The number of employees of Fairbanks North Star Borough, AK was 1,433 for business and finance in 2016.

Occupations

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

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Jobs and Occupations Datasets Involving Fairbanks North Star Borough, AK

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    Virginia Beach Demographics

    data.vbgov.com | Last Updated 2017-10-12T13:51:45.000Z

    This dataset provides demographic information from the American Community Survey about residents of Virginia Beach. This data was originally provided in the executive summary of the City of Virginia Beach’s Operating Budget.

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    Tourism Nova Scotia Visitation

    data.novascotia.ca | Last Updated 2018-09-05T13:21:56.000Z

    Number of non resident overnight visitors to Nova Scotia. The dataset is broken down by visitor origin and mode of entry to the province. Data is reported monthly.

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    Median Household Income All States 2000-2012

    opendata.utah.gov | Last Updated 2014-10-31T18:29:13.000Z

    Median Household Income All States 2000-2012

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    Number Of People In Poverty All States 2000-2012

    opendata.utah.gov | Last Updated 2014-10-31T18:32:57.000Z

    Number Of People In Poverty All States 2000-2012

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    FIPS Codes for PA Counties

    data.pa.gov | Last Updated 2017-07-19T15:40:20.000Z

    This is a listing of Federal Information Processing Standard (FIPS) codes for each of the 67 counties in Pennsylvania. Information gathered from census data - https://www.census.gov/geo/reference/codes/cou.html For more technical details : Federal Information Processing Standards Publications (FIPS PUBS) are issued by the National Institute of Standards and Technology (NIST) after approval by the Secretary of Commerce pursuant to Section 111 (d) of the Federal Property and Administrative Services Act of 1949 as amended by the Computer Security Act of 1987, Public Law 100-235. Federal Information Processing Standard (FIPS) 6-4, Counties and Equivalent Entities of the U.S., Its Possessions, and Associated Areas -- 90 Aug 31 , provides the names and codes that represent the counties and other entities treated as equivalent legal and/or statistical subdivisions of the 50 States, the District of Columbia, and the possessions and freely associated areas of the United States. Counties are considered to be the "first-order subdivisions" of each State and statistically equivalent entity, regardless of their local designations (county, parish, borough, etc.).

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    Uninsured Population Census Data CY 2009-2014 Human Services

    data.pa.gov | Last Updated 2018-07-25T18:50:47.000Z

    This data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties. For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64 •3 sex categories: both sexes, male, and female •6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold •4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race). In addition, estimates for age category 0-18 by the income categories listed above are published. Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured. This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges. We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response. The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010 Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.

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    Medicare Physician and Other Supplier National Provider Identifier (NPI) Aggregate Report, Calendar Year 2014

    data.cms.gov | Last Updated 2017-10-31T15:46:00.000Z

    The “Medicare Physician and Other Supplier National Provider Identifier (NPI) Aggregate Report”, a supplement to the Medicare Provider Utilization and Payment Data: Physician and Other Supplier data, contains information on utilization, payments (Medicare allowed amount, Medicare payment, standardized Medicare payment), and submitted charges organized by NPI. Sub-totals for medical type services and drug type services are included as well as overall utilization, payment and charges. In addition, beneficiary demographic and health characteristics are provided which include age, sex, race, Medicare and Medicaid entitlement, chronic conditions and risk scores.

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    Medicare Physician and Other Supplier National Provider Identifier (NPI) Aggregate Report, Calendar Year 2015

    data.cms.gov | Last Updated 2017-10-31T15:44:49.000Z

    The “Medicare Physician and Other Supplier National Provider Identifier (NPI) Aggregate Report”, a supplement to the Medicare Provider Utilization and Payment Data: Physician and Other Supplier data, contains information on utilization, payments (Medicare allowed amount, Medicare payment, standardized Medicare payment), and submitted charges organized by NPI. Sub-totals for medical type services and drug type services are included as well as overall utilization, payment and charges. In addition, beneficiary demographic and health characteristics are provided which include age, sex, race, Medicare and Medicaid entitlement, chronic conditions and risk scores.

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    Medicare Physician and Other Supplier National Provider Identifier (NPI) Aggregate Report, Calendar Year 2016

    data.cms.gov | Last Updated 2018-06-14T16:19:22.000Z

    The “Medicare Physician and Other Supplier National Provider Identifier (NPI) Aggregate Report”, a supplement to the Medicare Provider Utilization and Payment Data: Physician and Other Supplier data, contains information on utilization, payments (Medicare allowed amount, Medicare payment, standardized Medicare payment), and submitted charges organized by NPI. Sub-totals for medical type services and drug type services are included as well as overall utilization, payment and charges. In addition, beneficiary demographic and health characteristics are provided which include age, sex, race, Medicare and Medicaid entitlement, chronic conditions and risk scores.

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    Hate Crimes by County and Bias Type: Beginning 2010

    data.ny.gov | Last Updated 2018-08-07T23:35:10.000Z

    Under New York State’s Hate Crime Law (Penal Law Article 485), a person commits a hate crime when one of a specified set of offenses is committed targeting a victim because of a perception or belief about their race, color, national origin, ancestry, gender, religion, religious practice, age, disability, or sexual orientation, or when such an act is committed as a result of that type of perception or belief. These types of crimes can target an individual, a group of individuals, or public or private property. DCJS submits hate crime incident data to the FBI’s Uniform Crime Reporting (UCR) Program. Information collected includes number of victims, number of offenders, type of bias motivation, and type of victim.