components of income, the components of deductions, and the addition/subtraction modifications. Caution: The current datasets are based on population data. For tax years prior to 2015, data were based on sample data. Data customers are advised to use caution when drawing conclusions comparing data for tax years prior to 2015 and subsequent tax years. Further details are included in the Overview.

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    Personal Income Tax Filers, Summary Dataset 1 - Major Items by Liability Status and Place of Residence: Beginning Tax Year 2015

    data.ny.gov | Last Updated 2024-08-08T13:00:30.000Z

    Beginning with tax year 2015, the Department of Taxation and Finance (hereafter “the Department”) began producing a new annual population data study file to provide more comprehensive statistical information on New York State personal income tax returns. The data are from full‐year resident, nonresident, and part‐year resident returns filed between January 1 and December 31 of the year after the start of the liability period (hereafter referred to as the “processing year”). The four datasets display major income tax components by tax year. This includes the distribution of New York adjusted gross income and tax liability by county or place of residence, as well as the value of deductions, exemptions, taxable income and tax before credits by size of income. In addition, three of the four datasets include all the components of income, the components of deductions, and the addition/subtraction modifications. Caution: The current datasets are based on population data. For tax years prior to 2015, data were based on sample data. Data customers are advised to use caution when drawing conclusions comparing data for tax years prior to 2015 and subsequent tax years. Further details are included in the Overview.

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    Hospital Inpatient Discharges (SPARCS De-Identified): 2014

    health.data.ny.gov | Last Updated 2019-09-13T16:31:56.000Z

    The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed.

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    Residential Existing Homes (One-to-Four Units) Energy Efficiency Projects for Households with Income up to 60% State Median Income: Beginning January 2018

    data.ny.gov | Last Updated 2024-01-22T15:31:03.000Z

    IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income. D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf. This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD). How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

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    Hospital Inpatient Discharges (SPARCS De-Identified): 2015

    health.data.ny.gov | Last Updated 2019-09-13T16:33:50.000Z

    The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed.

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    Patient Characteristics Survey (PCS) 2022: Persons Served by Survey Year, Region of Provider, Gender, Age Group and Race/Ethnicity

    data.ny.gov | Last Updated 2022-09-30T21:48:34.000Z

    The data are organized by OMH Region‐specific (Region of Provider), program type, and by the following demographic characteristics of the clients served during the week of the survey: sex (Male, Female, X (Non-binary), and Unknown), Transgender (No, Not Transgender; Yes, Transgender and Unknown), age (below 17 (Child), 18 and above(Adult) and unknown age) and race (White only, Black Only, Multi‐racial, Other and Unknown race) and ethnicity (Non‐Hispanic, Hispanic, Client Did Not Answer and Unknown). Persons with Hispanic ethnicity are grouped as “Hispanic,” regardless of race or races reported.

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    National Immunization Survey Adult COVID Module (NIS-ACM): COVIDVaxViews| Data | Centers for Disease Control and Prevention (cdc.gov)-Archived

    data.cdc.gov | Last Updated 2024-01-24T15:02:36.000Z

    National Immunization Survey Adult COVID Module (NIS-ACM): CDC is providing information on COVID-19 vaccine confidence to supplement vaccine administration data. These data represent trends in vaccination status and intent, and other behavioral indicators, by demographics and other characteristics.

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    National Immunization Survey Child COVID Module (NIS-CCM): Vaccination Status and Intent by Demographics | Data | Centers for Disease Control and Prevention (cdc.gov)

    data.cdc.gov | Last Updated 2023-08-03T18:27:46.000Z

    National Immunization Survey Child COVID Module (NIS-CCM): CDC is providing information on COVID-19 vaccine confidence to supplement vaccine administration data. These data represent trends in vaccination status and intent, and other behavioral indicators, by demographics and other characteristics.