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 ...

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.

About this dataset

Context

The dataset tabulates the Veteran town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Veteran town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Veteran town was 3,235, a 0.61% decrease year-by-year from 2022. Previously, in 2022, Veteran town population was 3,255, a decline of 1.30% compared to a population of 3,298 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Veteran town decreased by 41. In this period, the peak population was 3,352 in the year 2011. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Veteran town is shown in this column.
• Year on Year Change: This column displays the change in Veteran town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Veteran town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Veteran town. The dataset can be utilized to understand the population distribution of Veteran town by age. For example, using this dataset, we can identify the largest age group in Veteran town.

Key observations

The largest age group in Veteran, New York was for the group of age Under 5 years years with a population of 405 (12.19%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Veteran, New York was the 20 to 24 years years with a population of 71 (2.14%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Veteran town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Veteran town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Age. You can refer the same here

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 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.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Veteran town by race. It includes the distribution of the Non-Hispanic population of Veteran town across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Veteran town across relevant racial categories.

Key observations

Of the Non-Hispanic population in Veteran town, the largest racial group is White alone with a population of 3,203 (97.86% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Veteran town
• Population: The population of the racial category (for Non-Hispanic) in the Veteran town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Veteran town total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Race & Ethnicity. You can refer the same here

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

About this dataset

Context

The dataset tabulates the population of Veteran town by race. It includes the population of Veteran town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Veteran town across relevant racial categories.

Key observations

The percent distribution of Veteran town population by race (across all racial categories recognized by the U.S. Census Bureau): 97.87% are white, 0.09% are Black or African American, 0.18% are American Indian and Alaska Native, 0.30% are Asian and 1.55% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Veteran town
• Population: The population of the racial category (excluding ethnicity) in the Veteran town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Veteran town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Race & Ethnicity. You can refer the same here

This data set consists of one row per federal fiscal year (FY) from FY 2005 - FY 2019, and reports the number and percent of users each of seven VA programs for Veterans who were in service at any time between August 2, 1990, and September 10, 2001, the dates of the Pre-9/11 Gulf War era. The denominator of percent is the number of living Veterans in the FY. The number and percent of users is cumulative since FY 2005. Thus, for example FY 2006 data includes all Veterans who served in the era, were alive at some time during FY 2006 and participated in the program at any time during FY 2005 and FY 2006.

This layer shows Veteran Counts by Sex and Age Group by Census Tract for 2012-2016. This tile layer is best viewed atop a darker basemap such as the Dark Blue Canvas. Click here to view the feature layer that includes margin of error fields and calculated percentages.There are currently over 19.6 million veterans in the United States.Data came from American Community Survey 5-year estimates and were retrieved from the Census Bureau's API on Sept. 27th, 2017 by Diana Lavery.

This quick facts summarizes the Veteran new hires into the Federal government by disabled and by 30 percent and higher disabled groups for 2008 to 2015. It shows the Veteran new hires by agency for 2015 and Veterans by occupation for 2015.

This spotlight uses 2013 National Survey on Drug Use and Health to examine the percentage of U.S. military veterans that a substance use disorder. Results are shown by the eras in which the veterans served, including post-9/11, August 1990 to August 2001, Mary 1975 to July 1990, the Vietnam era, and the pre-Vietnam era.

This spotlight uses 2013 National Survey on Drug Use and Health to examine the percentage of U.S. military veterans that a substance use disorder. Results are shown by the eras in which the veterans served, including post-9/11, August 1990 to August 2001, Mary 1975 to July 1990, the Vietnam era, and the pre-Vietnam era.

This is a simple proportion analysis to determine the number of veterans who may be impacted by food scarcity in the United states by county. The population of veterans in each county (9L_VetPop2016_County) was used with the total population in each county (DataDownload3.18) to determine the proportion of veterans in each county. We assumed that veterans were just as likely as anyone else to be in food scarcity and multiplied the proportion of veterans in each county by the number of low access people in the county to determine the number of food insecure veterans by county. We also used statewide very low food secure percentage as a conservative estimate of the number of veterans affected by food scarcity.This dataset was not created to be a perfect representation of the exact number of food insecure veterans. In fact, it is a very rough calculation. However, this back of the envelope calculation shows that the number of food insecure veterans is likely very high. Using county level food access we find that up to 3 million veterans could be affected by low food access, as a conservative estimate, we use the state level "very low food security percentage" and find that a minimum of 200 thousand veterans are likely food insecure. For calculations see sheet "Calculations" in DataDownload3.18.xlsVeteran Population in counties of the United States.(9L_VetPOP2016_Count.csv)https://va.gov/vetdata/Veteran_Population.aspFood Insecurity By County (DataDownload3.18.xls)https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-and-documentation-downloads/

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18

Other research has shown (1) that civilians and the military differ in their views about when and how to use military force; (2) that the opinions of veterans track more closely with military officers than with civilians who never served in the military; and (3) that U.S. civil–military relations shaped Cold War policy debates. We assess whether this opinion gap “matters” for the actual conduct of American foreign policy. We examine the impact of the presence of veterans in the U.S. political elite on the propensity to initiate and escalate militarized interstate disputes between 1816 and 1992. As the percentage of veterans serving in the executive branch and the legislature increases, the probability that the United States will initiate militarized disputes declines. Once a dispute has been initiated, however, the higher the proportion of veterans, the greater the level of force the United States will use in the dispute.

This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data about Veteran Status, 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 the civilian population over the age of 18 that 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: 2018-2022ACS Table(s): DP02Data downloaded from: CensusBureau's API for American Community Survey Date of API call: January 18, 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. 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:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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 Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

VBA EDUCATION PROGRAMS to provide, through purchase and/or fabrication, prosthetic and related appliances, equipment and services to eligible veterans so that they may live and work as productive citizens. Veterans eligible for prosthetic services are service-connected veterans seeking care for a service-connected disability; veterans with compensable service-connected disabilities generally rated 10 percent or more; former prisoners of war, veterans discharged or released from active military service for a disability that was incurred or aggravated in the line of duty, and veterans who are in receipt of Section 1151 benefits; veterans who are in receipt of increased pension based on a need of regular aid and attendance or by reason of being permanently housebound; veterans who have annual income and net worth below the "means test" threshold; all other veterans who are not required to pay a copayment for their care, i.e., veterans of the Mexican border period and World War I, compensated zero (0) percent service-connected veterans who are receiving statutory awards, veterans exposed to a toxic substance, radiation or environmental hazard (limited to certain disabilities); and veterans who must pay a copayment for their care. Ineligible veterans are nonservice-connected veterans residing or sojourning in foreign lands.

Percent of Veterans that use VA benefits by program and sex in FY 2023