The Military Bases dataset is as of May 21, 2019, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

Acknowlegement

Original Data

Foto von israel palacio auf Unsplash

This dataset contains demographic information on every member of the US armed forces including gender, race, and rank.

Acknowlegement

Original Data

Foto von israel palacio auf Unsplash

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 dataset is about countries in Central America. It has 8 rows. It features 3 columns: military expenditure, and population.

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 dataset is about countries per year in Northern America. It has 2 rows and is filtered where the date is 2021. It features 4 columns: country, military expenditure, and rural population.

This report presents findings from the 2015 National Survey on Drug Use and Health (NSDUH) with a focus on comparing estimates related to substance use and mental health for military family members (spouses and children) with general population estimates. The numbers of military family members included in the 2015 NSDUH were relatively small. As a consequence, the report focuses on wives aged 18 to 49 and children aged 12 to 17. In the general area of substance use, the report includes estimates for past year use of any illicit substances (marijuana, cocaine in any form including crack, heroin, hallucinogens, inhalants, and methamphetamine and misuse of four categories of prescription drugs -- pain relievers, tranquilizers, stimulants and sedatives), past year cigarette use, past year alcohol use (both any use and binge use), and past year treatment for substance use, including alcohol use. In the general area of mental health, the report includes estimates for any mental illness (AMI) in the past year for wives. For both wives and children, the report includes estimates for past year major depressive episode (MDE) and mental health service use. For children, estimates of mental health service are reported by general treatment setting (e.g., mental health, educational, medical). As additional years of data become available, it will be possible in future reports to include both male and female spouses and to make more detailed comparisons -- for example for more specific types of illicit substances used and for treatment received by setting, by race/ethnicity and for spouses, by employment and educational background.

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.

The high prevalence of dual use of cigarettes and smokeless tobacco is a unique tobacco use behavior in the US military population. However, dual tobacco use has rarely been addressed in active duty populations. We aimed to identify factors contributing to dual tobacco use among active duty service members from Army and Air Force. We also compared age at initiation, duration of use, and amount of use between dual users and exclusive users. The study included 168 exclusive cigarette smokers, 171 exclusive smokeless tobacco users, and 110 dual users. In stepwise logistic regression, smokeless tobacco use among family members (OR = 4.78, 95% CI = 2.05–11.13 for father use vs. no use, OR = 3.39, 95% CI = 1.56–7.37 for other relatives use vs. no use), and deployment history (serving combat unit vs. combat support unit: OR = 4.12, 95% CI = 1.59–10.66; never deployed vs. combat support unit: OR = 3.32, 95% CI = 1.45–7.61) were factors identified to be associated with dual use relative to exclusive cigarette smoking. Cigarette smoking among family members (OR = 1.96, 95% CI = 1.07–3.60 for sibling smoking), high perception of harm using smokeless tobacco (OR = 2.34, 95% CI = 1.29–4.26), secondhand smoke exposure (OR = 4.83, 95% CI = 2.73–8.55), and lower education (associated degree or some college: OR = 2.76, 95% CI = 1.01–7.51; high school of lower: OR = 4.10, 95% CI = 1.45–11.61) were factors associated with dual use relative to exclusive smokeless tobacco use. Compared to exclusive cigarette smokers, dual users started smoking at younger age, smoked cigarettes for longer period, and smoked more cigarettes per day. Our study addressed dual tobacco use behavior in military population and has implications to tobacco control programs in the military.

The Military Bases dataset is as of May 21, 2019, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

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

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.

Free trade has gradually shifted the burden of military service onto the American South. While trade shocks generally lead to local increases in US Army enlistment, there are two different regional dynamics that concentrate this effect in the South. First, trade-related job losses are disproportionately concentrated in this region, where manufacturing jobs grad- ually migrated during the second half of the 20th century. Second, the South’s “military tradition,” a relatively youthful population, and weak labor unions, combine to translate trade shocks into larger spikes in Army enlistment than the rest of the country. This paper uses county-level data from 1996-2010 to demonstrate the importance of meso-level, regional factors for understanding the location of trade shocks, as well as how communities adjust to such economic dislocations. We find that trade-related job losses account for roughly 7 percent of the South’s over-representation in the Army during our period of study.

The U.S. military’s nonpartisan norms are important for healthy civil-military relations. Some research, however, suggests these norms are weakening. This study examines the evidence for eroding nonpartisan norms by analyzing U.S. military servicemembers’ partisan affiliations and political activism levels from 2008 to 2018. It finds that since 2008, military servicemembers have become more likely to identify as partisans. Servicemembers have also become more politically active than civilians, although this is due to decreasing activism among the American public. It also finds that longer-serving service members have stronger nonpartisan norms, but that newer servicemembers are more politically active than both longer-serving servicemembers and civilians. These findings provide a firmer empirical foundation for previous claims of eroding norms and suggest more research is needed to understand how increased partisanship and political activism impact military readiness and civil-military relations.

Munitions and explosives of concern (MEC) have been deposited on the seabed of the United States outer continental shelf since World War I. The bulk of these munitions have originated from the U.S. Armed Forces while conducting military training exercises, war-time placement, and disposal and dumping activities. Since 1972 ocean disposal of munitions and other pollutants has been banned by the Marine Protection, Research, and Sanctuaries Act. Federal and state efforts to mitigate, map, monitor, and sometimes remove these munitions are ongoing. The location of these munitions is generally unknown, and their existence remains a hazard to people and the natural resources within this geography. The term MEC defines a collection of munitions including; a) unexploded ordnance, b) discarded military munitions, and c) munitions constituents that are present in high enough concentrations to pose an explosive hazard. Additional information on the location of MECs can be found in the data and references listed below: Formerly Used Defense Sites Danger Zones and Restricted Areas U.S. Disposal of Chemical Weapons in the Ocean: Background and Issues for Congress, CRS Report for Congress, January 3, 2007 Defense Environmental Programs Annual Report to Congress for Fiscal Year 2009. Chapter 10. Sea Disposal of Military Munitions

Comprehensive demographic dataset for Military Reservation, Buckeye, AZ, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

The Impact of COVID-19 on Veterans in America

County-Level Data on Veteran Cases and Proportion of Population

About this dataset

Welcome to the Kaggle dataset on The Impact of COVID-19 on Veterans in the United States! This dataset contains data on confirmed cases of COVID-19 in counties across the United States, as well as information on the percentage of each county's population that are veterans. With this dataset, you can investigate how the pandemic has impacted veterans specifically, and compare veteran case rates to the general population. How do veteran cases differ across age groups? Are there any geographical patterns? What can we learn about risk factors for COVID-19 among veterans? Download the dataset and explore for yourself today!

How to use the dataset

This dataset includes information on the number of confirmed cases of COVID-19 by county, as well as the percentage of the population in each county that are veterans. This data can be used to examine the relationship between veteran cases and the proportion of population who are veterans.

To do this, simply look at the 'CASES' and 'VET_CASES' columns for each county. The 'CASES' column represents the total number of confirmed cases of COVID-19 in that county, while the 'VET_CASES' column represents the number of confirmed cases among veterans. To compare these two values, simply divide 'VET_CASES' by 'CASES'. This will give you a ratio of veteran cases to total cases for each county.

You can then use this ratio to compare counties and see which ones have a higher proportion of veteran cases. This data can be used to help understand where more outreach may be needed to support veterans during this pandemic

Research Ideas

• Find the correlation between the number of veterans in a county and the number of confirmed cases of COVID-19.
• Find the counties with the highest percentage of veterans and the lowest number of confirmed cases of COVID-19.
• Predict how many veterans in a county will contract COVID-19 based on the percentage of veterans in the population

Columns

File: CountyVACOVID.csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------------------------------------------------------------| | FIPS | Federal Information Processing Standards code that uniquely identifies counties within the USA. (String) | | COUNTY | County name. (String) | | STATE | State name. (String) | | POP | County population. (Integer) | | VETS | Number of veterans in the county. (Integer) | | VET_PERCENT | Percentage of the population that are veterans. (Float) | | CASES | Number of confirmed cases of COVID-19 in the county. (Integer) | | YESTER_CASES | Number of confirmed cases of COVID-19 in the county from the previous day. (Integer) | | VET_CASES | Number of confirmed cases of COVID-19 in veterans in the county. (Integer) | | VET_YESTER | Number of confirmed cases of COVID-19 in veterans in the county from the previous day. (Integer) | | LOWER_Hospitalizations | Lower bound of the 95% confidence interval for the number of hospitalizations due to COVID-19 in the county. (Integer) | | UPPER_Hospitalizations | Upper bound of the 95% confidence interval for the number of hospitalizations due to COVID-19 in the county. (Integer) | | DATE | Date of data. (Date) |

File: VAChart.csv | Column name | Description | |:------------------------|:----------------------------------------------------------------------------------| | DATE | Date of data. (Date) | | US Cases | The number of confirmed cases of COVID-19 in the United States. (Integer) | | **New US ...

Capital-Expenditures Time Series for Science Applications International Corporation Common Stock. Science Applications International Corporation provides technical, engineering, and enterprise information technology (IT) services in the United States. It operates in two segments, Defense and Intelligence, and Civilian. The company offers IT modernization services for defense, intelligence, and civilian agencies; digital engineering services; artificial intelligence solutions; weapon systems support for the U.S. military; training and simulation; and ground vehicles support services for the nation's armed forces. It serves military forces, including the army, air force, navy, marines, coast guard, and space force; agencies of the Department of Defense, National Aeronautics and Space Administration, U.S. Department of State, Department of Justice, and Department of Homeland Security; and members of the Intelligence Community, as well as civilian markets, such as federal, state, and local governments. The company was formerly known as SAIC Gemini, Inc. and changed its name to Science Applications International Corporation in September 2013. Science Applications International Corporation was founded in 1969 and is headquartered in Reston, Virginia.

ANSUR 2 (2012)

A public dataset drawn from the 2012 U.S. Army Anthropometric Survey. This sample is improved in all respects from the ANSUR 88 study and should be used in place of ANSUR 88. Note that this military population is not likely to be representative of any particular user population, but remains valuable because of the ability to explore interrelationships among the variables.

References:

1. http://mreed.umtri.umich.edu/mreed/downloads.html

KAV 8711 cover memo. Visit https://dataone.org/datasets/sha256%3Ac7ba852f5dce20698238c242df5dc8e596d394ee21ee01abc8efda6ccddaeb92 for complete metadata about this dataset.