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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?

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

This dataset will be used to help users get familiar with Google Data Studio. It's also a great way to mix history with data visualization skills.

Later I will develop a lesson that will help new users get up to speed with Google Data Studio. This dataset is small, but it will be a good dataset to start with. The same concepts learned with this project will be used with larger datasets.

Content

This data comes from wikipedia.

Acknowledgements

wikipedia

https://en.wikipedia.org/wiki/United_States_military_casualties_of_war

Inspiration

Project based learning. Get it going! Teach kids using data.

This dataset is about countries per year in South America. It has 768 rows. It features 4 columns: country, armed forces personnel, and female population.

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.

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

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)

This data consists of three files:lists of all enlisted applicants, contracts, and accessions to the US military from October 2000 to September 2010, as well as a small Excel file that serves as a data dictionary. Individuals are identified only by 3 digit ZIP codes, and do not contain an individual identifier so they cannot be reliably tracked across stages of enlistment. The data was obtained through Freedom of Information Act request 11-F-0024, filed by Garret Christensen in 2010. The only documentation provided with the request is included here, in the Excel file.

This dataset is about countries per year in the United States. It has 64 rows. It features 4 columns: country, armed forces personnel, and urban population.

These data are intended to be the universe of individual enlisted applicants to the US military from 1990-2006, obtained through FOIA request from the Defense Manpower Data Center. Separate files exist for applicants, contracts, and accessions. Individuals are identified only by ZIP code, with no persistent identifier across stage of enlistment, so individuals cannot be perfectly tracked across enlistment stage.

Attitude of the population of the FRG to the military, defense policy and NATO. Topics: Attitude to rearmament of the FRG and a professional or volunteer army; military preparedness; military knowledge; attitude to military drill and obedience; position of the FRG in NATO; attitude and relationship of the Germans to American occupying forces; reasons and evaluation of presence of American soldiers in the FRG; attitude to military service; image of the soldiers of selected countries; social distance from Americans; evaluation of Russian recommendations about reunification; evaluation of the cultural achievements of various peoples; personal participation in the world wars; relative social prestige of selected occupations; membership in a club, trade union or party; honorary activities; party preference. Demography: age (classified); sex; religious denomination; school education; occupation; household income; head of household; state; refugee status. Interviewer rating: social class and willingness of respondent to cooperate; number of contact attempts; city size. Also encoded were: identification of interviewer; sex of interviewer and age of interviewer. Einstellung der Bevölkerung der BRD zum Militär, zur Verteidigungspolitik und zur NATO. Themen: Einstellung zur Wiederbewaffnung der BRD und zu einem Berufs- oder Freiwilligenheer; Wehrbereitschaft; militärische Kenntnisse; Einstellung zu militärischem Drill und Gehorsam; Stellung der BRD in der NATO; Einstellung und Verhältnis der Deutschen zu den amerikanischen Besatzungstruppen; Gründe und Bewertung der Anwesenheit amerikanischer Soldaten in der BRD; Einstellung zum Militärdienst; Image der Soldaten ausgewählter Länder; soziale Distanz zu Amerikanern; Bewertung der russischen Vorschläge über eine Wiedervereinigung; Bewertung der kulturellen Leistungen verschiedener Völker; eigene Teilnahme an den Weltkriegen; relatives Sozialprestige ausgewählter Berufe; Mitgliedschaft in einem Verein, einer Gewerkschaft oder einer Partei; ehrenamtliche Tätigkeiten; Parteipräferenz. Demographie: Alter (klassiert); Geschlecht; Konfession; Schulbildung; Beruf; Haushaltseinkommen; Haushaltungsvorstand; Bundesland; Flüchtlingsstatus. Interviewerrating: Kooperationsbereitschaft und Schichtzugehörigkeit des Befragten; Anzahl der Kontaktversuche; Ortsgröße. Zusätzlich wurden verkodet: Intervieweridentifikation; Interviewergeschlecht und Intervieweralter. Multi-stage random sample Mehrstufige Zufallsauswahl Oral survey with standardized questionnaire

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.

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

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.

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

White-tailed deer (WTD) can exert substantial impacts on the ecosystems in the Southeastern United States. WTD populations in Vicksburg National Military Park (VICK) have not been surveyed since 2010. This study aimed to develop a cost-effective visual count-density conversion method using WTD counts from spotlight surveys and density estimates from fecal-DNA spatially explicit capture-recapture (SECR) models. We conducted spotlight surveys of WTD in January, March, and May, 2023. Average visual counts of WTD were 96.0 in January, March, and May. Direct visual deer counts were greater than those of the 2009 VICK WTD spotlight surveys by 40%-220%. Average WTD relative abundances were 8.5 deer per mile in January, March, and May, respectively. Average density estimated by distance sampling models was 0.65 deer per ha. We used eight microsatellite markers to genotype WTD fecal samples, fecal DNA spatially explicit capture-recapture models estimated 778 WTD within VICK. The WTD densities were positively related to the proportions of forests and open fields in VICK. White-tailed deer appeared to be overabundant within VICK, causing concerns relative to WTD-human conflicts and exacerbating the risk of wildlife disease transmission. We proposed a method for estimating WTD densities with visual counts, which will allow park staff to convert WTD visual counts from spotlight surveys to WTD densities until substantial changes in VICK’s vegetation and/or habitat management occur. The timely, cost-effective monitoring of WTD populations can help park staff better manage natural resources within VICK, including the mitigation of the damages caused by overabundant WTD to natural resources.

This data was collected from the DSCA (Defense Security Cooperation Agency). This data only shows Foreign Military Sales, not Private Arms Sales. Foreign Military Sales only make up around 1/3 of total arms sales from the United States.

I will be updating the data set, going back further and updating data to the present.

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.