In 2024, about 1.4 million veterans were living in Texas - the most out of any state. Florida, California, North Carolina, and Georgia rounded out the top five states with the highest veteran population in that year.

In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

This layer shows veteran status of adults (18+) broken down by age and sex. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of adults who are veterans. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B21001Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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 ...

VetPop2023 top 10 states where Veterans reside in fiscal year 2024

In 2024, there were around 13.96 million male veterans living in the United States. In that year, there were significantly more male veterans than female veterans living in the country.

The Office of Data Governance and Analysis (DGA) creates statistical data for various Veteran related projects. This table displays the count and percent, by county, of Veterans who are farmers and/or dairymen comparative for the entire state's population of Veteran farmers or dairymen in California for 2015. The data was created from our administrative database U.S. Veterans Eligibility Trends and Statistics (USVETS), for the recent event Apps for Ag Hackathon. The U.S. Veterans Eligibility Trends and Statistics (USVETS) is the single integrated dataset of Veteran demographic and socioeconomic data. It provides the most comprehensive picture of the Veteran population possible to support statistical, trend and longitudinal analysis. USVETS has both a static dataset, represents a single authoritative record of all living and deceased Veterans, and fiscal year datasets, represents a snapshot of a Veteran for each fiscal year. USVETS consists mainly of data sources from the Veterans Benefit Administration, the Veterans Health Administration, the Department of Defense’s Defense Manpower Data Center, and other data sources including commercial data sources. This dataset contains information about individual Veterans including demographics, details of military service, VA benefit usage, and more. The dataset contains one record per Veteran. It includes all living and deceased Veterans. USVETS data includes Veterans residing in states, US territories and foreign countries. VA uses this database to conduct statistical analytics, predictive modeling, and other data reporting. USVETS includes the software, hardware, and the associated processes that produce various VA work products and related files for Veteran analytics.

This is the state summary of veterans' statistics for Kansas for fiscal year 2023. There are tables for facilities and healthcare and benefits provided by the VA. The demographics data include age, period of service, sex, and race, as well as projected changes in veteran demographics. Some data, such as information on median personal income and educational attainment, are excluded from the data set "to meet stakeholder demand and establish a more timely delivery of State Summaries."National Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdataSources: VA Veteran Population Projection Model and VA Annual Benefits Report

In 2023, there were almost *********** Black or African American veterans in the United States, representing around ** percent of the total veteran population.

Veterans Health Administration (VHA) offers Annual Evaluations (AEs) to Veterans with spinal cord injuries and disorders (SCI/D) for preventive services, managing common complications, psychosocial services, and addressing equipment needs. Obtain Veteran perceptions of AE services and examine their association with Veteran characteristics and receipt of AEs. Surveys were sent to n = 8,421 Veterans in the VHA SCI/D Registry electronically (n = 8,121) or through US mail (n = 300), with a 23.64% response rate. After excluding participants with missing data, n = 1,687 Veterans were included in descriptive and logistic regression analyses. Respondents were aged 60.7 years (SD=11.60) on average; most were male (91.2%), white (75.9%), and non-Hispanic (90.5%). 72.4% indicated receiving their most recent SCI/D AE about one year ago, 24.8% more than one year ago, and 3.4% had never had an AE. The most frequently reported reasons for not receiving an AE were COVID-19 related concerns (46.1%), and that AE was not offered (23.4%). Most (73.7%) indicated the AE was “Very Important” for their health and well-being. Veterans living more than 120 min from their VA had lower odds (0.51, 95% CI 0.32–0.80) of reporting recent AEs compared to Veterans who lived within 30 min. Veterans expressed high levels of satisfaction with the AE and found many aspects to be valuable. Additional strategies are needed to address travel barriers and COVID-19 related access challenges to bolster AE receipt. Future research and efforts to leverage technology for reminders may improve AE uptake.

This table provides state-level estimates of the percentage of Veterans who were VA pension recipients at the end of the fiscal years 2019, 2020, 2021, and 2023. Percents are rounded to the nearest tenth. Percents for fiscal year (FY) 2022 are not available by state.

Prepared by the National Center for Veterans Analysis and Statistics.

Sources: Department of Veterans Affairs, Office of Enterprise Integration, Veteran Population Projection Model (VetPop) 2020, Veteran Object FY 2023 data, United States Veterans Eligibility Trends & Statistics (USVETS) 2019, 2020, and 2021 data; Veterans Benefits Administration, VETSNET FY 2019, FY 2020, FY 2021, and FY 2023 pension data.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Context

Veteran Employment Outcomes (VEO) are new experimental U.S. Census Bureau statistics on labor market outcomes for recently discharged Army veterans. These statistics are tabulated by military specialization, service characteristics, employer industry (if employed), and veteran demographics. They are generated by matching service member information with a national database of jobs, using state-of-the-art confidentiality protection mechanisms to protect the underlying data.

https://lehd.ces.census.gov/data/veo_experimental.html

Content

"The VEO are made possible through data sharing partnerships between the U.S. Army, State Labor Market Information offices, and the U.S. Census Bureau. VEO data are currently available at the state and national level."

"Veteran Employment Outcomes (VEO) are experimental tabulations developed by the Longitudinal Employer-Household Dynamics (LEHD) program in collaboration with the U.S. Army and state agencies. VEO data provides earnings and employment outcomes for Army veterans by rank and military occupation, as well as veteran and employer characteristics. VEO are currently released as a research data product in "experimental" form."

"The source of veteran information in the VEO is administrative record data from the Department of the Army, Office of Economic and Manpower Analysis. This personnel data contains fields on service member characteristics, such as service start and end dates, occupation, pay grade, characteristics at entry (e.g. education and test scores), and demographic characteristics (e.g. sex, race, and ethnicity). Once service member records are transferred to the Census Bureau, personally-identifying information is stripped and veterans are assigned a Protected Identification Key (PIK) that allows for them to be matched with their employment outcomes in Census Bureau jobs data."

Earnings, and Employment Concepts

Earnings "Earnings are total annual earnings for attached workers from all jobs, converted to 2018 dollars using the CPI-U. For the annual earnings tabulations, we impose two labor force attachment restrictions. First, we drop veterans who earn less than the annual equivalent of full-time work at the prevailing federal minimum wage. Additionally, we drop veterans with two or more quarters with no earnings in the reference year. These workers are likely to be either marginally attached to the labor force or employed in non-covered employment."

Employment

"While most VEO tabulations include earnings from all jobs, tabulations by employer characteristics only consider the veteran's main job for that year. Main jobs are defined as the job for which veterans had the highest earnings in the reference year. To attach employer characteristics to that job, we assign industry and geography from the highest earnings quarter with that employer in the year. For multi-establishment firms, we use LEHD unit-to-worker imputations to assign workers to establishments, and then assign industry and geography."

https://lehd.ces.census.gov/data/veo_experimental.html

Acknowledgements

United States Census Bureau

https://lehd.ces.census.gov/data/veo_experimental.html

Photo by Robert Linder on Unsplash

Inspiration

U.S. Veterans.

The Department of Veterans Affairs provides official estimates and projections of the Veteran population using the Veteran Population Projection Model (VetPop). Based on the latest model VetPop2023 and the most recent national survey estimates from the 2023 American Community Survey 1-Year (ACS) data, the projected number of Veterans living in the 50 states, DC and Puerto Rico for fiscal years, 2023 to 2025, are allocated to Urban and Rural areas. As defined by the Census Bureau, Rural encompasses all population, housing, and territory not included within an Urban area (https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html). This table contains the Veteran estimates by urban/rural, sex and age group. Note: rounding to the nearest 1,000 is always appropriate for VetPop estimates.

https://i.imgur.com/Vrs6apv.png" alt="">

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?

VBA EDUCATION BENEFITS PROGRAM to help servicepersons adjust to civilian life after separation from military service, assist in the recruitment and retention of highly qualified personnel in the active and reserve components in the Armed Forces by providing education benefits, and to provide educational opportunities to the dependents of certain service members and veterans. Individuals who entered active duty after September 10, 2001 may be eligible for the Post-9/11 GI Bill. Individuals can use the Post-9/11 GI Bill after serving 90 days on active duty (excluding entry level and skill training). Only periods of active duty under title 10 will be used to establish eligibility for the Post 9/11 GI Bill. A high school diploma or equivalency certificate is always required for eligibility. Individuals who are eligible for the Montgomery GI Bill – Active Duty (chapter 30), the Montgomery GI Bill – Selected Reserve (chapter 1606), or the Reserve Educational Assistance Program (REAP) will have to make an irrevocable election to relinquish eligibility under one of those benefit programs to establish eligibility under the Post-9/11 GI Bill. The dependent children of a person who died in the line of duty while serving as a member of the Armed Forces may be eligible to use benefits under the Fry Scholarship provision of the Post-9/11 GI Bill. The spouse and/or child(ren) of a veteran or service member may be eligible for the Post 9/11 GI Bill if the veteran or service member transfers entitlement to those dependents. Eligibility to transfer entitlement to dependents is determined by the Department of Defense. This is not a complete list of eligibility requirements. For more information on the latest changes to the Post-9/11 GI Bill go to the VA web-site.

FY 10 Education Recipients by State. The data represents the number of individuals by VA education program who have used their VA education benefit during fiscal year 2010. State statistics may include individuals who used their education benefits in more than one state, therefore the total within this table may be different than the total number of beneficiaries during the fiscal year.

This statistic shows the unemployment rate of veterans in the United States in 2024, by state. In that year, Kansas had the highest unemployment rate of veterans, at 7.2 percent. New Hampshire had the lowest unemployment rate of veterans at 0.2 percent.

My HealtheVet (www.myhealth.va.gov) is a Personal Health Record portal designed to improve the delivery of health care services to Veterans, to promote health and wellness, and to engage Veterans as more active participants in their health care. The My HealtheVet portal enables Veterans to create and maintain a web-based PHR that provides access to patient health education information and resources, a comprehensive personal health journal, and electronic services such as online VA prescription refill requests and Secure Messaging. Veterans can visit the My HealtheVet website and self-register to create an account, although registration is not required to view the professionally-sponsored health education resources, including topics of special interest to the Veteran population. Once registered, Veterans can create a customized PHR that is accessible from any computer with Internet access.

In 2014, the San Diego Association of Governments applied for and received funding from the National Institute of Justice (NIJ) to conduct a process and impact evaluation of the Veterans Moving Forward (VMF) program that was created by the San Diego County Sheriff's Department in partnership with the San Diego Veterans Administration (VA) in 2013. VMF is a veteran-only housing unit for male inmates who have served in the U.S. military. When the grant was written, experts in the field had noted that the population of veterans returning to the U.S. with numerous mental health issues, including post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and depression, were increasing and as a result, the number of veterans incarcerated in jails and prisons was also expected to increase. While numerous specialized courts for veterans had been implemented across the country at the time, veteran-specific housing units for those already sentenced to serve time in custody were rarer and no evaluations of these units had been published. Since this evaluation grant was awarded, the number of veteran-only housing units has increased, demonstrating the need for more evaluation information regarding lessons learned. A core goal when creating VMF was to structure an environment for veterans to draw upon the positive aspects of their shared military culture, create a safe place for healing and rehabilitation, and foster positive peer connections. There are several components that separate VMF from traditional housing with the general population that relate to the overall environment, the rehabilitative focus, and initiation of reentry planning as early as possible. These components include the selection of correctional staff with military backgrounds and an emphasis on building on their shared experience and connecting through it; a less restrictive and more welcoming environment that includes murals on the walls and open doors; no segregation of inmates by race/ethnicity; incentives including extended dayroom time and use of a microwave and coffee machine (under supervision); mandatory rehabilitative programming that focuses on criminogenic and other underlying risks and needs or that are quality of life focused, such as yoga, meditation, and art; a VMF Counselor who is located in the unit to provide one-on-one services to clients, as well as provide overall program management on a day-to-day basis; the regular availability of VA staff in the unit, including linkages to staff knowledgeable about benefits and other resources available upon reentry; and the guidance and assistance of a multi-disciplinary team (MDT) to support reentry transition for individuals needing additional assistance. The general criteria for housing in this veteran module includes: (1) not being at a classification level above a four, which requires a maximum level of custody; (2) not having less than 30 days to serve in custody; (3) no state or federal prison holds and/or prison commitments; (4) no fugitive holds; (5) no prior admittance to the psychiatric security unit or a current psychiatric hold; (6) not currently a Post-Release Community Supervision Offender serving a term of flash incarceration; (7) not in custody for a sex-related crime or requirement to register per Penal Code 290; (8) no specialized housing requirements including protective custody, administration segregation, or medical segregation; and (9) no known significant disciplinary incidents.

In 2023, 42.14 percent of U.S. men aged 75 years and over were veterans - the highest share of any age group or gender. In comparison, less than one percent of women aged 75 and over were veterans at that time.