The population rate of change of Box Elder County, UT was 1.57% 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 Box Elder County, UT

  • API

    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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    2010 Census/ACS Detailed Block Group Data

    data.kcmo.org | Last Updated 2021-11-12T14:22:17.000Z

    detailed characteristics of people and housing for individual 2010 census block groups

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    2013-2017 American Community Survey Detailed Census Tract Data

    data.kcmo.org | Last Updated 2023-03-24T19:40:40.000Z

    DETAILED CHARACTERISTICS OF PEOPLE AND HOUSING FOR INDIVIDUAL 2010 CENSUS TRACT PORTIONS INSIDE OR OUTSIDE KCMO - Some demographic data are from the 2010 Census while other data are from the 2013-2017 American Community Survey (ACS). The ACS replaces what until 2000 was the Long Form of the census; both have been based on surveys of a partial sample of people. The ACS sample is so small that surveys from five years must be combined to be reliable. The 2013-2017 ACS is the most recent grouping of 5 years of data. ACS data have been proportioned to conform with 2010 Census total population and total households.

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    2010 Census/ACS Basic Block Group Data

    data.kcmo.org | Last Updated 2021-11-12T14:15:42.000Z

    basic characteristics of people and housing for individual 2010 census block groups

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    Uninsured Population Census Data CY 2009-2014 Human Services

    data.pa.gov | Last Updated 2022-10-18T14:19:11.000Z

    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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    2015-2019 American Community Survey Detailed Census Tract Data

    data.kcmo.org | Last Updated 2023-03-24T21:11:36.000Z

    DETAILED CHARACTERISTICS OF PEOPLE AND HOUSING FOR INDIVIDUAL 2010 CENSUS TRACT PORTIONS INSIDE OR OUTSIDE KCMO - Some demographic data are from the 2010 Census while other data are from the 2015-2019 American Community Survey (ACS). The ACS replaces what until 2000 was the Long Form of the census; both have been based on surveys of a partial sample of people. The ACS sample is so small that surveys from five years must be combined to be reliable. The 2015-2019 ACS is the most recent grouping of 5 years of data. ACS data have been proportioned to conform with 2010 Census total population and total households.

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    Federal Funds By Agency To Counties Utah 2010

    opendata.utah.gov | Last Updated 2019-04-19T07:06:36.000Z

    The data are from the Census Bureau's Consolidated Federal Funds Reports on Federal expenditures and obligations for grants, salaries and wages, procurements, direct payments, direct loans, guaranteed loans, and insurance obtained from Federal Government agencies. Each file contains Federal outlays by program for each county in the State. County population is also included so that per capita spending can be calculated. Summary of the data for the United States and a ZIP file for all States are also available.

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    Federal Funds By Agency To Counties Utah 2007

    opendata.utah.gov | Last Updated 2019-04-19T02:39:59.000Z

    The data are from the Census Bureau's Consolidated Federal Funds Reports on Federal expenditures and obligations for grants, salaries and wages, procurements, direct payments, direct loans, guaranteed loans, and insurance obtained from Federal Government agencies. Each file contains Federal outlays by program for each county in the State. County population is also included so that per capita spending can be calculated. Summary of the data for the United States and a ZIP file for all States are also available.

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    Public Housing

    data.bayareametro.gov | Last Updated 2021-12-10T20:13:08.000Z

    The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States. According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. HCD downloaded the HUD data

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

    data.cdc.gov | Last Updated 2024-07-18T21:15:31.000Z

    • National Immunization Survey Adult COVID Module (NIS-ACM): CDC is providing information on the Updated 2023-24 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. • The data start in October 2023. • The archived data can be found here: