The graph illustrates the number of personnel in each branch of the U.S. Military for the year 2025. The x-axis lists the military branches: Army, Navy, Marine Corps, Air Force, and Coast Guard. The y-axis represents the number of personnel, ranging from 41,689 to 452,823. Among the branches, the Army has the highest number of personnel with 452,823, followed by the Navy with 337,209 and the Air Force with 321,211. The Marine Corps and Coast Guard have 170,201 and 41,689 personnel, respectively. The data is displayed in a bar graph format, effectively highlighting the distribution of military personnel across the different branches.

The U.S. Army remains the largest branch of the American military, with 449,344 active duty personnel in 2023. While the Army leads in numbers, the newly established Space Force had just 8,879 active duty members, highlighting the evolving nature of modern warfare and the increasing importance of space-based capabilities. Confidence in military remains high Despite fluctuations in force size, public trust in the U.S. military remains strong. In 2024, 61 percent of Americans expressed a great deal or quite a lot of confidence in the armed forces, a slight increase from the previous year. While a slightly higher share of Republicans have shown more confidence in the military, trust in the institution remains high across party lines. Global commitments The United States continues to invest heavily in its military capabilities, with defense spending reaching 916.02 billion U.S. dollars in 2023. This substantial budget supports not only domestic defense needs but also enables the U.S. to respond to global crises, as evidenced by the over 40 billion euros in military aid provided to Ukraine following Russia's invasion. The high level of spending, which translates to about 2,220 U.S. dollars per capita.

Throughout the 19th century, the share of military personnel employed by the United States government was below 0.2 percent of the total population in most years. There were noticeable spikes in enlistments and conscriptions during the American Civil War (1861-65), the First World War (1917-18*), and Second World War (1941-45*), as well as smaller increases during the Mexican-American War (1946-48) and the Spanish-American War (1898), but figures were generally much lower than the post-WWII era.

Following the Second World War, the United States abandoned many of its isolationist positions as it sought to become the world's leading superpower. This involved stationing millions of troops in overseas bases during the Cold War, in strategically important locations such as West Germany, Japan, and Taiwan. Additionally, involvement in conflicts such as the Korean War (1950-1953) and Vietnam War (1964-1973*) kept military employment high, usually between 1-2 percent until the 1970s. Figures remained just below the one percent mark until the 1990s, when the end of the Cold War and the growing influence of technology in conventional warfare saw a decrease in demand for many traditional combat roles. Despite U.S. involvement in a number of overseas conflicts in the 21st century, military personnel represented less than 0.5 percent of the total population in most years between 2000 and 2016.

In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.

Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.

The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.

The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.

The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.

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

In 2023, there were around 1.11 million active duty U.S. Armed Forces personnel stationed within the United States. In that year, there were 156,418 U.S. Armed Forces personnel stationed in California, the most of any state.

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 ...

In 2023, around 21.3 percent of active duty officers in the United States Navy were women. Additionally, approximately 19.4 percent of officers in the Space Force were women.

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 scatter chart displays military expenditure (% of GDP) against rural population (people) in the United States. The data is filtered where the date is 2023. The data is about countries per year.

Context

This data is all US military divisions

Content

This data is all US military divisions

Acknowledgements

The government

Inspiration

Thanks

This scatter chart displays military expenditure (% of GDP) against female population (people) in the United States. The data is filtered where the date is 2021. The data is about countries per year.

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.

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Context

There is a well-documented phenomenon of increased suicide rates among United States military veterans. One recent analysis, published in 2016, found the suicide rate amongst veterans to be around 20 per day. The widespread nature of the problem has resulted in efforts by and pressure on the United States military services to combat and address mental health issues in and after service in the country's armed forces.

In 2013 News21 published a sequence of reports on the phenomenon, aggregating and using data provided by individual states to typify the nationwide pattern. This dataset is the underlying data used in that report, as collected by the News21 team.

Content

The data consists of six files, one for each year between 2005 and 2011. Each year's worth of data includes the general population of each US state, a count of suicides, a count of state veterans, and a count of veteran suicides.

Acknowledgements

This data was originally published by News21. It has been converted from an XLS to a CSV format for publication on Kaggle. The original data, visualizations, and stories can be found at the source.

Inspiration

What is the geospatial pattern of veterans in the United States? How much more vulnerable is the average veteran to suicide than the average citizen? Is the problem increasing or decreasing over time?

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

In the United States, active duty Armed Forces personnel tended to be young, with the majority under the age of 30 years old. In 2023, there were 546,361 U.S. Defense Armed personnel aged 25 and under. In the age group 26 to 30, there were 275,756 Armed Forces personnel.

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

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.

The Military-Hollywood Collaboration Database

This is a dataset of 865 films and TV shows that have reached out to the United States military for production assistance in the last 100 years, how the military responded to the request, and remarks — written directly by a military representative — as to why. It was collected as part of a data visualization project.

Background

The United States Department of Defense (DoD) and people who make film and TV entertainment have been in active collaboration for over 100 years — since the dawn of American cinema.

How does the relationship work? A production company reaches out to the DoD and requests access to hard-to-get resources: weapons, planes, filming locations, advisors, and more. In exchange, the DoD sets conditions for acceptance — that can even include changing aspects of the film's script — to present the armed forces, its personnel, and the country itself in a better light.

About the Data

The original data was acquired in a 2017 Freedom of Information Act (FOIA) request to the U.S. Pentagon by Tom Secker, a journalist and creator of the Spy Culture website. Key columns from the resulting PDF were then manually transcribed in a CSV file.

The data does not encompass every single film that collaborated with the DoD in the last century — it is limited by what the Pentagon chose to release in the 2017 FOIA request and can contain errors.

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.