The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Census Bureau includes landmarks such as military installations in the MTDB for locating special features and to help enumerators during field operations. In 2012, the Census Bureau obtained the inventory and boundaries of most military installations from the U.S. Department of Defense (DOD) for Air Force, Army, Marine, and Navy installations and from the U.S. Department of Homeland Security (DHS) for Coast Guard installations. The military installation boundaries in this release represent the updates the Census Bureau made in 2012 in collaboration with DoD.

Content

Lists the military spending, GDP, and population estimate for the US each year from 1960 to 2020.

Acknowledgements

Banner image source: https://unsplash.com/photos/BQgAYwERXhs

This comprehensive report chronicles the history of women in the military and as Veterans, profiles the characteristics of women Veterans in 2009, illustrates how women Veterans in 2009 utilized some of the major benefits and services offered by the Department of Veterans Affairs (VA), and discusses the future of women Veterans in relation to VA. The goal of this report is to gain an understanding of who our women Veterans are, how their military service affects their post-military lives, and how they can be better served based on these insights.

From the project website: "url"> https://sites.tufts.edu/css/mip-research/mip-dataset/

The Military Intervention Project (MIP) within the Center for Strategic Studies (CSS) seeks to solve the puzzle of US foreign military interventions. It is building a new, comprehensive dataset of all US military interventions from 1776 until 2017 to measure the costs, benefits, and unintended consequences of US military involvements abroad. In other words, this dataset will provide strong empirical evidence regarding the trade-offs of US military interventions – a current hot topic in Congress, the media, and in public opinion. MIP will measure the costs and benefits to US national interests, economic growth, international reputation as well as human rights, democratic, and economic outcomes within the target state, and much more.

This information is designed to provide service members, their families, veterans, the general public, and other concerned citizens with the most comprehensive and accurate figures available regarding diagnosed cases of TBI within the U.S. military. Information is collected from electronic medical records and analyzed by the Defense and Veterans Brain Injury Center in cooperation with the Armed Forces Health Surveillance Center. Numbers for the current year will be updated on a quarterly basis. Other data will be updated annually. At this time, the MHS is unable to provide information regarding cause of injury or location because that information is not available in most medical records. The numbers represent actual medical diagnoses of TBI within the U.S. Military. Other, larger numbers routinely reported in the media must be considered inaccurate because they do not reflect actual medical diagnoses. Many of these larger numbers are developed utilizing sources such as the Post Deployment Health Assessment (PDHA) or Post Deployment Health Reassessment (PDHRA). However, these documents are assessment tools with TBI screening questions and are not diagnostic tools.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES VETERAN STATUS - DP02 Universe - Civilian population 18 Year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps.

Analysis of ‘US Military Spending by Year (1960 - 2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brandonconrady/us-military-spending-by-year-1960-2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

Lists the military spending, GDP, and population estimate for the US each year from 1960 to 2020.

Acknowledgements

Banner image source: https://unsplash.com/photos/BQgAYwERXhs

--- Original source retains full ownership of the source dataset ---

The 2012 US Army Anthropometric Survey (ANSUR II) was executed by the Natick Soldier Research, Development and Engineering Center (NSRDEC) from October 2010 to April 2012 and is comprised of personnel representing the total US Army force to include the US Army Active Duty, Reserves, and National Guard. The data was made publicly available in 2017. In addition to the anthropometric and demographic data described below, the ANSUR II database also consists of 3D whole body, foot, and head scans of Soldier participants. These 3D data are not publicly available out of respect for the privacy of ANSUR II participants. The data from this survey are used for a wide range of equipment design, sizing, and tariffing applications within the military and has many potential commercial, industrial, and academic applications.These data have replaced ANSUR I as the most comprehensive publicly accessible dataset on body size and shape. The ANSUR II dataset includes 93 measurements from over 6,000 adult US military personnel, comprising 4,082 men (ANSUR_II_MALE_Public.csv) and 1,986 women (ANSUR_II_FEMALE_Public.csv).

The ANSUR II working databases contain 93 anthropometric measurements which were directly measured, and 15 demographic/administrative variables.

Much more information about the data collection methodology and content of the ANSUR II Working Databases may be found in the following Technical Reports, available from theDefense Technical Information Center (www.dtic.mil) through:

a. 2010-2012 Anthropometric Survey of U.S. Army Personnel: Methods and Summary
Statistics. (NATICK/TR-15/007)
b. Measurer’s Handbook: US Army and Marine Corps Anthropometric Surveys,
2010-2011 (NATICK/TR-11/017)

Access a market-leading database of 18 million verified military veterans, backed by our money-back quality guarantee. Our veteran mailing lists are meticulously updated and verified every month to ensure accuracy. Understanding that every campaign is unique, we provide a comprehensive range of demographic and psychographic filters to help you target the exact veteran audience you need.

Whether you aim to offer benefits, home loans, educational opportunities, or specialized services, our data ensures your message reaches the right audience, enabling you to connect effectively with both active and non-active military members. Discover how our targeted data solutions can enhance your engagement and drive success for your initiatives.

Here are some of the customizable segments you can create with our filters:

• Veteran Ethnicities Available
• Senior Veterans (65+)
• Affluent Veterans
• Veterans with Advanced Degrees
• Veteran's Hobbies
• Disabled Veterans
• Families with two or more veterans in the household

Our military veterans email campaign offers targeted outreach to qualified veteran leads with a guaranteed open rate, ensuring your message reaches a receptive audience. After the campaign, you can opt to receive a list of veterans who opened your email, providing a valuable pool of warm leads for follow-up. If you prefer to manage your own campaign, we also offer highly accurate veteran email lists, complete with unlimited usage rights for ongoing marketing efforts.

Additionally, you can extend your reach by using the same veteran email list for targeted Facebook ads, leveraging the power of multi-channel marketing. For a more tangible approach, our veterans mailing list allows you to engage veterans directly through direct mail, offering an uninterrupted opportunity to capture their attention. To maximize impact, we recommend synchronizing direct mail with a complementary digital ad campaign, enhancing your overall return on investment. With our active military database, you can connect with military personnel both on and off base.

Context

According to The Oregonian hundreds of National Guard armories across the U.S. may have been contaminated with lead from indoor firing ranges. It was reported that areas populated by children under 7 years of age should have less than 40 micrograms of lead per square foot.

Content

The Oregonian collected over 23,000 pages of public records following a Freedom Of Information Act request. The dataset covers armory inspections conducted since 2012 and may facilitate investigation of lead contamination in the U.S.

Acknowledgements

The data assembly process is described by Melissa Lewis here.

Inspiration

This dataset can be used to conduct research in the realm of public health. It will be especially useful if 1) you know about health effects of exposure to lead in relatively short terms periods; 2) you are able to find relevant health data to conduct a study on lead poisoning.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

These deidentified datasets have been approved for public release by the VA Boston Healthcare System's Institutional Review Board and may be used without restriction. Please cite one or more of the source articles when using these data:

Feyman, Y, Auty, SG, Tenso, K, Strombotne, KL, Legler, A, & Griffith, KN. (2022). “County-Level Impact of the COVID-19 Pandemic on Excess Mortality Among U.S. Veterans: A Population-Based Study.” The Lancet Regional Health – Americas 5: 100093.

Tenso, K, Strombotne, KL, Feyman, Y, Auty, SG, Legler, A, & Griffith KN. (in press). “Excess Mortality at Veterans Health Administration Facilities During the COVID-19 Pandemic.” Medical Care.

Avila, CJ, Feyman, Y, Auty, SG, Mulugeta, M, Strombotne, KL, Legler, A, & Griffith, KN. (in progress). “Racial and ethnic disparities in excess mortality due to COVID-19 among U.S. veterans.” Health Services Research.

This is a collection that contains code and dataframes of a study that investigates the well-being trend of Afghanistan population following the U.S. military withdrawal in 2021. The synthetic analytic datasets in this data collection are provided solely for study replication and should not be used for other purposes.

AbstractObjective: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. Methods: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. Results: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. Conclusion: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions. Usage notesPrev of MS in the US-E-Appendix-Feb-19-2018

We investigate the impact of U.S. bombing on later economic development in Vietnam. The Vietnam War featured the most intense bombing campaign in military history and had massive humanitarian costs. We use a unique U.S. military dataset containing bombing intensity at the district level (N = 584) to assess whether the war damage led to persistent local poverty traps. We compare the heavily bombed districts to other districts controlling for district demographic and geographic characteristics, and use an instrumental variable approach exploiting distance to the 17th parallel demilitarized zone. U.S. bombing does not have negative impacts on local poverty rates, consumption levels, infrastructure, literacy or population density through 2002. This finding indicates that even the most intense bombing in human history did not generate local poverty traps in Vietnam.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

This study involved family members of military troops in the Persian Gulf. Questions were asked about bombing of military sites, Israel's involvement, media coverage, anti-war protests, length of the war, use of nuclear weapons, terrorism, volunteer versus draft, public support, President Bush's handling of the war, prominent leaders in the war effort, anti-war demonstrators, and removal of Saddam Hussein from power

The Census Bureau determines that a person is living in poverty when his or her total household income compared with the size and composition of the household is below the poverty threshold. The Census Bureau uses the federal government's official definition of poverty to determine the poverty threshold. Beginning in 2000, individuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin. The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. Race categories include the following - White, Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, Some other race, and Two or more races. We are not including specific combinations of two or more races as the counts of these combinations are small. Ethnic categories include - Hispanic or Latino and White Non-Hispanic. This data comes from the American Community Survey (ACS) 5-Year estimates, table B17001. The ACS collects these data from a sample of households on a rolling monthly basis. ACS aggregates samples into one-, three-, or five-year periods. CTdata.org generally carries the five-year datasets, as they are considered to be the most accurate, especially for geographic areas that are the size of a county or smaller.Poverty status determined is the denominator for the poverty rate. It is the population for which poverty status was determined so when poverty is calculated they exclude institutionalized people, people in military group quarters, people in college dormitories, and unrelated individuals under 15 years of age.Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, Below poverty level are households as determined by the thresholds based on the criteria of looking at household size, number of children, and age of householder.number of children, and age of householder.

NOTE: As of 12/17/2024, this dataset is no longer updated. Please use ASPR Treatments Locator.

This dataset displays pharmacies, clinics, and other locations with safe and effective COVID-19 medications. These medications require a prescription from a healthcare provider. Some locations, known as Test to Treat sites, give you the option to get tested, get assessed by a healthcare provider, and receive treatment – all in one visit. COVID-19 medications may be available at additional locations that are not shown in this dataset.

The locations displayed have either self-attested they have inventory of Paxlovid (nirmatrelvir packaged with ritonavir), Lagevrio (molnupiravir), or Veklury (Remdesivir) within at least the last two months and/or reported participation in the Paxlovid Patient Assistance Program. Sites that have not reported in the last two weeks display a notification, "Inventory has not been reported in the last 2 weeks. Please contact the provider to make sure the product is available." Outpatient COVID-19 medications may be available at additional locations not listed on this website.

All therapeutics identified in the locator not approved by the FDA must be used in alignment with the terms of the respective product’s Emergency Use Authorization. Visit COVID-19 Treatments and Therapeutics for more information on all treatment options.

This website identifies sites that have commercially purchased inventory of COVID-19 treatments and, in some cases, may identify sites that have remaining, no-cost U.S. government distributed supply. Some sites may charge for services not covered by insurance. Some sites may offer telehealth services. This website is intended for informational purposes only and does not serve as an endorsement or recommendation for use of any of the locations listed on the sites.

Clarification for DoD Facilities: Those individuals eligible for care in an MTF include Active Duty Service Members (ADSMs), covered beneficiaries enrolled in TRICARE Prime or Select, including TRICARE Reserve Select (TRS), TRICARE Retired Reserve (TRR) and TRICARE Young Adult (TYA) participants, TRICARE for Life beneficiaries, and individuals otherwise entitled by law to MTF care (e.g., regular retired members and their dependents who are not enrolled in TRICARE but who are otherwise eligible for MTF space-available care, certain foreign military members and their families registered in DEERS, and others).

To evaluate the incidence, refractive error (RE) association, and distribution of atraumatic rhegmatogenous retinal detachment (RRD) in U.S. military service members (SMs). This study used data from the Military Health System (MHS) M2 database to identify active U.S. military and National Guard SMs diagnosed with RRD from 2017 to 2022. The RE in diopters (D) was manually extracted from available medical charts for 518 eyes. The annual incidence rate of RRD was calculated overall and evaluated in terms of age, gender, and RE. A multivariate Poisson regression model was used to estimate the relative risk (RR) for RRD with RE. From 2017 to 2022, 1,537 SMs were diagnosed with RRD and 1,243,189 were diagnosed with RE. One thousand two hundred seventy-five SMs had both diagnoses: RRD and RE. The overall incidence rate of RRD over the 6-year study was 16.3 per 100,000 people (16.4 and 15.9 for males and females, respectively). In all study groups, the incidence of RRD increased with age. SMs with RE had an overall 25-fold increased risk for RRD compared to SMs without RE. RE was present in 83.0% of cases of RRD. Myopia accounted for 93.3% of cases for eyes with detailed refractive data. The incidence of RRD in U.S. SMs is comparable to other studies and is similar among male and female SMs. RE is present in most cases of RRD in SMs, with the most common type being low to moderate amounts of myopia.