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The population count of Quantico Base, VA was 5,213 in 2018.
Population
Population Change
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Demographics and Population Datasets Involving Quantico Base, VA
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Virginia Employment Status of the Population by Sex by Race and by Age by Year
data.virginia.gov | Last Updated 2022-12-09T15:15:31.000Z2004 to 2021 Virginia Employment Status of the Civilian Non-Institutional Population by Sex, by Race, Hispanic or Latino ethnicity, and detailed by Age, by Year. Annual averages, numbers in thousands. U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, Expanded State Employment Status Demographic Data Data accessed from the Bureau of Labor Statistics website (https://www.bls.gov/lau/ex14tables.htm) Statewide data on the demographic and economic characteristics of the labor force are published on an annual-average basis from the Current Population Survey (CPS), the sample survey of households used to calculate the U.S. unemployment rate (https://www.bls.gov/cps/home.htm). For each state and the District of Columbia, employment status data are tabulated for 67 sex, race, Hispanic or Latino ethnicity, marital status, and detailed age categories and evaluated against a minimum base, calculated to reflect an expected maximum coefficient of variation (CV) of 50 percent, to determine reliability for publication. The CPS sample was redesigned in 2014–15 to reflect the distribution of the population as of the 2010 Census. At the same time, BLS developed improved techniques for calculating minimum bases. These changes resulted in generally higher minimum bases of unemployment, leading to the publication of fewer state-demographic groups beginning in 2015. The most notable impact was on the detailed age categories, particularly the teenage and age 65 and older groups. In an effort to extend coverage, BLS introduced a version of the expanded state employment status demographic table with intermediate age categories, collapsing the seven categories historically included down to three. Ages 16–19 and 20–24 were combined into a 16–24 year-old category, ages 25–34, 35–44, and 45–54 were combined into a 25–54 year-old category, and ages 55–64 and 65 and older were combined into a 55-years-and-older category. These intermediate age data are tabulated for the total population, as well as the four race and ethnicity groups, and then are evaluated against the unemployment minimum bases. The more detailed age categories continue to be available in the main version of the expanded table, where the minimum base was met. Additional information on the uses and limitations of statewide data from the CPS can be found in the document Notes on Using Current Population Survey (https://www.bls.gov/lau/notescps.htm) Subnational Data and in Appendix B of the bulletin Geographic Profile of Employment and Unemployment (https://www.bls.gov/opub/geographic-profile/home.htm).
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Health Opportunity Index
data.virginia.gov | Last Updated 2021-10-07T19:00:36.000ZThe Virginia Health Opportunity Index (HOI) is a group of indicators that provide broad insight into the overall opportunity Virginians have to live long and healthy lives based on the Social Determinants of Health. It is a hierarchical index that allows users to examine social determinants of health at multiple levels of detail in Virginia. It is made up of over 30 variables, combined into 13 indicators, grouped into four profiles, which are aggregated into a single Health Opportunity Index. For more information visualizations visit: https://apps.vdh.virginia.gov/omhhe/hoi/
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Social Vulnerability Index for Virginia by Census Tract, 2018
data.virginia.gov | Last Updated 2023-05-22T14:49:26.000Z"ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event. SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking." For more see https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.html
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VDH-COVID-19-PublicUseDataset-WW-Viral-Load
data.virginia.gov | Last Updated 2023-08-24T19:46:43.000ZAs of 5/18/2023 this dataset will be updated weekly on Tuesdays with a weekly granularity. This dataset includes the VA health planning region, sewershed (i.e., wastewater treatment facility service area), sample collection date, start of sample collection week (Sunday-Saturday), sample type, sample matrix, detection, population served by sewershed, daily flow associated with collected sample, viral concentration in sample, calculated viral load in sample, and report date. This dataset was first published on 5/18/2023. The data set increases in size weekly and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon. When you download the data set, the sewersheds will be sorted in ascending alphabetical order by health region. The sample collection dates will be sorted in ascending order, meaning that the earliest date will be at the top. The most recent date will be at the bottom of each sewershed’s data.
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Veteran Population Projection Model 2016 (VetPop2016)
data.michigan.gov | Last Updated 2019-12-06T19:24:54.000Z2015-2045. Veteran population projections by county for US counties. From va.gov: "The Veteran Population Projection Model 2016 (VetPop2016) provides the latest official Veteran population projection from the Department of Veterans Affairs (VA). VetPop2016 is a deterministic actuarial projection model developed by the office of Predictive Analytics and Actuary (PAA) to estimate and project the Veteran Population from Fiscal Year (FY) 2015 to FY2045. Using the best available Veteran data at the end of FY2015 as the base population. VetPop2016 projects living and deceased Veteran counts by key demographic characteristics such as age, gender, period of service, and race/ethnicity at various geographic levels for the next 30 years." ***NOTE: Current upload to data.mi excludes location information for Puerto Rico, American Samoa, Guam, Northern Mariana Islands, Virgin Islands, and Foreign Countries projections. This is because of a geocoding error between the VetPop2016 and the county location file from the US Census Bureau. Point locations for the above mentioned geographies will be added to this dataset once the error is resolved.
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Indicators of Anxiety or Depression Based on Reported Frequency of Symptoms During Last 7 Days
data.cdc.gov | Last Updated 2023-08-16T15:38:06.000ZThe U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,
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COVID-19 Vaccinations by Town - ARCHIVE
data.ct.gov | Last Updated 2023-08-02T14:53:12.000ZNOTE: As of 4/15/2021, this dataset will no longer be updated and will be replaced by two new datasets: 1) "COVID-19 Vaccinations by Town" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town/x7by-h8k4) and "COVID-19 Vaccinations by Town and Age Group" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town-and-Age-Group/gngw-ukpw). A summary of COVID-19 vaccination coverage in Connecticut by town. Records without an address could not be included in town vaccine coverage estimates. Total population estimates are based on 2019 data. A person who has received one dose of any vaccine is considered to have received at least one dose. A person is considered fully vaccinated if they have received 2 doses of the Pfizer or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. The number with At Least One Dose and the number Fully Vaccinated add up to more than the total number of doses because people who received the Johnson & Johnson vaccine fit into both categories. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. Towns with a "yes" in the "Has SVI tract >0.75" field are those that have at least one census tract that is in the top quartile of vulnerability (e.g., a high-need area). 34 towns in Connecticut have at least one census tract in the top quartile for vulnerability. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.
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Virginia Median Household Income in the Past 12 Months by Census County (ACS 5-Year)
data.virginia.gov | Last Updated 2022-12-08T21:43:16.000Z2013-2021 Virginia Median Household Income based on the past 12 months by Census County or County equivalent. Contains estimates and margins of error. U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B19013 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html) The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html) Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html) Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/) Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties. 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 roughly 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 ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables. Annotation values are character representations of estimates and have values when non-integer information needs to be represented. Below are a few examples. Complete information is available on the ACS website under Notes on ACS Estimate and Annotation Values. (https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-estimate-and-annotation-values.html) A value of -666,666,666 in the estimate column indicates that 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. A value of -222,222,222 in 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.
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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 2019-11-15T22:30:02.000ZHow 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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Rate of Hospitalizations for Opioid Overdose per 100,000 Residents by Demographics CY 2016- 2017 Statewide Health Care Cost Containment Council (PHC4)
data.pa.gov | Last Updated 2022-10-17T20:22:39.000ZRate of hospitalization for opioid overdose per 100,000 PA Residents categorized by principal diagnosis of heroin or opioid pain medication overdose by year and demographic. This analysis is restricted to Pennsylvania residents age 15 and older who were hospitalized in Pennsylvania general acute care hospitals. Disclaimer: PHC4’s database contains statewide hospital discharge data submitted to PHC4 by Pennsylvania hospitals. Every reasonable effort has been made to ensure the accuracy of the information obtained from the Uniform Claims and Billing Form (UB-82/92/04) data elements. Computer collection edits and validation edits provide opportunity to correct specific errors that may have occurred prior to, during or after submission of data. The ultimate responsibility for data accuracy lies with individual providers. PHC4 agents and staff make no representation, guarantee, or warranty, expressed or implied that the data received from the hospitals are error-free, or that the use of this data will prevent differences of opinion or disputes with those who use published reports or purchased data. PHC4 will bear no responsibility or liability for the results or consequences of its use.